diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 803277043..10abe37a2 100644
Binary files a/doc/LectureNotes/_build/.doctrees/environment.pickle and b/doc/LectureNotes/_build/.doctrees/environment.pickle differ
diff --git a/doc/LectureNotes/_build/.doctrees/week34.doctree b/doc/LectureNotes/_build/.doctrees/week34.doctree
index 158bcbbe2..aba08a777 100644
Binary files a/doc/LectureNotes/_build/.doctrees/week34.doctree and b/doc/LectureNotes/_build/.doctrees/week34.doctree differ
diff --git a/doc/LectureNotes/_build/html/_sources/week34.ipynb b/doc/LectureNotes/_build/html/_sources/week34.ipynb
index 95856e1b9..899bd8e9f 100644
--- a/doc/LectureNotes/_build/html/_sources/week34.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week34.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3b47d5e6",
+   "id": "0b9e53e2",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f010738",
+   "id": "d69d6c55",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9821466c",
+   "id": "ed7a2caa",
    "metadata": {
     "editable": true
    },
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d635526f",
+   "id": "1a7fa793",
    "metadata": {
     "editable": true
    },
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46d1ebd2",
+   "id": "42eed58e",
    "metadata": {
     "editable": true
    },
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "722a1812",
+   "id": "19b78895",
    "metadata": {
     "editable": true
    },
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8724a41b",
+   "id": "920c8b78",
    "metadata": {
     "editable": true
    },
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a74065a9",
+   "id": "66791ac8",
    "metadata": {
     "editable": true
    },
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb3519d1",
+   "id": "d006c6f2",
    "metadata": {
     "editable": true
    },
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f46e4ab5",
+   "id": "d2b59d04",
    "metadata": {
     "editable": true
    },
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18134f7b",
+   "id": "2b1dfff8",
    "metadata": {
     "editable": true
    },
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e4b2cb4",
+   "id": "39ce46e9",
    "metadata": {
     "editable": true
    },
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dc04ba4",
+   "id": "cc1139fe",
    "metadata": {
     "editable": true
    },
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9d7446a",
+   "id": "f56c0bb5",
    "metadata": {
     "editable": true
    },
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ac913df",
+   "id": "4b601e8d",
    "metadata": {
     "editable": true
    },
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe4f142d",
+   "id": "2518a99e",
    "metadata": {
     "editable": true
    },
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82a26310",
+   "id": "a0e1e9ae",
    "metadata": {
     "editable": true
    },
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d97a91f",
+   "id": "8af343cb",
    "metadata": {
     "editable": true
    },
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0af85fa1",
+   "id": "c0cfedb4",
    "metadata": {
     "editable": true
    },
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca1c0d4b",
+   "id": "4561f563",
    "metadata": {
     "editable": true
    },
@@ -398,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e3a4194",
+   "id": "b94446df",
    "metadata": {
     "editable": true
    },
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44fe97f3",
+   "id": "2da0b611",
    "metadata": {
     "editable": true
    },
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fd61872",
+   "id": "cb6368d0",
    "metadata": {
     "editable": true
    },
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50148149",
+   "id": "cd5643ee",
    "metadata": {
     "editable": true
    },
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12bc5144",
+   "id": "dad2568b",
    "metadata": {
     "editable": true
    },
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25428d4e",
+   "id": "ccf34c1f",
    "metadata": {
     "editable": true
    },
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2efac50e",
+   "id": "d9a80c4f",
    "metadata": {
     "editable": true
    },
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfd0c6a0",
+   "id": "bf1454b7",
    "metadata": {
     "editable": true
    },
@@ -572,7 +572,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f92f8d35",
+   "id": "4a01a6f0",
    "metadata": {
     "editable": true
    },
@@ -588,7 +588,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "174d6e96",
+   "id": "2bf27180",
    "metadata": {
     "editable": true
    },
@@ -604,12 +604,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fcac42b",
+   "id": "15ddfa02",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Example of discriminative modeling, [taken from Generative Deeep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n",
+    "## Example of discriminative modeling, [taken from Generative Deep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n",
     "\n",
     "<!-- dom:FIGURE: [figures/standarddeeplearning.png, width=900 frac=1.0] -->\n",
     "<!-- begin figure -->\n",
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8942d9a",
+   "id": "371a54c3",
    "metadata": {
     "editable": true
    },
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a28ea0e4",
+   "id": "f9ec1f0a",
    "metadata": {
     "editable": true
    },
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3260f00",
+   "id": "19f2aef4",
    "metadata": {
     "editable": true
    },
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ac16500",
+   "id": "e08bd4e6",
    "metadata": {
     "editable": true
    },
@@ -736,7 +736,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "825ca09e",
+   "id": "729c3f46",
    "metadata": {
     "editable": true
    },
@@ -775,7 +775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40bd0bf2",
+   "id": "6825a222",
    "metadata": {
     "editable": true
    },
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a356fdf0",
+   "id": "bad98b2e",
    "metadata": {
     "editable": true
    },
@@ -845,7 +845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9730b8a",
+   "id": "1daa92e1",
    "metadata": {
     "editable": true
    },
@@ -869,7 +869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f967a6a",
+   "id": "1c840067",
    "metadata": {
     "editable": true
    },
@@ -896,7 +896,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76e167a3",
+   "id": "f33d6379",
    "metadata": {
     "editable": true
    },
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94a3c420",
+   "id": "e3a03646",
    "metadata": {
     "editable": true
    },
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6ff6b6b",
+   "id": "fb6d2495",
    "metadata": {
     "editable": true
    },
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55dbb002",
+   "id": "3101a680",
    "metadata": {
     "editable": true
    },
@@ -951,13 +951,10 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "655878bd",
+   "id": "66625f81",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -966,7 +963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "914fa12c",
+   "id": "d8edb94a",
    "metadata": {
     "editable": true
    },
@@ -977,13 +974,10 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a0cfca17",
+   "id": "7d44772f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -994,7 +988,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "342c0109",
+   "id": "7c3a5d5e",
    "metadata": {
     "editable": true
    },
@@ -1006,13 +1000,10 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d09ad51a",
+   "id": "bd150398",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1023,7 +1014,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be9009bc",
+   "id": "62bb511e",
    "metadata": {
     "editable": true
    },
@@ -1035,13 +1026,10 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "19dfe7ab",
+   "id": "de9d1b88",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1052,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c78e90e",
+   "id": "575f4f67",
    "metadata": {
     "editable": true
    },
@@ -1069,13 +1057,10 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "48603a4c",
+   "id": "eadd4613",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1089,7 +1074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5cfa34",
+   "id": "43c2b238",
    "metadata": {
     "editable": true
    },
@@ -1101,13 +1086,10 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "1c252e6c",
+   "id": "f1153763",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1118,7 +1100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "375afb81",
+   "id": "b897cb85",
    "metadata": {
     "editable": true
    },
@@ -1129,13 +1111,10 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4c87de99",
+   "id": "3417f7b5",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1146,7 +1125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afff17c8",
+   "id": "421de263",
    "metadata": {
     "editable": true
    },
@@ -1157,13 +1136,10 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "d5827065",
+   "id": "e9055b4f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1174,7 +1150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83e054e8",
+   "id": "52301fe3",
    "metadata": {
     "editable": true
    },
@@ -1189,13 +1165,10 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "0cd4400a",
+   "id": "7f400b7f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1206,7 +1179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2868228",
+   "id": "35677f44",
    "metadata": {
     "editable": true
    },
@@ -1217,13 +1190,10 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6e7ef557",
+   "id": "16b1c27a",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1235,7 +1205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47391e12",
+   "id": "7566ab17",
    "metadata": {
     "editable": true
    },
@@ -1246,13 +1216,10 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a9457054",
+   "id": "b10affa1",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1264,7 +1231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a04c51eb",
+   "id": "c50f969e",
    "metadata": {
     "editable": true
    },
@@ -1275,13 +1242,10 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "43e1f145",
+   "id": "35f1236f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1294,7 +1258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e11ea608",
+   "id": "02cec13a",
    "metadata": {
     "editable": true
    },
@@ -1305,13 +1269,10 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "ed021bcf",
+   "id": "821bc62d",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1324,7 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cf97a5d",
+   "id": "5df31e24",
    "metadata": {
     "editable": true
    },
@@ -1335,13 +1296,10 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "0dd77598",
+   "id": "d821d3ff",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1354,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a27628a3",
+   "id": "a99c04f6",
    "metadata": {
     "editable": true
    },
@@ -1366,7 +1324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80585c1d",
+   "id": "fdb302f5",
    "metadata": {
     "editable": true
    },
@@ -1381,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74725f56",
+   "id": "4c91d693",
    "metadata": {
     "editable": true
    },
@@ -1391,7 +1349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd223a73",
+   "id": "316ccd01",
    "metadata": {
     "editable": true
    },
@@ -1403,7 +1361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40dfa247",
+   "id": "73cf195b",
    "metadata": {
     "editable": true
    },
@@ -1414,7 +1372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56e47db3",
+   "id": "c9d6f1ba",
    "metadata": {
     "editable": true
    },
@@ -1429,7 +1387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26251aec",
+   "id": "a5bbc329",
    "metadata": {
     "editable": true
    },
@@ -1444,13 +1402,10 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "dab44ca5",
+   "id": "74dd353c",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1474,13 +1429,10 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "58c875cb",
+   "id": "f07645f8",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1501,7 +1453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d735077",
+   "id": "245ee493",
    "metadata": {
     "editable": true
    },
@@ -1527,13 +1479,10 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "d60ac131",
+   "id": "e428bcdc",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1550,7 +1499,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06e4b620",
+   "id": "861269ad",
    "metadata": {
     "editable": true
    },
@@ -1564,13 +1513,10 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "2174392e",
+   "id": "c34c9894",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1580,7 +1526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72c17dd0",
+   "id": "0ec5724d",
    "metadata": {
     "editable": true
    },
@@ -1591,13 +1537,10 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "7612f9de",
+   "id": "83302e08",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1606,7 +1549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "242d4c4b",
+   "id": "886277ee",
    "metadata": {
     "editable": true
    },
@@ -1617,13 +1560,10 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "0171d1ad",
+   "id": "c1010af7",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1638,7 +1578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5232bfb",
+   "id": "f3c82384",
    "metadata": {
     "editable": true
    },
@@ -1650,13 +1590,10 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "cb2ab622",
+   "id": "70542372",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1677,7 +1614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1998bc9c",
+   "id": "a83eb591",
    "metadata": {
     "editable": true
    },
@@ -1688,13 +1625,10 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "e0ec9915",
+   "id": "2f42295f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1720,7 +1654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebe44ad1",
+   "id": "fb1b0336",
    "metadata": {
     "editable": true
    },
@@ -1731,13 +1665,10 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "6f6d21e6",
+   "id": "0438c751",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1749,7 +1680,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f97c531",
+   "id": "f6d3d4c8",
    "metadata": {
     "editable": true
    },
@@ -1766,7 +1697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36a57ec4",
+   "id": "d552a0d0",
    "metadata": {
     "editable": true
    },
@@ -1778,7 +1709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca69e9e9",
+   "id": "b96bd9e5",
    "metadata": {
     "editable": true
    },
@@ -1809,7 +1740,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4bc6d8a",
+   "id": "41318c5c",
    "metadata": {
     "editable": true
    },
@@ -1821,7 +1752,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd9f61d0",
+   "id": "789c6fe4",
    "metadata": {
     "editable": true
    },
@@ -1849,13 +1780,10 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "81d8ecc3",
+   "id": "8c7472f2",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1882,7 +1810,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9f8b0e5",
+   "id": "25cef2d3",
    "metadata": {
     "editable": true
    },
@@ -1899,7 +1827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37a6973d",
+   "id": "58a04438",
    "metadata": {
     "editable": true
    },
@@ -1911,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b1616dd",
+   "id": "55191297",
    "metadata": {
     "editable": true
    },
@@ -1932,7 +1860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02f59dce",
+   "id": "2951bf64",
    "metadata": {
     "editable": true
    },
@@ -1945,7 +1873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37a3928f",
+   "id": "1109ceeb",
    "metadata": {
     "editable": true
    },
@@ -1976,7 +1904,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ced1c75",
+   "id": "7eeca264",
    "metadata": {
     "editable": true
    },
@@ -1988,7 +1916,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b2c7527",
+   "id": "fd8b7402",
    "metadata": {
     "editable": true
    },
@@ -2006,13 +1934,10 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "dcc4e935",
+   "id": "0de5719f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2036,7 +1961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef0a62d7",
+   "id": "ce194339",
    "metadata": {
     "editable": true
    },
@@ -2058,13 +1983,10 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "e2a9bb21",
+   "id": "cde68f17",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2099,7 +2021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6eca083",
+   "id": "f48a129c",
    "metadata": {
     "editable": true
    },
@@ -2110,7 +2032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c940fd70",
+   "id": "c337129d",
    "metadata": {
     "editable": true
    },
@@ -2123,7 +2045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b1317ef",
+   "id": "d6462e3c",
    "metadata": {
     "editable": true
    },
@@ -2144,7 +2066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3895d32d",
+   "id": "c2a5a5fd",
    "metadata": {
     "editable": true
    },
@@ -2156,7 +2078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88937a4",
+   "id": "d19d16f8",
    "metadata": {
     "editable": true
    },
@@ -2166,7 +2088,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d62abca",
+   "id": "7739ec09",
    "metadata": {
     "editable": true
    },
@@ -2178,7 +2100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "903ee760",
+   "id": "7f433018",
    "metadata": {
     "editable": true
    },
@@ -2190,7 +2112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef89ae2c",
+   "id": "20361838",
    "metadata": {
     "editable": true
    },
@@ -2202,7 +2124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "924ac782",
+   "id": "02d95914",
    "metadata": {
     "editable": true
    },
@@ -2213,7 +2135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50053e83",
+   "id": "524bb980",
    "metadata": {
     "editable": true
    },
@@ -2225,7 +2147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc7df77b",
+   "id": "6cb0b956",
    "metadata": {
     "editable": true
    },
@@ -2247,7 +2169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fe669ae",
+   "id": "142be577",
    "metadata": {
     "editable": true
    },
@@ -2259,7 +2181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28ceb188",
+   "id": "4e765569",
    "metadata": {
     "editable": true
    },
@@ -2272,7 +2194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d89d075b",
+   "id": "f79ebfdf",
    "metadata": {
     "editable": true
    },
@@ -2289,7 +2211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3816234",
+   "id": "eb09c88a",
    "metadata": {
     "editable": true
    },
@@ -2301,7 +2223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ddf946bd",
+   "id": "bf69319c",
    "metadata": {
     "editable": true
    },
@@ -2311,7 +2233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec48e2fd",
+   "id": "eec8df19",
    "metadata": {
     "editable": true
    },
@@ -2323,7 +2245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "462f1772",
+   "id": "313f9634",
    "metadata": {
     "editable": true
    },
@@ -2333,7 +2255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dabcb01d",
+   "id": "83b87f63",
    "metadata": {
     "editable": true
    },
@@ -2345,7 +2267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be2a6bd1",
+   "id": "be859efd",
    "metadata": {
     "editable": true
    },
@@ -2355,7 +2277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb37d8f8",
+   "id": "a976e227",
    "metadata": {
     "editable": true
    },
@@ -2367,7 +2289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de325e18",
+   "id": "9f1dc803",
    "metadata": {
     "editable": true
    },
@@ -2383,7 +2305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c68a0c1",
+   "id": "4fe2c4e9",
    "metadata": {
     "editable": true
    },
@@ -2395,7 +2317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3492a4b",
+   "id": "bd8e264d",
    "metadata": {
     "editable": true
    },
@@ -2406,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "207b9ead",
+   "id": "9a0e9ac7",
    "metadata": {
     "editable": true
    },
@@ -2418,7 +2340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d74c7ddc",
+   "id": "9b20bc14",
    "metadata": {
     "editable": true
    },
@@ -2432,7 +2354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a02ea08c",
+   "id": "5fae8c2f",
    "metadata": {
     "editable": true
    },
@@ -2444,7 +2366,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9863119",
+   "id": "c955d871",
    "metadata": {
     "editable": true
    },
@@ -2469,7 +2391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92ca8aa6",
+   "id": "10feca33",
    "metadata": {
     "editable": true
    },
@@ -2485,14 +2407,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "32b99126",
+   "execution_count": 27,
+   "id": "cd57c0cd",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2533,7 +2452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "185af862",
+   "id": "ed4e2094",
    "metadata": {
     "editable": true
    },
@@ -2550,13 +2469,10 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "2ae9db3d",
+   "id": "309536df",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2574,7 +2490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ade787b",
+   "id": "27597f0a",
    "metadata": {
     "editable": true
    },
@@ -2587,14 +2503,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "e0d18716",
+   "execution_count": 29,
+   "id": "1c60fe5f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2620,7 +2533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd819c63",
+   "id": "36972bcb",
    "metadata": {
     "editable": true
    },
@@ -2639,38 +2552,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "fc0346bc",
+   "execution_count": 30,
+   "id": "85ffeaf7",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            N    Z    A Element  Ebinding\n",
-      "A                                        \n",
-      "4   0       1    3    4      Li  1.153760\n",
-      "5   2       3    2    5      He  5.512132\n",
-      "6   7       3    3    6      Li  5.332331\n",
-      "7   12      4    3    7      Li  5.606439\n",
-      "8   17      4    4    8      Be  7.062435\n",
-      "...       ...  ...  ...     ...       ...\n",
-      "264 3297  156  108  264      Hs  7.298375\n",
-      "265 3303  157  108  265      Hs  7.296247\n",
-      "266 3310  158  108  266      Hs  7.298273\n",
-      "269 3331  159  110  269      Ds  7.250154\n",
-      "270 3337  160  110  270      Ds  7.253775\n",
-      "\n",
-      "[264 rows x 5 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "A = Masses['A']\n",
     "Z = Masses['Z']\n",
@@ -2682,7 +2570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f70ca63",
+   "id": "799627e9",
    "metadata": {
     "editable": true
    },
@@ -2693,14 +2581,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "d56956d6",
+   "execution_count": 31,
+   "id": "365fbba9",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2715,7 +2600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b3df36c",
+   "id": "0d3c5663",
    "metadata": {
     "editable": true
    },
@@ -2725,14 +2610,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "d10ecf29",
+   "execution_count": 32,
+   "id": "60ba302b",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2742,7 +2624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2289c78e",
+   "id": "af13881a",
    "metadata": {
     "editable": true
    },
@@ -2753,38 +2635,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "286afac2",
+   "execution_count": 33,
+   "id": "a5cba204",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 0.02\n",
-      "Variance score: 0.95\n",
-      "Mean absolute error: 0.05\n",
-      "[ 0.00000000e+00 -2.96611194e-02  2.01719003e-01  1.08078025e+01\n",
-      " -4.03097597e+01] 5.294399745619595\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The mean squared error                               \n",
     "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n",
@@ -2810,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fa56ca8",
+   "id": "db2d6c19",
    "metadata": {
     "editable": true
    },
@@ -2823,95 +2680,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "76f0ddd2",
+   "execution_count": 34,
+   "id": "66c27da6",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 1.73\n",
-      "Mean squared error: 19.48\n",
-      "Mean squared error: 12.11\n",
-      "Mean squared error: 23.42\n",
-      "Mean squared error: 0.23\n",
-      "Mean squared error: 0.18\n",
-      "Mean squared error: 0.29\n",
-      "Mean squared error: 0.20\n",
-      "Mean squared error: 0.26\n",
-      "Mean squared error: 4.07\n",
-      "Mean squared error: 2.12\n",
-      "Mean squared error: 7.54\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 15.78\n",
-      "Mean squared error: 122.02\n",
-      "Mean squared error: 51.22\n",
-      "Mean squared error: 152.55\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  1.7304881   19.47958494  12.11340253  23.41589548]\n",
-      " [  0.22948497   0.18392847   0.29364655   0.20072279]\n",
-      " [  0.26303845   4.06730814   2.11590451   7.5411205 ]\n",
-      " [ 15.77893972 122.02334824  51.2167072  152.54894451]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -2951,7 +2726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19fec57c",
+   "id": "50a1e770",
    "metadata": {
     "editable": true
    },
@@ -2971,7 +2746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bcb8b34",
+   "id": "5af218e3",
    "metadata": {
     "editable": true
    },
@@ -3003,7 +2778,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ebf620c",
+   "id": "55450c7b",
    "metadata": {
     "editable": true
    },
@@ -3027,7 +2802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db27019b",
+   "id": "be42ca6d",
    "metadata": {
     "editable": true
    },
@@ -3055,7 +2830,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ab4acd0",
+   "id": "4b7a4ceb",
    "metadata": {
     "editable": true
    },
@@ -3070,7 +2845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b60035b5",
+   "id": "e00d9920",
    "metadata": {
     "editable": true
    },
@@ -3082,7 +2857,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29af7e67",
+   "id": "6243c073",
    "metadata": {
     "editable": true
    },
@@ -3097,7 +2872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "740b4af2",
+   "id": "009ea7de",
    "metadata": {
     "editable": true
    },
@@ -3110,7 +2885,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ad3976",
+   "id": "712c684d",
    "metadata": {
     "editable": true
    },
@@ -3122,7 +2897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0051e649",
+   "id": "b4bcd2a5",
    "metadata": {
     "editable": true
    },
@@ -3132,7 +2907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a7712be",
+   "id": "143b6359",
    "metadata": {
     "editable": true
    },
@@ -3143,7 +2918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27e08926",
+   "id": "d2e56068",
    "metadata": {
     "editable": true
    },
@@ -3161,7 +2936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ec61954",
+   "id": "2541505d",
    "metadata": {
     "editable": true
    },
@@ -3172,7 +2947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7aab6da",
+   "id": "725efee7",
    "metadata": {
     "editable": true
    },
@@ -3184,7 +2959,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "523fa0e8",
+   "id": "cb15e9f1",
    "metadata": {
     "editable": true
    },
@@ -3194,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4172710",
+   "id": "87026e15",
    "metadata": {
     "editable": true
    },
@@ -3206,7 +2981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a21de62",
+   "id": "cd09c85a",
    "metadata": {
     "editable": true
    },
@@ -3216,7 +2991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a68d2930",
+   "id": "c7845cfd",
    "metadata": {
     "editable": true
    },
@@ -3228,7 +3003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "519a6c63",
+   "id": "20e94324",
    "metadata": {
     "editable": true
    },
@@ -3238,7 +3013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f28767d",
+   "id": "46cc127e",
    "metadata": {
     "editable": true
    },
@@ -3257,7 +3032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60e193f4",
+   "id": "b83e3f3f",
    "metadata": {
     "editable": true
    },
@@ -3267,7 +3042,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b76cff50",
+   "id": "d3c3d5b8",
    "metadata": {
     "editable": true
    },
@@ -3279,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c65fa96",
+   "id": "c6e741c3",
    "metadata": {
     "editable": true
    },
@@ -3289,7 +3064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "554a1508",
+   "id": "510abc9b",
    "metadata": {
     "editable": true
    },
@@ -3305,7 +3080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec603f5c",
+   "id": "1a6464a4",
    "metadata": {
     "editable": true
    },
@@ -3325,7 +3100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98c74b9b",
+   "id": "0f403e2d",
    "metadata": {
     "editable": true
    },
@@ -3335,7 +3110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f4d2c5c",
+   "id": "4a8138dc",
    "metadata": {
     "editable": true
    },
@@ -3346,7 +3121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "654bf9a6",
+   "id": "726cbaa4",
    "metadata": {
     "editable": true
    },
@@ -3365,7 +3140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f80909ca",
+   "id": "6b7adfb6",
    "metadata": {
     "editable": true
    },
@@ -3375,7 +3150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee25c0f7",
+   "id": "84165f3e",
    "metadata": {
     "editable": true
    },
@@ -3387,7 +3162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1947e0c",
+   "id": "ebf366ef",
    "metadata": {
     "editable": true
    },
@@ -3397,7 +3172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05229723",
+   "id": "02ffad05",
    "metadata": {
     "editable": true
    },
@@ -3408,7 +3183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb0abff0",
+   "id": "d54237d5",
    "metadata": {
     "editable": true
    },
@@ -3428,7 +3203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc16c9f8",
+   "id": "8a7e9dbb",
    "metadata": {
     "editable": true
    },
@@ -3440,7 +3215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7f65472",
+   "id": "d4d5ff52",
    "metadata": {
     "editable": true
    },
@@ -3455,13 +3230,10 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "acf08e73",
+   "id": "297415bf",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -3538,7 +3310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "937591a1",
+   "id": "2ce99829",
    "metadata": {
     "editable": true
    },
@@ -3548,7 +3320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "132c386c",
+   "id": "f6a44999",
    "metadata": {
     "editable": true
    },
@@ -3560,7 +3332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "869bdef2",
+   "id": "990c34c3",
    "metadata": {
     "editable": true
    },
@@ -3570,7 +3342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7781a1c4",
+   "id": "a06f9457",
    "metadata": {
     "editable": true
    },
@@ -3581,7 +3353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd8e693c",
+   "id": "19cd9843",
    "metadata": {
     "editable": true
    },
@@ -3593,7 +3365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5c29ebf",
+   "id": "cc8c11ab",
    "metadata": {
     "editable": true
    },
@@ -3603,7 +3375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a798f40c",
+   "id": "d3d4e384",
    "metadata": {
     "editable": true
    },
@@ -3615,7 +3387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bae820af",
+   "id": "6187929e",
    "metadata": {
     "editable": true
    },
@@ -3625,7 +3397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06c75a81",
+   "id": "e0ba2cb1",
    "metadata": {
     "editable": true
    },
@@ -3637,7 +3409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59236913",
+   "id": "468fec32",
    "metadata": {
     "editable": true
    },
@@ -3650,7 +3422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81b5da1b",
+   "id": "ba618e10",
    "metadata": {
     "editable": true
    },
@@ -3662,7 +3434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e74c517f",
+   "id": "a74d3f1c",
    "metadata": {
     "editable": true
    },
@@ -3672,7 +3444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "648beb7f",
+   "id": "fb434398",
    "metadata": {
     "editable": true
    },
@@ -3684,7 +3456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d9326d3",
+   "id": "42558cc8",
    "metadata": {
     "editable": true
    },
@@ -3696,7 +3468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f51ed614",
+   "id": "d851da5b",
    "metadata": {
     "editable": true
    },
@@ -3707,7 +3479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ced37c54",
+   "id": "49533cb3",
    "metadata": {
     "editable": true
    },
@@ -3719,7 +3491,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebbeea54",
+   "id": "a62f6a4c",
    "metadata": {
     "editable": true
    },
@@ -3738,7 +3510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b5d06a0",
+   "id": "381182cd",
    "metadata": {
     "editable": true
    },
@@ -3751,7 +3523,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74890321",
+   "id": "04b172c8",
    "metadata": {
     "editable": true
    },
@@ -3761,7 +3533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad33aa43",
+   "id": "265d8614",
    "metadata": {
     "editable": true
    },
@@ -3773,7 +3545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "626df4e6",
+   "id": "68c1005e",
    "metadata": {
     "editable": true
    },
@@ -3783,7 +3555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6594383b",
+   "id": "61fb0fbc",
    "metadata": {
     "editable": true
    },
@@ -3795,7 +3567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec04a0b3",
+   "id": "acb3fa59",
    "metadata": {
     "editable": true
    },
@@ -3805,7 +3577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a82796e0",
+   "id": "82f920b5",
    "metadata": {
     "editable": true
    },
@@ -3817,7 +3589,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9607cc25",
+   "id": "26de4671",
    "metadata": {
     "editable": true
    },
@@ -3828,7 +3600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f84698d3",
+   "id": "071a86ff",
    "metadata": {
     "editable": true
    },
@@ -3840,7 +3612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1245df8a",
+   "id": "fdb51c1d",
    "metadata": {
     "editable": true
    },
@@ -3850,7 +3622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6df8204",
+   "id": "5532b00a",
    "metadata": {
     "editable": true
    },
@@ -3862,7 +3634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6250497e",
+   "id": "a7c57982",
    "metadata": {
     "editable": true
    },
@@ -3872,7 +3644,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f788dc8f",
+   "id": "022ce912",
    "metadata": {
     "editable": true
    },
@@ -3884,7 +3656,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdb72b3a",
+   "id": "12518c37",
    "metadata": {
     "editable": true
    },
@@ -3905,7 +3677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d498ca67",
+   "id": "89f9bbda",
    "metadata": {
     "editable": true
    },
@@ -3916,7 +3688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "786a3fef",
+   "id": "51458daa",
    "metadata": {
     "editable": true
    },
@@ -3928,7 +3700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0080ea0",
+   "id": "eaf4a93e",
    "metadata": {
     "editable": true
    },
@@ -3938,7 +3710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27b8b857",
+   "id": "2fea3537",
    "metadata": {
     "editable": true
    },
@@ -3950,7 +3722,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bf5241a",
+   "id": "ca4f1123",
    "metadata": {
     "editable": true
    },
@@ -3960,7 +3732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b578bc7",
+   "id": "09de659a",
    "metadata": {
     "editable": true
    },
@@ -3972,7 +3744,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11220b8a",
+   "id": "77f9fd08",
    "metadata": {
     "editable": true
    },
@@ -3984,7 +3756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a920365b",
+   "id": "beab9d54",
    "metadata": {
     "editable": true
    },
@@ -3998,13 +3770,10 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "0b7b8c69",
+   "id": "ce0d14e1",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4016,7 +3785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c209e80c",
+   "id": "20bbbdf2",
    "metadata": {
     "editable": true
    },
@@ -4027,13 +3796,10 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "947eedad",
+   "id": "d1b21aae",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4043,7 +3809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55e5aa0a",
+   "id": "02bb27d6",
    "metadata": {
     "editable": true
    },
@@ -4054,13 +3820,10 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "3fb277ad",
+   "id": "98630c99",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4080,7 +3843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36c1d2bc",
+   "id": "9af04036",
    "metadata": {
     "editable": true
    },
@@ -4094,13 +3857,10 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "6db56b3d",
+   "id": "2d8cd3da",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4110,7 +3870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0ee98a4",
+   "id": "71899eed",
    "metadata": {
     "editable": true
    },
@@ -4121,13 +3881,10 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "8774bce4",
+   "id": "f8cc2dd0",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4136,7 +3893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d29c10a",
+   "id": "44f3dd69",
    "metadata": {
     "editable": true
    },
@@ -4147,13 +3904,10 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "93c8b0c6",
+   "id": "acdf8d3d",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4166,7 +3920,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff36a22e",
+   "id": "26d00774",
    "metadata": {
     "editable": true
    },
@@ -4177,13 +3931,10 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "35feafa3",
+   "id": "fcf52c3c",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4194,7 +3945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c4ac2dc",
+   "id": "82083461",
    "metadata": {
     "editable": true
    },
@@ -4216,7 +3967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc2eacf3",
+   "id": "c247a29f",
    "metadata": {
     "editable": true
    },
@@ -4228,7 +3979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48c5a8b6",
+   "id": "5c6266aa",
    "metadata": {
     "editable": true
    },
@@ -4238,7 +3989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc4699e",
+   "id": "c36f8d5c",
    "metadata": {
     "editable": true
    },
@@ -4250,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4db48e9d",
+   "id": "5b1a5174",
    "metadata": {
     "editable": true
    },
@@ -4262,7 +4013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "350fec9b",
+   "id": "81c82695",
    "metadata": {
     "editable": true
    },
@@ -4272,7 +4023,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b474ddf5",
+   "id": "7dc8a3bc",
    "metadata": {
     "editable": true
    },
@@ -4284,7 +4035,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67fc1d79",
+   "id": "41789a38",
    "metadata": {
     "editable": true
    },
@@ -4294,7 +4045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6aa9f684",
+   "id": "4a2e42bf",
    "metadata": {
     "editable": true
    },
@@ -4306,7 +4057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce023be0",
+   "id": "acd67ee6",
    "metadata": {
     "editable": true
    },
@@ -4316,7 +4067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3754c848",
+   "id": "ee90b0eb",
    "metadata": {
     "editable": true
    },
@@ -4328,7 +4079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d884286",
+   "id": "54eaab7f",
    "metadata": {
     "editable": true
    },
@@ -4340,7 +4091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8446340",
+   "id": "f941f1ac",
    "metadata": {
     "editable": true
    },
@@ -4350,7 +4101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6830538",
+   "id": "89e02a88",
    "metadata": {
     "editable": true
    },
@@ -4362,7 +4113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84f9e6b4",
+   "id": "3d1f3b65",
    "metadata": {
     "editable": true
    },
@@ -4372,7 +4123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "586509f5",
+   "id": "1dda0ac2",
    "metadata": {
     "editable": true
    },
@@ -4384,7 +4135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff04787a",
+   "id": "aafe7aae",
    "metadata": {
     "editable": true
    },
@@ -4396,7 +4147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fab40e86",
+   "id": "37f9c1fa",
    "metadata": {
     "editable": true
    },
@@ -4408,7 +4159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "175c9026",
+   "id": "373adac2",
    "metadata": {
     "editable": true
    },
@@ -4418,7 +4169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b8d1d83",
+   "id": "bdd6d800",
    "metadata": {
     "editable": true
    },
@@ -4430,7 +4181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c64f049",
+   "id": "913df005",
    "metadata": {
     "editable": true
    },
@@ -4440,7 +4191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a591f8f",
+   "id": "76f6737e",
    "metadata": {
     "editable": true
    },
@@ -4452,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "956b5691",
+   "id": "48f6aa66",
    "metadata": {
     "editable": true
    },
@@ -4462,7 +4213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85b1bd72",
+   "id": "513a1986",
    "metadata": {
     "editable": true
    },
@@ -4474,7 +4225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6950fb95",
+   "id": "20fbbe07",
    "metadata": {
     "editable": true
    },
@@ -4485,7 +4236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2d998e7",
+   "id": "4dd97daa",
    "metadata": {
     "editable": true
    },
@@ -4497,7 +4248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79e10d20",
+   "id": "032afd0e",
    "metadata": {
     "editable": true
    },
@@ -4507,7 +4258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6585dbe2",
+   "id": "25eac667",
    "metadata": {
     "editable": true
    },
@@ -4519,7 +4270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4291e4ad",
+   "id": "18c343cd",
    "metadata": {
     "editable": true
    },
@@ -4529,7 +4280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a50444a",
+   "id": "9864986f",
    "metadata": {
     "editable": true
    },
@@ -4541,7 +4292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58d08cf2",
+   "id": "1ecdde02",
    "metadata": {
     "editable": true
    },
@@ -4554,7 +4305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8215c3c6",
+   "id": "9d62dddc",
    "metadata": {
     "editable": true
    },
@@ -4566,7 +4317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09efd53b",
+   "id": "db398f5b",
    "metadata": {
     "editable": true
    },
@@ -4578,7 +4329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c694cf4",
+   "id": "2bbf293c",
    "metadata": {
     "editable": true
    },
@@ -4590,7 +4341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "909419d5",
+   "id": "d82e1506",
    "metadata": {
     "editable": true
    },
@@ -4602,7 +4353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d35d3a4",
+   "id": "1ddd417c",
    "metadata": {
     "editable": true
    },
@@ -4614,7 +4365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "845b476b",
+   "id": "e7904873",
    "metadata": {
     "editable": true
    },
@@ -4624,7 +4375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "644470d4",
+   "id": "19604164",
    "metadata": {
     "editable": true
    },
@@ -4636,7 +4387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c4ad6c8",
+   "id": "0f262e03",
    "metadata": {
     "editable": true
    },
@@ -4648,7 +4399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "839e60fb",
+   "id": "cdb18e0d",
    "metadata": {
     "editable": true
    },
@@ -4660,25 +4411,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/doc/LectureNotes/_build/html/genindex.html b/doc/LectureNotes/_build/html/genindex.html
index 905369861..907e860ee 100644
--- a/doc/LectureNotes/_build/html/genindex.html
+++ b/doc/LectureNotes/_build/html/genindex.html
@@ -31,7 +31,7 @@
     <link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=eba8b062" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css?v=6644e6bb" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
   
diff --git a/doc/LectureNotes/_build/html/intro.html b/doc/LectureNotes/_build/html/intro.html
index 179fca5f2..19dc216b0 100644
--- a/doc/LectureNotes/_build/html/intro.html
+++ b/doc/LectureNotes/_build/html/intro.html
@@ -32,7 +32,7 @@
     <link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=eba8b062" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css?v=6644e6bb" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
   
diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html
index 506889057..31483789c 100644
--- a/doc/LectureNotes/_build/html/search.html
+++ b/doc/LectureNotes/_build/html/search.html
@@ -30,7 +30,7 @@
     <link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=eba8b062" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css?v=6644e6bb" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
   
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index b29a6249f..3b1b6e80c 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
+++ b/doc/LectureNotes/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare  Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Discriminative Modeling": [[23, "discriminative-modeling"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example of discriminative modeling, taken from Generative Deeep Learning by David Foster": [[23, "example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise:  Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function  with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function  with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Generative Versus Discriminative Modeling": [[23, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[3, "layers"]], "Layers used to build CNNs": [[5, "layers-used-to-build-cnns"]], "Learning goals": [[0, "learning-goals"], [1, "learning-goals"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[3, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[2, null]], "Linear Regression, basic elements": [[2, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[7, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[7, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[7, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[9, null], [9, "id1"]], "MNIST and GANs": [[6, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[11, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[12, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[7, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[10, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[5, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[7, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix  multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation  and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the  covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning  at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning  at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[13, "other-techniques"]], "Other types of networks": [[14, "other-types-of-networks"]], "Other ways of visualizing the trees": [[11, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[13, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[4, "partial-differential-equations"]], "Practical tips": [[15, "practical-tips"]], "Practicalities": [[21, "practicalities"], [21, "id1"]], "Predicting New Points With A Trained Recurrent Neural Network": [[6, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[5, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[15, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[11, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[15, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[12, "random-forests"]], "Randomized PCA": [[13, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[14, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[6, null]], "Reducing the number of degrees of freedom, overarching view": [[2, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[4, "reformulating-the-problem"]], "Regression Case": [[12, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[3, "regularization"]], "Reminder on Statistics": [[8, "reminder-on-statistics"]], "Replace or not": [[15, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[8, null]], "Resampling methods": [[8, "id1"]], "Resources on differential equations and deep learning": [[4, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[15, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[7, null], [7, "id1"]], "Same code but now with momentum gradient descent": [[15, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[11, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[14, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[4, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[15, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[11, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[2, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations  with Deep Learning": [[4, null]], "Solving the one dimensional Poisson equation": [[4, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[4, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[15, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[2, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[15, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[15, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[15, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[10, null]], "Systematic reduction": [[5, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[21, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[2, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[13, "the-algorithm-before-theorem"]], "The Boston housing data example": [[2, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[3, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[11, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[11, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[5, "the-cifar01-data-set"]], "The MNIST dataset again": [[5, "the-mnist-dataset-again"]], "The RELU function family": [[3, "the-relu-function-family"]], "The Softmax function": [[3, "the-softmax-function"]], "The \\chi^2 function": [[2, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[8, "the-bias-variance-tradeoff"]], "The code for solving the ODE": [[4, "the-code-for-solving-the-ode"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[9, "the-logistic-function"]], "The moons example": [[10, "the-moons-example"]], "The multilayer  perceptron (MLP)": [[14, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[4, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The plethora  of machine learning algorithms/methods": [[23, "the-plethora-of-machine-learning-algorithms-methods"]], "The singular value decomposition": [[7, "the-singular-value-decomposition"]], "The two-dimensional case": [[10, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[13, "towards-the-pca-theorem"]], "Train and test datasets": [[3, "train-and-test-datasets"]], "Two-dimensional Objects": [[5, "two-dimensional-objects"]], "Type of problem": [[4, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[15, "using-autograd"]], "Using forward Euler to solve the ODE": [[4, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[15, "using-gradient-descent-methods-limitations"]], "Visualization": [[3, "visualization"], [3, "id1"]], "Visualizing the Tree, Classification": [[11, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "What Is Generative Modeling?": [[23, "what-is-generative-modeling"]], "What is Machine Learning?": [[2, "what-is-machine-learning"]], "What is a good model?": [[2, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[23, "what-is-a-good-model-can-we-define-it"]], "Which activation function should I use?": [[3, "which-activation-function-should-i-use"]], "Why Linear Regression (aka Ordinary Least Squares and family)": [[23, "why-linear-regression-aka-ordinary-least-squares-and-family"]], "Wisconsin Cancer Data": [[9, "wisconsin-cancer-data"]], "Writing Our First Generative Adversarial Network": [[6, "writing-our-first-generative-adversarial-network"]], "Writing our own PCA code": [[13, "writing-our-own-pca-code"]], "XGBoost: Extreme Gradient Boosting": [[12, "xgboost-extreme-gradient-boosting"]], "scikit-learn implementation": [[3, "scikit-learn-implementation"]]}, "docnames": ["E1", "E2", "chapter1", "chapter10", "chapter11", "chapter12", "chapter13", "chapter2", "chapter3", "chapter4", "chapter5", "chapter6", "chapter7", "chapter8", "chapter9", "chapteroptimization", "clustering", "intro", "linalg", "schedule", "statistics", "teachers", "textbooks", "week34"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["E1.ipynb", "E2.ipynb", "chapter1.ipynb", "chapter10.ipynb", "chapter11.ipynb", "chapter12.ipynb", "chapter13.ipynb", "chapter2.ipynb", "chapter3.ipynb", "chapter4.ipynb", "chapter5.ipynb", "chapter6.ipynb", "chapter7.ipynb", "chapter8.ipynb", "chapter9.ipynb", "chapteroptimization.ipynb", "clustering.ipynb", "intro.md", "linalg.ipynb", "schedule.md", "statistics.ipynb", "teachers.md", "textbooks.md", "week34.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 23], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "00": [2, 3, 7, 13, 23], "000": [3, 5], "00000000e": 23, "001": [4, 10, 15], "004": 7, "00727646693": [2, 23], "0086649156": [2, 23], "01": [2, 3, 4, 7, 11, 13, 15, 22, 23], "0110": 20, "01719003e": 23, "02": [2, 6, 9, 14, 23], "02334824": 23, "02857": 6, "02f": 8, "03077640549": 6, "03097597e": 23, "031": 7, "04": 13, "0458": 11, "05": [6, 8, 23], "062292565": 6, "062435": 23, "06730814": 23, "07": 23, "0713": [2, 23], "07285": 5, "08": 20, "08078025e": 23, "08336233266": 6, "0917": 11, "0n": [2, 23], "1": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23], "10": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23], "100": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "1000": [2, 3, 4, 6, 7, 10, 13, 15, 16, 17, 20, 23], "10000": [4, 7, 8, 12, 13, 15, 20], "100000": 10, "10001": 12, "1001": 20, "1002": 20, "1003": 20, "1005": 20, "1009": 20, "101": 1, "1011": 20, "1013": 20, "1013904243": 20, "1015": 20, "102": 1, "1023": 20, "1024": 5, "1026": 20, "1027": 20, "103": 3, "1030": 20, "1037": 20, "1038": 20, "1040": 20, "1047": 20, "107": 1, "108": 23, "10th": 11, "10x": [2, 23], "11": [1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 22, 23], "110": 23, "1100": 20, "1101": 20, "111": [3, 9, 14], "112": 1, "11340253": 23, "11590451": 23, "116": 1, "117": 1, "118": 1, "12": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 18, 20, 22, 23], "120": 5, "121": [1, 10, 11, 12], "1215pm": [21, 23], "122": [10, 11, 12, 23], "124": [2, 23], "125": 1, "127": [1, 6], "128": [5, 6, 15], "129": 1, "1298": 11, "12pm": [21, 23], "13": [2, 4, 11, 14, 18, 20, 23], "131": 1, "133": 9, "135": 1, "136": 1, "14": [2, 4, 6, 8, 10, 11, 12, 14, 18, 20, 22], "141": 1, "143": 1, "1446729567": 6, "149": 1, "14g": 8, "15": [2, 4, 6, 8, 9, 10, 11, 14, 15, 20, 23], "150": [6, 10], "152": [1, 23], "153760": 23, "156": [1, 23], "157": 23, "158": 23, "159": [1, 23], "15g": 8, "15pm": 23, "16": [3, 4, 5, 6, 7, 10, 11, 12, 20, 23], "160": [1, 23], "1603": 5, "161": 1, "162": 1, "16231451": 6, "163": 1, "16384": 5, "164": 1, "167": 1, "17": [3, 4, 10, 20, 23], "172": 1, "173": 1, "176": 1, "178": 1, "179": 1, "1797": 3, "18": [4, 8, 9, 10, 11, 12, 20, 23], "1807": 6, "18392847": 23, "19": [4, 20, 23], "1940": 2, "1943": 14, "1970": [18, 23], "1973": 11, "1979": 8, "1_1": 14, "1_2": 14, "1_3": 14, "1cm": [2, 10, 12, 20, 23], "1d": [3, 4, 5], "1e": [4, 6, 15, 16], "1e10": 16, "1e4": 8, "1f": 3, "1k": 18, "1n": [2, 23], "1x": [2, 23], "2": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "20": [1, 2, 3, 4, 8, 9, 10, 20, 21, 23], "200": [2, 4, 5, 6, 10, 11, 12], "2000": 2, "2004": 15, "2006": 22, "20072279": 23, "2008": 23, "2010": 3, "2011": 3, "2014": 6, "2015": 3, "2016": [2, 23], "2018": [2, 8], "2021": [8, 16], "2022": 23, "2025": 23, "21": [2, 3, 7, 9, 11, 14, 18, 23], "2116753732": 6, "215pm": [21, 23], "2167072": 23, "22": [2, 3, 7, 14, 15, 18, 23], "221": 10, "225": 6, "22948497": 23, "23": [3, 14, 18, 23], "24": [2, 3, 18, 23], "25": [4, 5, 6, 7, 8, 10, 11, 13], "250": [4, 6, 9, 11], "25000": 2, "250154": 23, "253775": 23, "255": 5, "256": 6, "26": 23, "26303845": 23, "264": 23, "265": 23, "265109911": 6, "266": 23, "269": 23, "27": [2, 3], "270": 23, "27n_": 20, "28": [3, 5, 6], "2830637392": 6, "2861": 20, "2873": 11, "2882": 20, "2886": 20, "2890": [2, 23], "2892": 20, "29": 23, "2915": 20, "2931": 23, "29364655": 23, "294399745619595": 23, "296247": 23, "2968": 23, "2980": 23, "298273": 23, "298375": 23, "2990": 23, "2_": 14, "2_1": 14, "2_2": 14, "2_3": 14, "2_i": 14, "2_m": [8, 20], "2_t": 15, "2_x": 20, "2b": 20, "2cm": 10, "2d": [3, 5, 13, 14, 17, 23], "2e": 8, "2f": [2, 9, 11, 12, 13, 14, 23], "2g": 4, "2g_i": 4, "2k": 5, "2m": 8, "2n": [2, 4, 5, 23], "2nd": 11, "2p": 20, "2pt": 6, "2x": [2, 5, 10, 15, 23], "2x_ix_jy_iy_j": 10, "2x_j": 10, "2y_i": 12, "2y_j": 10, "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "30": [2, 3, 6, 8, 9, 12, 15, 21], "30000": [2, 23], "3072": 5, "31": [14, 18, 20], "315": 8, "3155": [2, 7, 8], "32": [5, 6, 8, 14, 15, 18, 20], "3200": 3, "3250": 3, "3297": 23, "33": [14, 18, 21], "3303": 23, "3310": 23, "332331": 23, "333": 9, "3331": 23, "3337": 23, "34": 18, "3436": [2, 23], "3437": [2, 23], "35": [2, 8, 23], "3581341341": 6, "359": 7, "36": [2, 7, 8, 20], "370782966": 6, "38": 20, "39": [2, 21, 23], "3d": [1, 4, 5, 6, 8, 15], "3f": [3, 5, 11], "3n": 18, "3x": [4, 10], "3x_i": 4, "3y": 10, "4": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "40": [3, 8, 21, 23], "400": 6, "4000": 23, "4050": [22, 23], "41": 18, "4155": [0, 4], "41589548": 23, "42": [3, 6, 10, 11, 12, 18, 23], "43": [2, 9, 18], "4310": 23, "436462435": 6, "44": [2, 18], "45": [21, 23], "46": [21, 23], "462": 9, "47": [21, 23], "479465113": 6, "47958494": 23, "48": 23, "48257387": [21, 23], "49": [7, 8, 13], "49152": 5, "4940954": [2, 23], "4990": 20, "4992": 20, "4997": 20, "4c4c7f": [11, 12], "4d": 5, "4f": 8, "4pm": [21, 23], "4y": 10, "4y_i": 12, "5": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "50": [3, 4, 5, 6, 8, 9, 10, 12, 15, 23], "500": [3, 5, 6, 8, 11, 12, 15], "5018": 20, "506": 2, "507d50": [11, 12], "50j": 15, "50x10": 3, "51": [12, 23], "510": 3, "512132": 23, "5177783846": 6, "53": 11, "54": [8, 20, 23], "5411205": 23, "54894451": 23, "55": [3, 23], "56": 3, "56536": [2, 23], "569": 3, "57": [2, 10, 21, 23], "571": 7, "58": [12, 21, 23], "591317992": 6, "5cm": 20, "5f": 10, "5x": 10, "5y": 10, "6": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 21, 23], "60": [3, 5], "60000": 6, "6019067271": 6, "606439": 23, "625": 9, "63": [2, 3], "64": [3, 5, 6, 15, 18, 23], "64x50": 3, "65": [3, 10, 11], "6887363571": 6, "69": [1, 20], "69069n_": 20, "691": 23, "6n_": 20, "7": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "70": [3, 9], "70653767": 6, "71": 3, "724": 5, "73": 23, "7304881": 23, "75": [7, 8, 10, 13], "76": [21, 23], "765": 9, "77": [21, 23], "7718": 11, "7782028952": 6, "77893972": 23, "78": 23, "7d7d58": [11, 12], "8": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 21, 23], "80": [2, 3, 7, 10], "800": [6, 9], "81": 3, "815am": [21, 23], "85": 3, "8702784034": 6, "88": 23, "8f": 8, "8g": 8, "8n": 18, "8x8": 3, "9": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "90": 3, "9040": 11, "91": [21, 23], "92": [21, 23], "93": 1, "931": [2, 23], "933": 7, "937": 20, "938": 20, "939": [2, 20, 23], "94": 20, "95": [3, 13, 23], "954": 20, "955820c21e8b": 6, "96": 8, "960": 20, "961": 20, "962": 20, "9649652536": 6, "96611194e": 23, "9780387310732": 22, "9780387848570": 22, "9781098134174": 23, "9781492032632": 22, "9781801819312": 23, "98": [1, 2, 3], "985": 20, "986": 20, "989": 20, "9898ff": [11, 12], "99": [1, 15], "991": 20, "992": 20, "993": 20, "996": 7, "999": [11, 20], "9x": 8, "9y": 8, "A": [0, 1, 4, 5, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22], "AND": 4, "And": [2, 5, 6, 7, 8, 11, 15, 17, 20], "As": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 18, 20, 23], "At": [2, 6, 8, 15, 23], "BE": [2, 23], "Be": [4, 17, 23], "Being": 15, "But": [1, 2, 3, 4, 5, 7, 8, 11, 12, 20], "By": [2, 5, 7, 8, 14, 15, 18, 23], "For": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "IF": 8, "IN": 22, "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "Ising": [7, 14], "It": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "Its": [3, 4, 6, 13], "No": [8, 11, 23], "Not": [2, 3, 7, 8], "OR": 20, "Of": 20, "On": [2, 5, 20, 21, 22, 23], "One": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 20], "Or": [2, 3, 8, 23], "Such": [1, 2, 8, 14, 20], "That": [2, 7, 9, 12, 13, 14, 16, 20, 23], "The": [1, 6, 12, 15, 16, 18, 19, 20, 21, 22], "Then": [0, 1, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "There": [0, 2, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 21, 23], "These": [2, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20], "With": [1, 2, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 23], "_": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "_0": [7, 10, 12, 13, 15], "_1": [4, 7, 8, 10, 12, 13, 14, 15, 16, 18], "_2": [4, 7, 10, 13, 14, 15, 18], "_3": 18, "_4": 18, "_9": 15, "__class__": 12, "__doc__": 8, "__future__": [10, 11], "__init__": 3, "__main__": 4, "__name__": [4, 12], "_auto1": [4, 5, 6, 7, 8, 9, 14, 15, 18, 20], "_auto10": [8, 14], "_auto11": 8, "_auto12": 8, "_auto2": [4, 5, 6, 7, 8, 14, 15, 18, 20], "_auto3": [5, 6, 7, 8, 14, 15, 18], "_auto4": [6, 8, 14, 15, 18], "_auto5": [6, 8, 14, 15, 18], "_auto6": [6, 8, 14, 18], "_auto7": [6, 8, 14, 18], "_auto8": [8, 14], "_auto9": [8, 14], "_build": [2, 17, 22, 23], "_c": 3, "_compon": 13, "_depth": 11, "_export": [0, 1], "_fraction": 11, "_i": [2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 23], "_j": [2, 3, 4, 5, 7, 8, 10, 15], "_k": 15, "_l": 14, "_lambda": 8, "_leaf": 11, "_m": 12, "_multilayer_perceptron": 23, "_n": [4, 7, 10, 13, 15], "_node": 11, "_p": [7, 10], "_ratio": 13, "_sampl": 11, "_split": [8, 11], "_t": 15, "_test": 8, "_varianc": 13, "_weight": 11, "a0": 5, "a0faa0": [11, 12], "a1": [2, 23], "a2": [2, 23], "a3": [2, 23], "a4": [2, 23], "a_": [1, 2, 3, 18, 23], "a_0": [2, 23], "a_1a": [2, 23], "a_2a": [2, 23], "a_3": [2, 23], "a_3a": [2, 23], "a_4": [2, 23], "a_4a": [2, 23], "a_h": 3, "a_i": [2, 3, 4, 14, 23], "a_j": [3, 14], "a_k": [2, 3, 14], "aaron": 22, "ab": [2, 4, 7, 15, 16, 23], "ab_channel": 17, "abandon": 3, "abid": 20, "abil": [2, 12], "abl": [1, 2, 3, 6, 7, 8, 9, 12, 14, 15], "about": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21], "abov": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "abovement": [8, 23], "abscissa": 15, "absolut": [2, 4, 7, 8, 15, 23], "abstract": 3, "acceler": 15, "accept": [2, 5, 8, 11], "access": [2, 5, 13, 20, 23], "accid": [6, 8], "accompani": [2, 23], "accomplish": [10, 11, 15], "accord": [2, 3, 4, 7, 8, 11, 14, 15, 16, 20, 23], "accordingli": 13, "account": [0, 1, 2, 5, 7, 15, 20, 23], "accumul": [14, 15, 20], "accur": [2, 5, 6, 8, 12, 15], "accuraci": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 23], "accuracy_scor": [2, 3, 12, 23], "accuracy_score_numpi": 3, "achiev": [2, 3, 7, 8, 10, 14, 18, 23], "aco": 20, "acquaint": 17, "acquir": [3, 17, 23], "acr": 2, "across": [3, 5, 8, 11, 17, 23], "act": [3, 5, 18], "action": 20, "activ": [0, 2, 4, 5, 6, 11, 19, 21, 23], "actual": [0, 1, 2, 3, 6, 7, 8, 10, 13, 18, 20, 23], "ad": [0, 1, 3, 5, 6, 7, 10, 15, 18], "ada_clf": 12, "adaboostclassifi": 12, "adadelta": 15, "adam": [3, 5, 6, 23], "adapt": [6, 8, 15, 22], "add": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 20, 21, 23], "add_subplot": [3, 9, 14, 16], "addendum": 7, "addit": [0, 2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23], "addition": [14, 15], "address": [3, 11, 13, 15, 23], "adjac": [5, 14], "adjoint": 7, "adjust": [2, 7, 14, 15], "admir": [2, 23], "advanc": [6, 8, 14, 22, 23], "advantag": [3, 5, 7, 8, 12, 15, 18], "adversari": 23, "afecionado": 23, "affect": [0, 5], "affin": [2, 5, 10, 13], "afford": 5, "aficionado": 23, "aforement": 16, "african": 2, "after": [0, 1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 17, 18, 20, 23], "afterward": [2, 23], "ag": [2, 9, 23], "ag_0": 4, "again": [2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "against": [3, 6, 9, 12], "agegroup": 9, "agegroupmean": 9, "aggreg": [11, 12], "agorithm": 12, "agre": [7, 8, 20], "agreement": 15, "ahead": 11, "ai": [2, 22], "aid": 13, "aim": [1, 2, 3, 6, 8, 9, 13, 16, 17, 18], "ainv": 7, "airplan": 5, "aka": 7, "al": [1, 2, 4, 6, 22, 23], "alarm": [7, 9], "algebra": [2, 5, 7, 15, 17], "algorithm": [1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 16, 17, 18, 20, 22], "align": [2, 4, 7, 8, 9, 10, 15, 20, 23], "all": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "allevi": [3, 15], "alloc": [5, 18], "allow": [2, 3, 4, 5, 7, 8, 10, 12, 15, 17, 18, 23], "almost": [2, 3, 8, 10, 13, 15, 20], "alon": [4, 11], "along": [0, 4, 5, 6, 7, 8, 11, 12, 13, 17, 18, 23], "alpha": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 23], "alpha_": 12, "alpha_0": 5, "alpha_1": 5, "alpha_2": 5, "alpha_i": [5, 15], "alpha_k": 15, "alpha_m": 12, "alpha_n": 5, "alpha_opt": 15, "alreadi": [0, 4, 5, 6, 7, 8, 12, 14, 17, 18, 20, 23], "also": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "alter": 3, "altern": [2, 3, 6, 7, 8, 10, 11, 13, 15, 18, 23], "although": [1, 2, 3, 7, 8, 10, 12, 15, 23], "alwai": [1, 2, 5, 7, 8, 14, 15, 20, 23], "am": 6, "ame2016": [2, 23], "american": 2, "among": [2, 5, 7, 11, 12, 14, 18, 23], "amongst": 7, "amount": [2, 3, 5, 6, 8, 10, 12, 16, 17], "an": [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22], "an_": 20, "anaconda": [2, 3, 17, 23], "analogi": 15, "analys": 8, "analysi": [3, 5, 6, 9, 16, 18, 22], "analyt": [4, 5, 7, 8, 9, 14, 15, 17, 23], "analyz": [1, 2, 3, 5, 6, 7, 8, 20], "andrew": 3, "angl": [2, 5, 11], "anharmon": 5, "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 23], "anim": [6, 14], "ann": 14, "annot": [2, 3, 5, 9, 10, 23], "announc": 23, "anoth": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "ansatz": [2, 23], "answer": [2, 3, 5, 7, 8, 18, 21, 23], "antialias": [4, 8], "anticip": 6, "anymor": [3, 10], "anyon": [0, 6, 10], "anyth": [0, 1, 3, 20], "anytim": [21, 23], "apach": 3, "apart": [13, 15], "api": [3, 17, 23], "appar": 4, "appear": [2, 3, 5, 15, 18, 20], "append": [3, 5, 6, 10, 11, 15, 23], "appli": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 20, 22, 23], "applic": [1, 2, 3, 5, 6, 7, 8, 9, 11, 14, 15, 18, 20, 22, 23], "apply_gradi": 6, "approach": [0, 1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 22], "appropri": [4, 8, 11, 14, 15, 17, 20], "approv": 23, "approx": [2, 4, 5, 8, 12, 13, 15, 20, 23], "approxim": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 15, 20, 23], "apt": [2, 17, 23], "aq": 20, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "aragorn": 23, "arang": [3, 5, 6, 8, 9, 11, 12, 14, 15, 23], "arbitrari": [3, 6, 8, 10, 14, 15, 20], "arbitrarili": [2, 3, 13, 23], "arc": 8, "architectur": [5, 6, 14], "area": [2, 5, 8, 22, 23], "argmax": [3, 13], "argmin": [6, 12, 16], "argsort": 13, "argu": [3, 15], "argument": [2, 4, 5, 7, 13, 14, 15, 23], "aris": [2, 8, 14, 15, 20, 23], "arithmet": [2, 15, 18, 23], "arm": 8, "armadillo": 18, "around": [2, 3, 6, 7, 8, 13, 20, 23], "arrai": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20], "arrang": [5, 23], "arraybox": 15, "arriv": [2, 8, 11, 13, 18, 20, 23], "arrow": 14, "arrowprop": 10, "art": [2, 3, 17], "articl": [2, 5, 6, 8, 12, 23], "artifici": [2, 4, 9, 14, 22, 23], "artificialneuron": 14, "arug": 15, "arxiv": [5, 6], "asarrai": [2, 8, 11], "ask": [0, 7, 8, 13, 14], "aspect": [2, 8, 17, 23], "assembl": 5, "assembli": [2, 23], "assert": 6, "assess": [2, 8, 23], "assici": 6, "assign": [0, 2, 9, 10, 11, 14, 15, 16, 19, 21, 22, 23], "associ": [2, 8, 11, 14, 16, 20, 23], "assum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "assumpt": [2, 5, 7, 8, 11, 13, 20, 23], "ast": [2, 7, 8, 23], "astyp": [6, 11, 12], "asymmetri": [2, 23], "asymptot": [6, 8], "atom": [2, 23], "attempt": [2, 6, 8, 9, 10, 12, 23], "attend": 23, "attent": [2, 18, 23], "attract": [2, 12, 23], "attribut": [2, 11, 23], "audi": [2, 23], "audio": [5, 6], "august": 23, "aurelien": [2, 22, 23], "austfjel": 8, "auth": 0, "authent": 0, "author": [2, 3, 12, 20], "authour": 23, "auto": [11, 12, 20], "autocor": 20, "autocorrelation_tim": 20, "autocorrelform": 20, "autocovari": 20, "autoencod": [6, 17, 23], "autoencond": 17, "autograd": [17, 23], "autom": [2, 17, 22, 23], "automac": 18, "automag": 23, "automat": [1, 2, 3, 4, 5, 6, 13, 17, 18, 23], "automobil": 5, "autonom": 6, "avail": [2, 3, 6, 8, 12, 13, 17, 18, 19, 21, 22, 23], "averag": [2, 3, 5, 8, 11, 12, 15, 16, 20, 21, 23], "avoid": [2, 6, 7, 8, 11, 13, 15, 18], "awai": [4, 5, 8], "awar": [4, 12], "award": [21, 23], "ax": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 23], "axes3d": [4, 8, 15], "axes_grid1": 8, "axhlin": 10, "axi": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "axiom": 7, "axvlin": [6, 10], "axvspan": 6, "b": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 20, 21, 23], "b1": 10, "b2": 10, "b3": 10, "b_": [2, 3, 18], "b_0": 2, "b_1": [2, 4, 14, 15], "b_2": [2, 15], "b_5": 15, "b_group": 11, "b_i": [2, 3, 4, 14, 23], "b_ia_": [2, 23], "b_ia_i": 2, "b_index": 11, "b_j": [3, 14], "b_k": [2, 3, 14, 15], "b_m": 14, "b_score": 11, "b_valu": 11, "babcock": 23, "bachelor": [19, 21], "back": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 18, 20, 23], "backbon": 18, "backend": [3, 6], "background": [22, 23], "backpropag": 3, "backtrack": 11, "backup": 18, "backward": [3, 4, 6, 14, 18], "bad": 8, "badli": 20, "bag": [11, 17, 23], "bag_clf": 12, "baggin": 23, "baggingboot": 12, "baggingclassifi": 12, "baggingtre": 12, "balanc": 8, "band": 18, "bandwidth": 18, "bar": [2, 8, 13, 23], "barber": 22, "bare": [6, 12], "base": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23], "basi": [7, 9, 10, 12, 13, 14, 15, 18], "basic": [0, 8, 10, 14, 15, 16, 17, 20, 23], "batch": [5, 6, 13, 14, 15], "batch_shap": 6, "batch_siz": [3, 5, 6], "batchnorm": 6, "bay": 9, "bayesian": [7, 17, 22, 23], "becaus": [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 23], "becom": [2, 3, 4, 7, 8, 9, 11, 14, 15, 20, 23], "been": [2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 17, 18, 23], "befor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 18, 20, 23], "beforehand": [2, 20, 23], "begin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 20, 21, 23], "behav": [3, 8, 15], "behavior": [2, 3, 15, 23], "behaviour": 14, "behind": [2, 3, 8, 10, 15, 23], "being": [2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 20, 23], "believ": [11, 18], "belong": [9, 10, 11, 15, 16], "below": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "benchmark": 12, "benefici": [3, 15], "benefit": [2, 3, 6, 13, 15, 17, 23], "bengio": [3, 22, 23], "benign": [3, 9], "besid": [6, 7], "bessel": 7, "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 21, 23], "beta": [1, 2, 3, 5, 12, 13, 15, 23], "beta_": [5, 15], "beta_0": [3, 5, 15], "beta_1": [3, 5, 12, 15], "beta_1x_i": 15, "beta_2": [5, 15], "beta_3": 5, "beta_i": 5, "beta_j": 15, "beta_k": 15, "beta_linreg": 15, "beta_m": 12, "beta_mg_m": 12, "beta_n": 5, "better": [2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 23], "between": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 23], "beyond": [2, 3, 7, 8, 10, 15, 23], "bf": [15, 16, 18, 20], "bg": 23, "bgd": 15, "bia": [2, 3, 4, 5, 7, 10, 11, 12, 14, 15, 23], "bias": [3, 4, 5, 7, 8, 11, 14], "big": [2, 3, 4, 7, 8, 16], "bigger": [3, 8], "bigr": 14, "bike": 11, "bilbo": 23, "billion": [5, 14, 17], "bin": [2, 9, 20], "binari": [2, 5, 7, 9, 11, 12, 14, 23], "binarycrossentropi": 6, "bind": 2, "binomi": [17, 20, 23], "binsboot": 8, "bioinformat": 2, "biolog": [3, 14], "bios1100": [17, 23], "bird": [2, 5], "birth": 23, "bishop": [22, 23], "bit": [3, 6, 18, 20, 23], "bitwis": 20, "bivari": 4, "bk": [2, 15], "bla": [18, 23], "black": [10, 11, 16], "block": [8, 12, 17, 18, 20, 23], "blog": 23, "blogpost": 6, "blue": [2, 5], "bmatrix": [2, 3, 5, 7, 9, 10, 13, 15, 18, 23], "bmi": 3, "bodi": [2, 3, 6, 14], "bold": 3, "boldfac": [1, 2, 7], "boldsymbol": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 23], "boltzmann": [14, 17, 23], "book": [22, 23], "book1": 22, "boolean": 6, "boost": [3, 11, 17, 23], "boostrap": 12, "bootstrap": [3, 15, 17, 23], "borrow": 23, "boston_dataset": 2, "bot": 10, "both": [0, 1, 2, 3, 6, 7, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23], "bottl": 9, "bound": [2, 10, 14], "boundari": [4, 6, 10, 13, 14], "box": [6, 11], "boyd": [10, 15], "bracket": [6, 20], "brain": [3, 9, 14], "branch": [11, 23], "break": [2, 6, 8, 13, 16, 23], "breast": [7, 9, 13], "breviti": 15, "brew": [2, 17, 23], "brg": 10, "briefli": [1, 2, 23], "bring": [2, 7, 8, 12], "britt": [21, 23], "broad": 2, "broadli": 23, "brought": [15, 17, 23], "brownle": 6, "browser": [0, 23], "brute": [5, 7, 13], "buffer_s": 6, "bui": 6, "build": [1, 2, 6, 7, 8, 12, 18, 20, 23], "built": [2, 3, 5, 6, 8], "bunch": 13, "busi": 2, "byte": [18, 23], "c": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "c1": [10, 13], "c2": [10, 13], "c_": [2, 10, 11, 12, 15, 20], "c_0": 20, "c_1": 14, "c_2": 14, "c_3": 14, "c_4": 14, "c_i": [14, 15], "c_k": 20, "ca": [3, 23], "cach": 12, "cal": [2, 10, 12, 14, 15], "calcul": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "call": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "calor": 2, "cambridg": [15, 22], "can": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "cancel": [2, 15, 23], "cancer": [7, 12], "cancerpd": 9, "candid": [10, 11, 12], "cannot": [2, 3, 6, 7, 8, 9, 10, 11, 20], "canopi": [2, 17, 23], "canva": [0, 1, 23], "cap": 7, "capabl": [2, 3, 10, 15, 17, 23], "capac": [4, 21], "capita": 2, "captur": [6, 13, 14, 23], "car": [5, 6], "card": [2, 9, 23], "cardin": 3, "care": [0, 13], "carefulli": 15, "carlo": [2, 8, 17, 20, 22, 23], "carri": [4, 8, 9], "cart": 12, "case": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 17, 18, 23], "casella": 22, "cast": 3, "cat": [5, 6], "catch": 2, "categor": [2, 3, 5, 11, 13, 23], "categori": [2, 3, 5, 9, 12, 14, 16, 23], "categorical_crossentropi": [3, 5], "caus": [2, 7, 8, 20, 23], "causal": 2, "causat": [2, 23], "cax": 3, "cb": [8, 23], "cbar": 3, "cc": [2, 3, 7, 15, 23], "ccc": [7, 14], "cdf": 20, "cdot": [2, 4, 8, 14, 15, 16, 18, 20, 23], "celebr": 15, "cell": 6, "center": [2, 3, 8, 9, 10, 11, 13, 16, 20, 23], "central": [1, 2, 5, 7, 8, 10, 18, 23], "centroid": [16, 20], "centroid_differ": 16, "centuri": 5, "certain": [2, 5, 8, 9, 11, 20, 23], "cg": 15, "cha": 2, "chain": [2, 3, 15, 17, 20, 23], "challeng": 0, "chanc": [3, 7, 15, 20], "chang": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "channel": 5, "chapter": [1, 2, 8, 12, 13, 18, 22, 23], "chapter3": 2, "charact": [2, 5, 7, 23], "character": [10, 11, 12, 14, 20], "characterist": [2, 3, 5, 12, 15, 23], "charg": [2, 23], "charl": 2, "chase": 6, "chatgpt": 0, "chd": 9, "chddata": 9, "cheap": 7, "cheaper": [3, 15], "check": [0, 1, 2, 3, 5, 6, 7, 13, 15, 18, 23], "checkmark": 5, "checkpoint": 6, "checkpoint_dir": 6, "checkpoint_prefix": 6, "chen": 12, "chiaramont": 4, "childcar": 1, "children": 1, "choic": [2, 3, 4, 5, 6, 8, 11, 14, 15, 16, 18, 23], "choleski": [7, 18], "choos": [0, 4, 5, 8, 11, 12, 13, 15, 16], "chosen": [1, 2, 3, 4, 8, 10, 11, 12, 15, 20, 23], "chosen_datapoint": 3, "christian": 22, "christoph": [22, 23], "cifar": 5, "cifar10": 5, "circ": [3, 14], "circl": [2, 10, 14], "circuit": 5, "circumfer": 11, "circumv": [3, 7, 15], "ckpt": 6, "clariti": 20, "class": [2, 3, 5, 6, 8, 9, 10, 11, 13, 14, 15, 20, 23], "class_nam": [5, 11], "class_val": 11, "class_valu": 11, "classic": [9, 11, 15], "classif": [2, 5, 7, 8, 9, 10, 13, 14, 17, 22, 23], "classifi": [2, 3, 6, 9, 11, 12, 13, 23], "classificaton": 3, "classifii": 12, "clean": 3, "clear": [3, 7, 12, 14, 15], "clearli": [2, 5, 7, 8, 9, 10, 20], "clever": [3, 12], "clf": [2, 8, 10, 11, 12, 23], "clf3": 2, "clf_lasso": 8, "clf_ridg": 8, "cli": 0, "clip": [5, 20], "clone": [0, 21], "close": [2, 3, 4, 6, 8, 10, 11, 13, 14, 15, 16, 20, 22, 23], "closer": [5, 7, 15], "closest": [10, 13, 15, 16], "closur": [17, 23], "cloud": [17, 23], "cluster": [2, 3, 6, 8, 13, 17, 23], "cluster_label": 16, "cm": [3, 4, 5, 8, 10, 15], "cmap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 23], "cmap_arg": 8, "cmd": [0, 11], "cn_": 20, "cnn": 14, "cnn_kera": 5, "cntk": [17, 23], "co": [2, 4, 5, 8, 11, 15, 23], "code": [5, 6, 8, 9, 10, 17, 18, 20, 22], "coef": [2, 23], "coef0": 10, "coef_": [1, 2, 7, 8, 10, 11, 15, 23], "coeff": 7, "coeffici": [2, 5, 7, 8, 9, 10, 11, 15, 18, 23], "coerc": [2, 8, 23], "coin": [12, 20], "coin_toss": 12, "col": [2, 13, 23], "colab": [17, 23], "cold": 11, "colinear": 2, "collaps": 10, "collect": [4, 8, 12, 13, 17, 20, 22, 23], "collinear": 7, "color": [2, 5, 6, 8, 10, 11, 12, 20], "color_channel": 5, "color_cod": 8, "colorbar": [3, 8], "colsample_bytre": 12, "colsaobject": 12, "column": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 18, 23], "columntransform": 11, "com": [0, 1, 6, 8, 17, 22, 23], "combin": [3, 4, 7, 8, 9, 12, 20], "come": [0, 2, 3, 5, 6, 7, 14, 15, 16, 23], "command": [0, 2, 3], "comment": [2, 6, 7, 8], "commerci": [2, 17, 23], "commit": 0, "commod": [2, 23], "common": [1, 2, 3, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "commonli": [2, 3, 6, 8, 9, 11, 15, 16], "commun": [2, 14], "commut": 5, "commutatitav": 5, "compact": [2, 3, 5, 7, 8, 9, 11, 13, 14, 15, 16, 23], "compair": 2, "compar": [2, 5, 6, 7, 8, 13, 15, 18, 23], "comparison": [4, 6, 15], "compat": 9, "compet": 2, "competit": 12, "compil": [2, 3, 5, 6, 15, 17, 18, 23], "complet": [0, 1, 2, 4, 5, 6, 11, 14, 23], "completenn": 14, "complex": [1, 3, 7, 10, 11, 13, 14, 15, 23], "complic": [2, 3, 11, 15, 23], "compon": [1, 2, 3, 5, 6, 7, 8, 9, 11, 16, 17, 23], "components_": 13, "compos": [11, 14, 15, 16, 17, 23], "compphys": [1, 2, 8, 17, 19, 21, 22, 23], "compress": [2, 23], "compris": 8, "compromis": 7, "compulsori": [17, 23], "comput": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23], "computation": [2, 5, 8, 11, 15, 20, 23], "computationalscienceuio": 23, "concaten": [4, 6, 8, 16], "concav": [3, 15], "concentr": [2, 12], "concept": [2, 4, 17, 23], "conceptu": [14, 15], "concern": [2, 3, 6, 9, 23], "concic": 23, "conclud": [2, 7, 15], "conclus": 3, "cond": 4, "conda": [2, 3, 17, 23], "condit": [2, 4, 6, 7, 8, 10, 11, 13, 15, 20, 23], "conduct": 17, "condwav": 4, "confid": [2, 7, 8, 9, 10, 23], "configur": 5, "confirm": [7, 14], "confus": [7, 8, 9, 12, 18], "confusion_matrix": 11, "congruenti": 20, "conjug": [6, 10], "conjugaci": 15, "conjunct": 5, "connect": [2, 3, 5, 6, 11, 13, 14, 15, 18, 23], "consequ": [7, 8, 10, 12, 14, 15], "conserv": [7, 16], "consid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "consider": [2, 3, 7, 15, 23], "consist": [2, 3, 4, 5, 6, 8, 14, 15, 20], "constant": [1, 2, 4, 6, 7, 8, 10, 14, 15, 20, 23], "constitu": [2, 23], "constitut": [4, 8], "constrain": [3, 5, 7, 9, 13], "constraint": [7, 8, 10, 15], "construct": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 20, 23], "contact": [2, 23], "contain": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "contemporari": 23, "content": [0, 3, 17, 18, 23], "context": [8, 12, 15], "contigu": 18, "continu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "contour": [11, 12, 15], "contourf": [10, 11, 12], "contrast": [3, 6, 11, 12, 14, 23], "contribut": [2, 5, 7, 15, 20, 23], "contributor": 2, "control": [0, 2, 3, 5, 11, 15, 17, 23], "conv": [5, 6], "conv2d": [5, 6], "conv2dtranspos": 6, "convei": 23, "conveni": [7, 8, 14, 15, 18, 23], "convent": 14, "converg": [3, 4, 6, 7, 10, 15, 16, 23], "convergencewarn": 23, "convert": [2, 3, 6, 7, 11, 13, 15, 18, 23], "converttomatrix": 6, "convex": [6, 7, 9], "convinc": 15, "convolut": [3, 6, 17, 23], "cool": [6, 11], "coolwarm": 8, "coordin": [7, 14, 16], "coorel": 2, "copi": [0, 2, 3, 16], "core": 12, "corel": 23, "coronari": 9, "corr": [2, 7, 9, 13], "correalt": [13, 17], "correct": [0, 2, 3, 4, 5, 6, 7, 9, 15, 18, 20, 23], "correctli": [3, 4, 8, 9, 12], "correl": [2, 3, 5, 7, 8, 9, 12, 14, 15, 17, 20, 23], "correlation_matrix": [2, 7, 9, 13], "correspond": [2, 5, 7, 8, 10, 11, 13, 14, 17, 18, 20, 23], "cortex": 14, "cosin": [5, 8], "cost": [1, 2, 4, 5, 7, 8, 9, 10, 11, 14, 15, 23], "cost_deep_grad": 4, "cost_funct": 4, "cost_function_deep": 4, "cost_function_deep_grad": 4, "cost_function_grad": 4, "cost_grad": 4, "cost_sum": 4, "costol": 15, "could": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "coulomb": [2, 23], "count": [0, 2, 11, 19, 20, 21, 23], "counterpart": 23, "countor": 15, "coupl": [6, 7, 8], "cours": [0, 1, 2, 3, 5, 7, 13, 21], "coursework": 0, "courvil": [22, 23], "cov": [7, 8, 13, 18, 20, 23], "cov_xi": [7, 13], "cov_xx": [7, 13], "cov_yi": [7, 13], "covari": [2, 9, 17, 18, 23], "covariance_matrix": [7, 13, 16], "cover": [2, 7, 17, 21, 22], "covert": [2, 23], "covxi": 20, "covxx": 20, "covxz": 20, "covyi": 20, "covyz": 20, "covzz": 20, "cpu": 3, "craft": 5, "creat": [0, 3, 5, 6, 7, 11, 12, 13, 14, 17, 23], "create_biases_and_weight": 3, "create_convolutional_neural_network_kera": 5, "create_neural_network_kera": 3, "create_x": [7, 13], "credit": [2, 9, 21, 23], "crim": 2, "crime": 2, "criteria": [2, 6, 11, 12, 16, 20, 23], "criterion": [11, 12, 15], "critic": 8, "cross": [0, 2, 3, 5, 9, 11, 12, 15, 17, 20, 23], "cross_entropi": 6, "cross_val_scor": 8, "cross_valid": [9, 12], "crossvalid": 8, "crucial": [3, 20], "cs231": 5, "csr_matrix": [18, 23], "csv": [2, 6, 8, 9, 11], "ctnk": 3, "cubic": 2, "cumbersom": 7, "cumsum": [12, 13, 23], "cumul": [9, 12, 20], "cumulative_heads_ratio": 12, "cup": 7, "current": [0, 1, 3, 4, 5, 6, 15, 16, 22], "curs": 2, "curv": [8, 9, 12, 14], "curvatur": 15, "custom": [8, 16], "custom_cmap": [11, 12], "custom_cmap2": [11, 12], "cutpoint": 11, "cv": [8, 9, 12], "cvxbook": 15, "cvxopt": [7, 10], "cycl": [3, 14], "d": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "d2_g_t": 4, "d_f": 15, "d_g_t": 4, "d_net_out": 4, "da": 5, "dagger": [7, 18], "dai": [3, 11, 17], "damp": 5, "darget": 11, "darkr": 20, "dat": [2, 23], "dat_id": [2, 8, 9, 11, 23], "data": [1, 4, 6, 7, 10, 12, 14, 15, 16, 18, 22], "data1": 16, "data2": 16, "data3": 16, "data4": 16, "data_id": [2, 8, 9, 11, 23], "data_indic": 3, "data_panda": 23, "data_path": [2, 8, 9, 11, 23], "databas": 3, "datafil": [2, 8, 9, 11, 23], "datafram": [2, 6, 7, 9, 11, 13, 23], "datapoint": [1, 3, 7, 8, 9, 13, 15], "datasci": [0, 1], "dataset": [1, 2, 6, 8, 9, 10, 11, 12, 13, 15, 16, 23], "datatyp": 6, "date": [0, 23], "daughter": 12, "david": 22, "dbh": 3, "dbo": 3, "dcomposit": 18, "ddot": 4, "dead": 3, "deadlin": 0, "deal": [2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 20, 23], "dealt": 2, "debt": 9, "debug": [2, 7, 8], "decad": [2, 5], "decai": [2, 15, 20, 23], "decemb": [21, 23], "decent": 12, "decid": [2, 4, 5, 7, 8, 11], "decim": [2, 23], "decis": [2, 3, 10, 13, 17, 22, 23], "decision_funct": 10, "decision_tre": 11, "decisiontreeclassifi": [11, 12], "decisiontreeregressor": [2, 11, 12], "declar": [2, 6, 18, 23], "decompos": [7, 8, 18], "decomposit": [2, 8, 14, 23], "decompost": 7, "deconvolut": 5, "decorrel": [12, 15], "decreas": [3, 4, 6, 7, 8, 12, 13, 15], "deduc": [2, 23], "deep": [5, 9, 14, 15, 17, 22], "deep_neural_network": 4, "deep_param": 4, "deep_tree_clf": [11, 12], "deep_tree_clf1": 11, "deep_tree_clf2": 11, "deepen": [7, 17, 23], "deeper": [2, 5, 6, 23], "deeplearningbook": [22, 23], "deer": 5, "def": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 20, 23], "def_covari": 20, "default": [2, 3, 4, 6, 8, 9, 18, 23], "default_tim": 6, "defect": 7, "defici": 7, "defin": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "definit": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "defint": 20, "degre": [0, 1, 5, 7, 8, 10, 11, 12, 13, 20, 23], "del": 3, "delet": [0, 8], "delimit": 6, "deliv": [0, 19, 23], "delta": [2, 4, 5, 8, 10, 14, 15, 16, 23], "delta_": [3, 18], "delta_0": 5, "delta_1": 5, "delta_2": 5, "delta_3": 5, "delta_4": 5, "delta_5": 5, "delta_h": [2, 3, 23], "delta_j": [5, 14], "delta_k": 14, "delta_l": [3, 5], "delta_momentum": 15, "delta_n": [2, 5, 23], "delug": 17, "delv": 2, "demand": 15, "demonstr": [2, 5, 7, 8, 9, 13, 14, 17, 23], "den": 6, "denomin": [3, 7], "denot": [3, 4, 8, 9, 15, 20], "dens": [3, 5, 6], "densiti": [2, 4, 8, 20], "depart": [21, 23], "depend": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 23], "depict": 20, "deploy": [2, 17, 23], "depth": [2, 5, 11, 12, 18], "deriv": [2, 3, 4, 8, 9, 10, 12, 13, 15, 17, 23], "derivati": 15, "derivative_fn": 15, "descend": [7, 11, 13], "descent": [2, 3, 5, 9, 10, 14, 23], "describ": [2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 18, 23], "descript": [2, 10, 11, 23], "design": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 23], "designmatrix": [2, 23], "desir": [2, 4, 6, 7, 15, 16, 23], "desktop": 0, "despit": [3, 14], "destroi": 18, "det": [7, 18], "detail": [2, 8, 13, 15, 16, 18], "detect": [5, 10, 14], "determin": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "determinist": [9, 15, 20], "dev": 3, "develop": [2, 5, 7, 10, 12, 13, 14, 17, 18, 23], "deviat": [2, 3, 4, 6, 7, 8, 20, 23], "devis": 14, "df": [6, 10, 13, 15, 23], "df1": 23, "di": 2, "diag": [7, 10], "diagnost": [3, 12], "diagon": [2, 7, 9, 15, 18, 20, 23], "diagonaliz": 7, "diagram": 12, "diagsvd": 8, "dice": [8, 20], "dict": [8, 10], "dictionari": 2, "did": [1, 2, 3, 7, 8, 9, 12, 13, 16, 23], "die": 3, "diff": 4, "diff1": 4, "diff2": 4, "diff_ag": 4, "diffeent": 10, "differ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "differenti": [1, 2, 5, 17, 18, 23], "difficult": [2, 3, 8, 12, 15, 20, 23], "difficulti": [2, 3, 15, 23], "diffonedim": 4, "digit": [2, 3, 5, 6, 8, 21, 23], "dilemma": 15, "dilut": 3, "dim": [6, 13, 16, 18], "dimens": [1, 2, 3, 4, 5, 6, 7, 10, 13, 16, 18, 23], "dimension": [2, 6, 7, 8, 11, 13, 15, 16, 17, 18, 23], "dimensionless": [2, 5, 23], "diment": 18, "dimnsion": 6, "diod": 5, "direct": [2, 3, 4, 6, 13, 14, 15, 16, 23], "directli": [3, 6, 7, 8, 20], "disadvantag": [2, 23], "disappear": [5, 8], "disc_loss": 6, "disc_tap": 6, "discard": [8, 13], "disciplin": [2, 5, 14], "disclaim": 20, "discord": 23, "discourag": [0, 15], "discov": [2, 23], "discover": 7, "discret": [3, 5, 7, 9, 15], "discrimin": [6, 9, 12, 13], "discriminator_loss": 6, "discriminator_loss_list": 6, "discriminator_model": 6, "discriminator_optim": 6, "discuss": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "diseas": 9, "disguis": 8, "disord": [3, 9], "displai": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "displaystyl": [2, 7, 23], "disregard": [2, 23], "dissimilar": [13, 16], "dist": 16, "distanc": [2, 10, 11, 13, 16, 20], "distance_list": 11, "distinct": [5, 9, 10, 11, 12, 16], "distinctli": 10, "distinguish": [2, 6, 9, 10, 20, 23], "distplot": 2, "distribut": [2, 3, 6, 8, 9, 12, 13, 15, 16, 17, 18, 23], "distrubut": [2, 17, 23], "dive": [2, 10, 18, 23], "diverg": [3, 15], "divid": [2, 3, 5, 7, 8, 9, 10, 11, 13, 14, 20, 23], "divis": [8, 10, 11, 15, 18, 20], "dna": 9, "dnn": [2, 3, 4, 6, 14, 23], "dnn1": 6, "dnn2_gru2": 6, "dnn_kera": 3, "dnn_model": 3, "dnn_numpi": 3, "dnn_scikit": [2, 3, 23], "do": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "doc": [0, 1, 2, 17, 19, 21, 22, 23], "document": [0, 6, 15], "doe": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20, 23], "doesn": [5, 11, 14, 23], "dog": [3, 5, 6], "domain": [7, 10, 15], "domin": [2, 23], "don": [0, 1, 2, 3, 5, 7, 8, 10, 13, 15, 17, 23], "done": [1, 2, 4, 5, 6, 7, 8, 11, 12, 13, 15, 18, 23], "dot": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "doubl": [1, 5, 6, 18, 23], "doubli": 3, "down": [2, 5, 8, 11, 13, 14, 15], "download": [0, 2, 3, 5, 7, 8, 18, 22, 23], "downsampl": 5, "dozen": 3, "dq": 8, "drag": 15, "dramat": 13, "drastic": 6, "draw": [6, 8, 12, 15], "drawback": [2, 3, 5, 15], "drawn": [3, 6, 8, 9, 13, 20, 23], "drive": [5, 6], "driven": 5, "drop": [2, 3, 7, 8, 13, 15, 20, 23], "dropna": [2, 8, 23], "dropout": 6, "dt": [4, 5, 15, 20], "dtype": [2, 3, 5, 6, 16, 18, 23], "dub": [2, 23], "due": [3, 4, 7, 8, 10, 12, 14, 15, 21, 23], "dummi": 2, "dure": [2, 3, 5, 6, 10, 11, 13, 17, 23], "dwell": 2, "dwh": 3, "dwo": 3, "dx": [4, 5, 10, 20], "dx_1": 20, "dx_1p": 8, "dx_2p": 8, "dx_mp": 8, "dx_n": 20, "dxp": 8, "dy": [3, 10, 20], "dynam": 6, "dz": 10, "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 21, 23], "e_": [2, 4, 23], "each": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "eapprox": [2, 23], "earli": [3, 15], "earlier": [2, 7, 9, 10, 11, 13, 14, 15, 23], "earthexplor": 8, "eas": [8, 11, 16], "easi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "easier": [0, 7, 8, 10, 11, 15, 20, 23], "easiest": 15, "easili": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "eastern": [21, 23], "ebind": [2, 23], "eblock": 11, "econometr": 23, "economi": 7, "ecosystem": [17, 23], "ect": 19, "edg": 5, "edgecolor": 8, "edu": 15, "educ": [2, 23], "eff": 20, "effect": [1, 3, 6, 12, 15, 20], "effic": 3, "effici": [2, 5, 12, 15, 17, 18, 20, 23], "efron": 8, "egrad": 15, "eig": [7, 13, 15, 18, 20, 23], "eigen": 20, "eigenpair": [7, 13], "eigenvalu": [2, 7, 10, 13, 15, 18, 23], "eigenvector": [7, 13, 15], "eight": [18, 23], "eigval": [18, 20, 23], "eigvalu": [13, 15], "eigvec": [18, 20, 23], "eigvector": [13, 15], "eir": [21, 23], "eispack": [18, 23], "either": [3, 7, 8, 9, 10, 11, 12, 13, 15, 20, 23], "eivind": 21, "eivinsto": 21, "ekstr\u00f8m": 6, "elabor": 20, "elarn": 5, "electr": [2, 5, 14, 23], "electron": 23, "eleg": 13, "element": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 22], "elementari": [12, 15, 18], "elementwis": [5, 15], "elementwise_grad": [4, 15], "elessar": 23, "elif": 16, "elim": 18, "elimin": [5, 10], "elin": [21, 23], "ellipsi": 1, "els": [1, 3, 5, 6, 9, 11, 14, 15, 18], "elu": 3, "elus": [2, 23], "email": [19, 21, 23], "embed": [2, 13], "embodi": 8, "emit": 20, "emner": 22, "emphas": [2, 12, 17, 23], "emphasi": [2, 17, 22, 23], "empir": [3, 13, 20], "emploi": [2, 3, 7, 8, 13, 15, 20, 23], "employ": 2, "empti": [0, 8, 12], "emul": 14, "en": [17, 22], "enabl": 13, "enbodi": 8, "encod": [2, 5, 7, 11, 13, 16, 23], "encompass": [2, 20], "encount": [0, 2, 3, 7, 9, 15, 20, 23], "encourag": 0, "end": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "endpoint": [5, 8], "energi": [2, 6, 8], "enforc": 14, "eng": 22, "engin": [2, 3, 5, 6, 17, 23], "enorm": 5, "enough": [2, 8, 15, 23], "ensembl": [3, 11, 23], "ensur": [2, 3, 4, 5, 7, 8, 13, 15, 20], "entail": 23, "enter": [7, 8], "enthought": [2, 17, 23], "entir": [3, 5, 9, 11, 17, 20, 23], "entiti": [11, 14, 18, 23], "entri": [2, 7, 10, 13, 14, 18, 23], "entropi": [3, 5, 9, 12, 15, 23], "enumer": [2, 3, 4, 5, 6, 8, 10, 23], "env": [20, 23], "environ": [4, 17, 23], "environemnt": 0, "eo": [2, 8], "eol": 2, "eosfit": 2, "epoch": [2, 3, 5, 6, 14, 15, 23], "epsilon": [2, 7, 8, 9, 15, 23], "epsilon_": [2, 23], "epsilon_0": [2, 23], "epsilon_1": [2, 23], "epsilon_2": [2, 23], "epsilon_i": [2, 23], "eq": [5, 15, 16, 18, 20], "eqnarrai": [5, 7, 8], "equal": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "equat": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "equilibrium": [4, 14], "equiv": [5, 15, 18, 20], "equival": [2, 3, 7, 9, 10, 13, 15, 17, 18, 23], "erf": 20, "eriador": 23, "err": [2, 12], "err_": 8, "err_sqr": 4, "errat": 15, "erron": 4, "error": [0, 1, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20], "error_estimate_corr_tim": 20, "error_hidden": 3, "error_output": 3, "escap": 15, "especi": [0, 3, 5, 11, 14, 15], "essenti": [0, 2, 7, 8, 11, 12, 14, 16, 20], "establish": [1, 2, 8, 12, 13], "estim": [2, 3, 7, 8, 9, 12, 13, 15, 17, 20, 23], "estimated_mse_fold": 8, "estimated_mse_kfold": 8, "estimated_mse_sklearn": 8, "et": [1, 2, 4, 6, 22, 23], "eta": [2, 3, 5, 10, 14, 15, 23], "eta0": [10, 15], "eta_": 15, "eta_t": 15, "eta_v": [2, 3, 5, 23], "etc": [2, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20], "ethic": 17, "euclidean": [2, 16], "evalu": [0, 1, 2, 4, 5, 6, 7, 8, 11, 15, 20, 23], "evalut": 15, "even": [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "evenli": 6, "event": [7, 9, 12, 20], "eventu": [2, 7, 8, 13, 14, 15, 21], "everi": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23], "everyth": [1, 6, 14], "everywher": [6, 15], "evolv": 2, "exact": [2, 7, 13, 14, 15, 18, 20, 23], "exactli": [2, 5, 6, 8, 14, 17], "exam": 23, "examin": 8, "exampl": [0, 1, 7, 13, 14, 15, 17, 18, 20, 22], "exce": [3, 14, 15], "excel": [2, 3, 6, 7, 12, 23], "except": [5, 6, 8, 10, 11, 18], "excess": [2, 23], "excit": 2, "exclud": [3, 8, 14], "exclus": [2, 3, 5, 8, 20, 23], "execut": [0, 4, 7, 15], "exemplifi": 15, "exercic": [21, 23], "exercis": [7, 17, 19, 21, 23], "exhaust": 8, "exhibit": [2, 7, 8, 10, 23], "exist": [2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 18, 23], "exit": [7, 18], "exp": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 20], "exp_term": 3, "expand": [7, 9, 13, 15], "expans": [2, 5, 7, 10, 12, 14, 15, 23], "expect": [0, 2, 3, 7, 8, 9, 13, 14, 15, 17, 23], "expectation_value_of_h_wrt_p": 20, "expens": [1, 8, 12, 15], "experi": [0, 2, 3, 8, 10, 15, 17, 23], "experiment": [2, 6, 8, 11, 20, 23], "expert": [3, 11], "explain": [1, 2, 8, 11, 12, 13, 15, 23], "explained_variance_ratio_": 13, "explanatori": [2, 23], "explicit": [2, 5, 8, 15, 18, 23], "explicitli": [2, 6], "explod": 3, "exploit": [2, 5, 14, 15, 23], "explor": [3, 6, 8, 10, 15, 17, 23], "expon": 3, "exponenti": [2, 3, 7, 8, 12, 15, 20, 23], "export": [0, 1, 11], "export_graphviz": 11, "export_text": 11, "exporttext": 11, "expos": 17, "express": [2, 4, 5, 7, 8, 9, 12, 14, 15, 18, 20, 23], "exptmean": 20, "exptvari": 20, "extend": [2, 4, 9, 13, 15, 17, 23], "extens": [0, 2, 14, 17, 23], "extent": [2, 3, 8, 22], "extern": [5, 8, 11], "extra": [0, 3, 5, 7, 21, 23], "extract": [1, 2, 5, 7, 8, 9, 10, 13, 15, 18, 23], "extrapol": [2, 23], "extrem": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 18], "extremum": 15, "extrins": 13, "ey": [2, 7, 8, 15, 16, 18, 23], "f": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 23], "f1": 15, "f11": [2, 23], "f12": [2, 23], "f13": [2, 23], "f1_grad": 15, "f1d": 15, "f2": 15, "f2_grad_x1": 15, "f2_grad_x1_analyt": 15, "f2_grad_x2": 15, "f2_grad_x2_analyt": 15, "f3": 15, "f3_grad": 15, "f3_grad_analyt": 15, "f4": 15, "f4_grad": 15, "f4_grad_analyt": 15, "f5": 15, "f5_grad": 15, "f6": 15, "f6_for": 15, "f6_for_grad": 15, "f6_grad_analyt": 15, "f6_while": 15, "f6_while_grad": 15, "f7": 15, "f7_grad": 15, "f7_grad_analyt": 15, "f8": 15, "f8_grad": 15, "f9": [2, 15, 23], "f9_altern": 15, "f9_alternative_grad": 15, "f9_grad": 15, "f_": 12, "f_0": [5, 12], "f_1": [12, 15], "f_2": [14, 15], "f_3": 14, "f_d": 20, "f_grad": 15, "f_grad_analyt": 15, "f_i": [1, 2, 8, 14], "f_m": [5, 12], "f_n": 5, "f_vec": 4, "face": [15, 23], "facecolor": [8, 10, 20], "facil": [2, 17], "facilit": 14, "fact": [2, 3, 5, 7, 11, 13, 14, 15, 23], "factor": [2, 3, 5, 7, 8, 11, 12, 13, 15, 18, 20, 23], "factori": 15, "fade": 8, "fafab0": [11, 12], "fail": [2, 8, 15, 21, 23], "failur": 9, "fairli": [3, 4, 20], "faisal": 1, "fake": 6, "fake_loss": 6, "fake_output": 6, "fall": [10, 11, 19], "fals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 16, 18, 23], "famili": [2, 9, 10, 20], "familiar": [0, 2, 5, 7, 8, 10, 17, 18, 20, 23], "famou": [8, 14], "far": [1, 2, 5, 6, 7, 8, 10, 13, 14, 15, 16, 23], "fashion": [2, 11, 12, 23], "fast": [3, 5, 8, 12, 14, 15, 17, 20, 23], "faster": [3, 13, 15], "fastest": [15, 18], "favor": 9, "favorit": 20, "fc": 5, "featur": [0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "feature_nam": [2, 3, 9, 11], "feautur": 11, "fed": 3, "feed": [2, 4, 5, 13, 17, 23], "feed_forward": 3, "feed_forward_out": 3, "feed_forward_train": 3, "feedback": [6, 23], "feeddorward": 6, "feedforward": [3, 6, 14], "feel": [0, 1, 2, 7, 8, 13, 15, 17, 21, 23], "feet": 2, "fetch": [0, 8], "few": [3, 5, 6, 7, 11, 20, 23], "fewer": [2, 11, 13, 23], "ffnn": [3, 14], "field": [2, 5, 8, 14, 17], "fifth": [2, 8, 23], "fig": [2, 3, 4, 5, 6, 8, 9, 14, 15, 16, 23], "fig_id": [2, 8, 9, 11, 23], "figaxi": 20, "figsiz": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "figur": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 23], "figure_id": [2, 8, 9, 11, 23], "figurefil": [2, 8, 9, 11, 23], "file": [0, 2, 6, 7, 8, 9, 11, 23], "file_prefix": 6, "filenam": 23, "fill": [7, 11], "filter": [5, 6], "final": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 23], "financ": 2, "find": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "fine": [2, 16], "finish": 4, "finit": [5, 7, 8, 14, 15, 20], "finnicki": 0, "first": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 22], "firsteigvector": 13, "fit": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20], "fit_intercept": [1, 2, 7, 8], "fit_mod": 11, "fit_theta": 8, "fit_transform": [0, 2, 8, 10, 11, 13], "fiti": [2, 23], "five": [2, 11, 23], "fix": [2, 5, 6, 8, 12, 13, 14, 15, 23], "flag": 6, "flat": [14, 15], "flatten": [3, 5, 6, 7, 18], "flexibl": [3, 8, 10, 12, 14, 23], "flip": [21, 23], "float": [2, 5, 6, 7, 11, 13, 15, 16, 18, 23], "float32": [6, 11], "float64": [6, 18, 23], "flop": [7, 18], "flow": [3, 6, 14], "fluctuat": 7, "fly": 13, "fm": 2, "fmax": 5, "fmesh": 15, "fn": 9, "focu": [0, 2, 5, 6, 7, 8, 17, 22, 23], "focus": [3, 8, 9, 18], "fold": [8, 11], "folder": [0, 2, 6, 8, 23], "follow": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "font": [9, 20, 23], "fontdict": 20, "fontsiz": [3, 8, 10, 11, 12, 20], "fontweight": 3, "footprint": 5, "foral": 10, "forc": [2, 7, 8, 12, 13], "forcast": 6, "forecast": [6, 14], "forest": [2, 3, 11, 17, 23], "forget": 13, "form": [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "formal": [5, 6, 16, 20], "format": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 17, 20, 22], "format_data": 6, "formatstrformatt": [8, 15], "formul": [6, 8, 13, 16], "formula": [5, 15, 20], "forth": [6, 14], "fortran": [2, 17, 18, 23], "fortran2003": [17, 23], "fortran90": 20, "fortun": [2, 13], "forward": [2, 5, 8, 17, 18, 23], "found": [3, 4, 6, 7, 8, 14, 15, 23], "foundat": [17, 23], "four": [6, 7, 8, 10, 14, 18, 19, 21, 23], "fourier": [2, 23], "fourierdef1": 5, "fourierdef2": 5, "fourierseriessign": 5, "fourth": [14, 23], "fp": 9, "frac": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "fraction": 11, "frame": 9, "framework": [3, 10, 12, 20], "frank": [7, 13], "frankefunct": [7, 8, 13], "fredli": [21, 23], "free": [0, 1, 2, 8, 13, 15, 17, 18, 20, 21, 22, 23], "freecodecamp": 17, "freedom": 7, "freeli": 2, "freez": 0, "frequenc": [5, 8, 9, 20], "frequent": [2, 10, 11, 15], "frequentist": 17, "fresh": 12, "fridai": [0, 21, 23], "friedman": [8, 22, 23], "friendli": 6, "frodo": 23, "frog": 5, "from": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 22], "from_cod": 11, "from_logit": [5, 6], "from_tensor_slic": 6, "front": [2, 6, 7, 23], "frustrat": 0, "fulfil": [4, 7, 14], "full": [2, 3, 5, 7, 9, 11, 12, 15, 20, 23], "full_matric": 7, "fulli": [5, 8, 14, 20], "fun": [17, 23], "func": 4, "function": [0, 1, 4, 5, 6, 7, 11, 16, 17, 18], "functionali": 13, "fundament": [2, 8, 17, 23], "funtion": 4, "further": [4, 9, 11, 23], "furthermor": [2, 5, 7, 8, 9, 13, 14, 15, 17, 23], "futur": [2, 6, 10, 11, 23], "fy": [0, 19, 21, 22, 23], "fys5419": [22, 23], "fys5429": [22, 23], "f\u00f8470": [21, 23], "g": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 20, 23], "g0": 4, "g_": [4, 11, 12], "g_0": 4, "g_1": [4, 12], "g_2": [4, 12], "g_analyt": 4, "g_dnn_ag": 4, "g_euler": 4, "g_i": 4, "g_m": [5, 12], "g_n": 5, "g_re": 4, "g_t": 4, "g_t_d2t": 4, "g_t_d2x": 4, "g_t_dt": 4, "g_t_hessian": 4, "g_t_hessian_func": 4, "g_t_jacobian": 4, "g_t_jacobian_func": 4, "g_trial": 4, "g_trial_deep": 4, "g_vec": 4, "gain": [3, 7, 9, 11, 12, 15], "galleri": [2, 23], "game": 6, "gamge": 23, "gamma": [2, 4, 10, 11, 12, 13, 15, 23], "gamma1": 10, "gamma2": 10, "gamma_": [2, 23], "gamma_0": 12, "gamma_1": 12, "gamma_1x": 12, "gamma_i": [2, 10, 20, 23], "gamma_j": 15, "gamma_k": 15, "gamma_m": 12, "gamma_x": [2, 23], "gap": 10, "gate": [6, 14], "gather": [2, 3, 14], "gaug": 14, "gaussbacksub": 18, "gaussian": [6, 7, 8, 10, 16, 20, 23], "gaussian_point": 16, "gaussian_rbf": 10, "gave": 15, "gavra": 23, "gbc": 23, "gca": [4, 8, 10, 15], "gd": 3, "gd_clf": 12, "gdclassiffiercgain": 12, "gdclassiffierconfus": 12, "gdclassiffierroc": 12, "gdm": 15, "gdregress": 12, "ge": [3, 7, 9, 20], "gen_loss": 6, "gen_tap": 6, "gender": [2, 23], "genener": 6, "gener": [0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22], "generaliz": 1, "generallay": 14, "generate_and_save_imag": 6, "generate_imag": 6, "generate_latent_point": 6, "generate_simple_clustering_dataset": 16, "generated_imag": 6, "generator_loss": 6, "generator_loss_list": 6, "generator_model": 6, "generator_optim": 6, "genom": 17, "geodes": 13, "geometr": [2, 15, 23], "geometri": 7, "georg": 22, "geotif": 8, "geq": [4, 7, 10, 11, 15], "geron": [2, 22, 23], "get": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20, 21, 23], "get_dummi": 11, "get_paramet": 4, "get_split": 11, "get_yaxi": 10, "get_yticklabel": 8, "gh": 0, "gibb": [17, 23], "gif": 6, "gini": 12, "gini_index": 11, "ginvers": 15, "git": [0, 2, 17, 23], "giter": 15, "github": [2, 17, 19, 21, 22, 23], "gitignor": 0, "gitlab": [2, 17, 23], "give": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 23], "given": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "global": [8, 9, 15], "glorot": 3, "gnew": 15, "go": [0, 1, 2, 3, 5, 7, 8, 10, 11, 13, 14, 15, 23], "goal": [2, 9, 11, 23], "goe": [0, 2, 3, 4, 7, 8, 15, 16, 18, 23], "golden": 15, "gone": 7, "gong": 3, "good": [0, 3, 5, 6, 7, 8, 11, 12, 13, 15, 17, 20, 22], "goodfellow": [6, 22, 23], "googl": [3, 6, 17, 23], "got": [3, 8], "gotten": 23, "gov": 8, "govern": 23, "gp": 22, "gpu": [3, 15, 17, 23], "grad": [4, 15], "grad_analyt": 15, "grade": 19, "gradient": [2, 5, 6, 9, 10, 11, 14, 17, 23], "gradientboostingclassifi": 12, "gradientboostingregressor": 12, "gradients_of_discrimin": 6, "gradients_of_gener": 6, "gradienttap": 6, "gradual": [3, 16], "grai": [6, 8], "graph": [1, 3, 11, 13, 14, 15], "graph_from_dot_data": 11, "graphic": [2, 3, 11, 23], "grasp": 2, "gray_r": [3, 5], "grayscal": 5, "great": [0, 7, 15], "greater": [3, 9, 20], "greatli": 15, "greedi": 11, "green": [2, 5, 11, 20], "grei": 6, "grid": [3, 5, 8, 9, 10, 14, 20], "grossli": 15, "ground": [2, 23], "group": [0, 2, 8, 9, 11, 16, 17, 19, 21, 23], "groupbi": [2, 23], "grow": [3, 5, 11, 12], "growth": [2, 23], "gru": 6, "guarante": [2, 6, 15, 20, 23], "guess": [3, 6, 12, 15, 16], "guestrin": 12, "guid": 3, "h": [0, 2, 3, 7, 8, 10, 15, 20, 21, 22, 23], "h1": 4, "h_": [2, 15, 23], "h_1": [4, 15], "h_2": [4, 15], "h_m": 12, "ha": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "haanen": [21, 23], "habit": 2, "had": [2, 3, 8, 9, 15, 23], "hadamard": [3, 14, 15], "half": [3, 10, 11], "halv": 12, "hand": [2, 3, 4, 5, 7, 13, 14, 15, 17, 18, 20, 21, 22, 23], "handi": 5, "handl": [0, 2, 3, 4, 7, 11, 13, 17], "handle_unknown": 11, "handsid": 14, "handwrit": 14, "handwritten": [3, 7], "happen": [3, 4, 5, 6, 7, 8, 12, 15, 20], "hard": [3, 9, 10, 12, 15], "hardcopi": [17, 23], "harder": [2, 3], "harmon": 5, "hasn": 23, "hassl": [2, 17, 23], "hast": [17, 23], "hasti": [1, 2, 8, 22, 23], "hat": [1, 2, 3, 7, 8, 9, 11, 12, 13, 14, 15, 18], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "haven": 3, "he": [9, 23], "head": [2, 6, 12, 20], "header": [2, 23], "heads_proba": 12, "health": 2, "hear": [2, 15, 23], "heart": [2, 9, 23], "heatmap": [2, 3, 5, 9, 23], "heavili": 2, "heavisid": 3, "height": [3, 5, 8], "held": 15, "help": [0, 1, 2, 3, 6, 14, 15, 23], "helper": [6, 16], "henc": [2, 7, 8, 10, 11, 12, 14, 15, 23], "henrik": [21, 23], "her": 9, "here": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hereaft": [2, 10, 14, 23], "hermitian": 18, "hessenberg": 18, "hessian": [2, 4, 7, 15], "heterogen": [11, 12], "hi": 9, "hidden": [3, 5, 6, 14], "hidden_bia": 3, "hidden_bias_gradi": 3, "hidden_layer_s": [2, 3, 23], "hidden_neuron": 6, "hidden_weight": 3, "hidden_weights_gradi": 3, "hierarch": 7, "high": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 23], "higher": [2, 3, 5, 7, 8, 10, 15, 23], "highest": [3, 4], "highli": [2, 5, 6, 12, 17, 18, 22, 23], "highwai": 2, "hing": 10, "hint": [0, 1, 15], "hip": 17, "hire": 2, "hist": [6, 8, 9, 20], "histogram": [2, 8, 9, 20], "histor": [9, 13], "histori": [0, 5, 6, 14], "hitherto": 7, "hjorth": [21, 23], "hobbi": 20, "hoc": 7, "hoff": 22, "hold": [3, 5, 8, 15, 16], "holder": [2, 23], "home": 2, "homepag": 23, "homework": [8, 15], "homogen": [3, 5, 11, 12, 15], "honchar": 4, "hopefulli": [0, 2, 13, 20, 23], "horizont": 13, "horlyk": [21, 23], "hors": [5, 9, 23], "hot": [3, 11], "hour": [3, 17, 19, 20, 21, 23], "how": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "howev": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hspace": [2, 6, 10, 12, 20, 23], "hstack": 3, "htf": 23, "html": [1, 2, 17, 19, 21, 22, 23], "http": [0, 1, 2, 5, 6, 8, 15, 17, 18, 19, 21, 22, 23], "huang": [2, 23], "huber": [2, 23], "huge": [3, 5, 6, 17], "human": [2, 3, 5, 8, 11, 14], "humid": 11, "hundr": 3, "hungri": 3, "hybrid": 19, "hydrogen": [2, 23], "hyperbol": [3, 6, 14], "hyperparam": 10, "hyperparamet": [5, 6, 7, 8, 11, 15], "hyperplan": 13, "h\u00f8rlyk": [21, 23], "i": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "i0": [2, 23], "i1": [2, 8, 10, 14, 23], "i2": [2, 10, 14, 23], "i3": [2, 14, 23], "i5": [2, 23], "i_": 15, "i_1": [7, 8], "i_2": [7, 8], "ian": 22, "ic": 3, "id": [9, 15], "ida": [21, 23], "idea": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 18], "ideal": [2, 4, 8, 10, 15, 20, 23], "idem": 8, "ident": [7, 8, 14, 15, 18], "identifi": [2, 3, 9, 11, 13, 14, 15, 16, 23], "ieor": 20, "ifi": 22, "ifs": [17, 23], "ignor": [0, 2, 3, 5, 11], "ii": [18, 20], "iii": [18, 23], "ij": [1, 2, 3, 5, 8, 10, 14, 16, 18, 20, 23], "ik": [2, 18, 23], "illustr": [7, 9, 12, 14, 15, 16, 17, 23], "im": 8, "imag": [3, 5, 6, 8, 11, 13, 14, 16, 22, 23], "image_at_epoch_": 6, "image_batch": 6, "image_height": 5, "image_path": [2, 8, 9, 11, 23], "image_width": 5, "imageio": 8, "images_from_seed_imag": 6, "imagin": 3, "immedi": [2, 5, 6, 8, 17, 23], "implement": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 20, 23], "impli": [5, 7, 8, 9, 15, 18], "implicit": 5, "implicitli": [13, 20], "import": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "importantli": 5, "impos": [2, 8, 13, 14, 23], "imposs": [2, 7, 23], "impress": [2, 14, 23], "improv": [0, 2, 6, 7, 11, 12, 13, 15], "impur": 11, "imread": 8, "imshow": [3, 5, 6, 8], "in3050": [22, 23], "in3310": 23, "in4080": [22, 23], "in4300": [22, 23], "in4310": 22, "in5400": 5, "in5550": 22, "in_out_neuron": 6, "inaccur": 15, "inact": 14, "inadequ": [2, 23], "inch": 8, "includ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 17, 20, 21, 22, 23], "include_bia": [8, 11], "incom": [1, 14], "incorrect": 3, "incoveni": 10, "increas": [2, 3, 5, 6, 7, 8, 11, 14, 15, 20, 23], "increasingli": 20, "ind": 8, "inde": [2, 4, 6, 7, 8, 15, 23], "indefinit": 6, "independ": [2, 7, 8, 9, 10, 14, 15, 20, 23], "index": [2, 3, 5, 6, 12, 16, 17, 18, 20, 22, 23], "index_col": [2, 23], "indic": [1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 23], "indispens": 8, "individu": [3, 8, 9, 12, 14, 20, 23], "indu": 2, "indx": 18, "indx1": 4, "indx2": 4, "indx3": 4, "ineffici": [5, 15], "inequ": [10, 15], "inertia": 15, "inf1000": [17, 23], "inf1100": [17, 23], "inf1100l": [17, 23], "inf1110": [17, 23], "inf3000": 23, "infeas": 11, "infer": [2, 3, 6, 8, 22, 23], "inferenc": 3, "infil": [2, 8, 9, 11, 23], "infin": [7, 8, 9, 13], "infinit": 5, "infinitesim": 20, "influenc": [8, 12], "influenti": 3, "info": 23, "inform": [2, 3, 5, 6, 8, 11, 13, 14, 15, 16, 18, 22, 23], "inforom": 0, "infti": [5, 8, 15, 20], "ingeni": 15, "ingredi": [2, 11, 23], "inher": 8, "inherit": [18, 23], "initi": [2, 3, 4, 8, 12, 15, 16, 18, 20, 23], "inject": 16, "inlin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "inner": [2, 15], "inp": 6, "inplac": 15, "input": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 20, 23], "input_dim": 3, "input_shap": [5, 6], "inputs": 3, "inputs_shuffl": [2, 3], "insert": [5, 7, 8, 10, 12, 20], "insid": [2, 6, 9], "insight": [2, 3, 7, 17, 23], "insist": [8, 15], "inspir": [2, 3, 14, 23], "instabl": 4, "instal": [0, 2, 3, 7, 8, 11], "instanc": [1, 2, 3, 4, 6, 8, 11, 13, 15, 23], "instanti": 12, "instead": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 20, 23], "institut": 3, "instruct": [2, 3], "int": [2, 3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 20], "int32": 12, "int_": [5, 8, 20], "int_0": 20, "int_a": 20, "intak": 2, "integ": [3, 4, 15, 16, 18, 20, 23], "integer_vector": 3, "integr": [5, 8, 20, 23], "intellig": [2, 16, 22, 23], "intend": 12, "intens": 3, "intention": 16, "interact": [2, 8, 11, 14, 17, 23], "intercept": [1, 2, 8, 10, 13, 15, 23], "intercept_": [2, 8, 10, 11, 15, 23], "interchang": [7, 14, 18], "interconnect": 3, "interest": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 20, 23], "interfac": [2, 3, 18], "interior": [2, 11, 23], "intermedi": 18, "intern": [3, 12, 14], "interpol": [3, 5, 6, 8, 14], "interpr": 7, "interpret": [0, 1, 2, 3, 8, 11, 12, 14, 15, 18, 20], "interv": [2, 5, 7, 8, 9, 15, 20, 23], "intial": 15, "intract": [2, 6], "intrins": [5, 13, 18, 20, 23], "intro": [17, 22, 23], "introduc": [2, 3, 7, 8, 10, 12, 14, 18, 20, 23], "introduct": [3, 4, 6, 15, 22], "introductori": [2, 6, 18, 22, 23], "intuit": [2, 7, 8, 10, 14, 15, 23], "inv": [2, 7, 15, 23], "invalu": [2, 15, 17, 23], "invari": 3, "invd": 7, "inver": 10, "invers": [2, 5, 8, 15, 23], "inverse_transform": 10, "invert": [1, 2, 7, 9, 12, 15, 23], "invh": 15, "invok": [2, 10], "involv": [2, 4, 8, 9, 13, 14, 23], "io": [2, 17, 19, 21, 22, 23], "ip": [2, 10, 20, 23], "ipca": 13, "ipynb": [17, 23], "ipython": [2, 7, 9, 11, 13, 16, 17, 23], "iq": 8, "iri": [10, 11], "irreduc": 8, "irrelev": 7, "irrespect": [2, 23], "isn": 7, "isnul": 2, "isomap": 13, "issu": [0, 3, 11, 18], "it_arrai": 15, "item": [2, 15, 23], "items": [18, 23], "iter": [3, 4, 6, 8, 10, 15, 16, 20, 23], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "itself": [7, 8, 14, 20, 23], "j": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 22, 23], "j1": 18, "j_": 8, "j_lasso_sk": 8, "j_ridge_sk": 8, "j_sk": 8, "jackknif": [8, 17, 23], "jacobian": [4, 15], "jason": 6, "jax": [17, 23], "jensen": [21, 23], "jerom": 22, "ji": [14, 18], "jit": 15, "jj": [2, 7, 8, 23], "jk": [2, 3, 8, 14, 18, 23], "jl": [2, 23], "jm": 18, "jnp": 15, "job": [0, 4, 10, 12], "join": [2, 6, 8, 9, 11, 23], "joint": [6, 7], "judg": 15, "judgement": 8, "julia": [17, 18], "jump": 20, "junk": 6, "jupit": 23, "jupyt": [0, 1, 2, 17, 22, 23], "just": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "justif": 2, "justifi": [5, 12], "k": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "k0": 9, "k1": 9, "kaggl": 8, "kappa_d": 20, "karl": [21, 23], "karush": 10, "katrin": [21, 23], "keep": [0, 2, 3, 6, 7, 8, 13, 15, 16, 18, 23], "keepdim": [3, 8, 12, 18], "kei": [2, 3, 5, 8, 14], "kept": [6, 8, 16], "kera": [2, 6, 17, 23], "kernel": [2, 3, 5, 17, 23], "kernel_regular": [3, 5], "kernel_s": 6, "kernelpca": 13, "kev": [2, 23], "kevin": [22, 23], "keyword": [18, 23], "kfold": 8, "kg": 3, "ki": 18, "kick": [3, 15], "kiener": 4, "kilomet": 8, "kind": [2, 4, 5, 6, 10, 14, 15, 16, 23], "kj": [8, 14, 18], "kjm": [17, 23], "kkt": 10, "kl": 20, "km": [14, 23], "kmean": 16, "kmeanspoint": 16, "kn_k": 16, "know": [0, 1, 2, 3, 4, 7, 8, 10, 15, 17, 23], "knowledg": [2, 17, 23], "known": [3, 5, 6, 7, 8, 9, 10, 11, 14, 18, 20, 22], "kondev": [2, 23], "kp": 20, "kpca": 13, "kroneck": 16, "kuhn": 10, "kvalsund": [21, 23], "kwown": [2, 23], "l": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "l0": 9, "l1": [2, 3, 5, 9, 23], "l1_l2": [3, 5], "l1regl": 7, "l2": [3, 5], "l_": 18, "l_1": 9, "l_2": [9, 15], "l_j": 14, "la": 15, "la_i": 14, "la_k": 14, "lab": [17, 23], "label": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 23], "labelencod": [9, 12], "labels": [8, 10, 11], "labels_shuffl": [2, 3], "laboratori": 19, "lack": [2, 23], "lagari": 4, "lagrang": [10, 13], "lambda": [2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 20, 23], "lambda_": 13, "lambda_0": 13, "lambda_1": [7, 10, 13], "lambda_2": [10, 13], "lambda_i": [10, 13], "lambda_iy_i": 10, "lambda_jy_iy_j": 10, "lambda_k": 10, "lambda_n": [7, 10], "lamda": 3, "land": [2, 10], "landmark": 10, "landscap": 15, "langl": [2, 8, 13, 20, 23], "languag": [2, 3, 6, 10, 17, 18, 22, 23], "lapack": [18, 23], "laplac": 7, "laptop": [0, 17], "larg": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 22, 23], "larger": [2, 5, 7, 8, 10, 12, 13, 15, 20, 23], "largest": [6, 10, 13], "lasso": [2, 9, 17, 23], "lasso_sk": 8, "last": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 18, 20, 21, 23], "latent": 6, "latent_dim": 6, "latent_point": 6, "latent_space_value_rang": 6, "later": [0, 2, 3, 6, 9, 10, 14, 15, 16, 17, 23], "latest": [0, 6, 17], "latest_checkpoint": 6, "latex": 23, "latter": [2, 5, 8, 9, 10, 13, 15, 18, 20, 23], "lattic": 14, "law": 2, "layer": [2, 6, 15, 23], "lbfg": [9, 11, 12], "lcc": [7, 8], "lda": 13, "ldot": [2, 8, 13, 23], "le": [7, 9, 12, 15, 20], "lead": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "leaf": 11, "leaki": 3, "leakyrelu": 6, "lear": 15, "learn": [5, 6, 7, 8, 9, 10, 11, 12, 14, 18, 21, 22], "learnabl": 5, "learner": 12, "learnig": 23, "learning_r": [10, 12], "learning_rate_init": [2, 3, 23], "learning_schedul": 15, "least": [2, 9, 10, 12, 13, 17, 18, 20], "leat": 15, "leav": [2, 3, 5, 7, 8, 11, 13, 23], "lectur": [2, 3, 7, 12, 13, 14, 15, 17, 18, 19, 21, 22], "lecturenot": [2, 17, 22, 23], "left": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "leftarrow": [10, 14], "legend": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 23], "leinonen": 23, "len": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 18, 23], "length": [1, 2, 3, 5, 6, 10, 11, 15, 17, 23], "length_of_sequ": 6, "leq": [2, 7, 9, 10, 15, 16, 20, 23], "less": [2, 3, 5, 6, 7, 8, 10, 11, 15, 17, 20, 23], "lessen": 3, "let": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "letter": [1, 2, 18, 20, 23], "level": [2, 3, 7, 8, 11, 17, 18, 19, 21, 23], "li": [10, 13, 23], "lib": 23, "liblinear": 12, "librari": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 18, 20, 22], "licens": [2, 3, 17, 23], "lie": [2, 8, 13, 20, 23], "life": [2, 3, 10, 14, 23], "lifetim": 15, "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23], "likelihood": [2, 3, 7, 11, 23], "lim_": 20, "limit": [2, 7, 8, 10, 14, 18, 23], "lin_clf": 10, "lin_model": 2, "lin_reg": 11, "linalg": [2, 4, 7, 8, 10, 13, 15, 18, 20, 23], "line": [0, 1, 2, 5, 8, 10, 13, 15, 23], "line1": 10, "line2": 10, "line3": 10, "line_model": 0, "line_ms": 0, "line_predict": 0, "linear": [1, 3, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20], "linear_model": [0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 15, 23], "linear_regress": 8, "linearli": 7, "linearloc": [8, 15], "linearregress": [0, 1, 2, 8, 9, 11, 23], "linearsvc": 10, "liner": [3, 5], "linerar": 12, "linewidth": [2, 4, 6, 8, 10, 11, 12], "link": [0, 2, 6, 11, 14, 17, 19, 21, 23], "linlag": 7, "linpack": [18, 23], "linreg": [2, 23], "linspac": [1, 2, 4, 5, 6, 8, 10, 11, 12, 15, 18, 20, 23], "linu": 6, "linux": [2, 3, 17, 23], "liquid": [2, 23], "list": [0, 2, 3, 4, 5, 6, 11, 17, 23], "listedcolormap": [11, 12], "literatur": [3, 9, 16, 22], "littl": [3, 5, 11, 14], "live": [1, 10], "ll": [2, 20, 23], "lle": 2, "lloyd": [6, 16], "lmb": [2, 4, 7, 8], "lmbd": [2, 3, 5, 23], "lmbd_val": [2, 3, 5, 23], "lmbda": 15, "ln": [3, 15], "load": [2, 3, 6, 8, 9, 11, 12], "load_boston": 2, "load_breast_canc": [3, 9, 11, 12, 13], "load_data": [5, 6], "load_digit": [3, 5], "load_iri": [10, 11], "loc": [5, 8, 9, 10, 11, 12, 23], "local": [0, 2, 3, 5, 9, 14, 15], "locat": [0, 4, 5, 10], "log": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 18, 23], "log10": [2, 7, 8], "log_": [2, 23], "log_clf": 12, "logarithm": [2, 7, 9, 18, 23], "logic": [2, 3, 11, 23], "login": 0, "logist": [2, 3, 4, 10, 11, 12, 13, 14, 15, 17], "logisticregress": [9, 11, 12, 13], "logit": 9, "logreg": [9, 11, 12, 13], "logspac": [2, 3, 5, 7, 8, 23], "long": [2, 3, 5, 6, 14, 15, 23], "longer": [4, 5, 10, 12, 16, 18, 20, 23], "loocv": 8, "look": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "loop": [1, 3, 6, 8, 12, 14, 16, 17, 18, 23], "lose": 3, "loss": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 18, 23], "loss_fil": 6, "lossfil": 6, "lost": 6, "lot": [1, 2, 3, 6, 8], "low": [2, 8, 11, 12, 13, 23], "lower": [1, 2, 3, 5, 8, 11, 12, 18], "lowercas": [18, 23], "lowest": [11, 15, 20], "lr": [3, 5, 6, 12], "lstat": 2, "lstm": 6, "lstm_2layer": 6, "lstsq": [2, 23], "lt": 8, "lu": [2, 7, 23], "lubksb": 18, "luckili": 4, "ludcmp": 18, "lux": 18, "lvert": 3, "lw": [2, 23], "m": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23], "m_": [11, 14], "m_1": 16, "m_h": [2, 23], "m_k": 16, "m_l": 14, "m_n": [2, 23], "m_p": [2, 23], "m_t": 15, "ma": 13, "machin": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 22], "machinelearn": [1, 2, 8, 17, 19, 21, 22, 23], "mackai": 22, "made": [2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 23], "mae": [2, 23], "magic": 6, "magnitud": [3, 8, 9, 15], "mai": [2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "mail": [19, 21], "main": [2, 3, 5, 6, 7, 8, 9, 11, 18, 22], "mainli": [2, 7, 8, 9, 11, 23], "maintain": 8, "major": [3, 8, 11, 12, 15, 18, 23], "make": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 22, 23], "make_axes_locat": 8, "make_moon": [10, 11, 12], "make_pipelin": [2, 8, 12], "makedir": [2, 8, 9, 11, 23], "malcondit": 18, "malign": [3, 9, 11], "mammographi": 7, "manag": [0, 2, 4, 5, 17, 23], "mandatori": [21, 23], "mani": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 22, 23], "manifold": 13, "manner": 5, "manual": 8, "map": [2, 3, 4, 8, 9, 10, 13, 14, 16, 20, 23], "margin": [2, 7, 10], "marit": [2, 23], "mark": 23, "marker": [2, 9, 18, 23], "markov": [17, 23], "marsaglia": 20, "mass": [2, 3, 7, 15], "massag": [2, 23], "masses2016": [2, 23], "masses2016ol": [2, 23], "masses2016tre": 2, "masseval2016": [2, 23], "master": [19, 21], "mat": [17, 23], "mat1100": [17, 23], "mat1110": [17, 23], "mat1120": [17, 23], "match": [0, 3, 6, 7, 15, 16], "materi": [0, 6, 7, 9, 15, 18, 19, 21], "math": [5, 9, 14, 15, 18, 20, 22, 23], "mathbb": [2, 6, 7, 8, 9, 10, 13, 14, 15, 16, 18, 20, 23], "mathbf": [2, 7, 8, 9, 10, 15, 18, 23], "mathcal": [3, 7, 8, 9, 15], "matheemat": 5, "mathemat": [2, 8, 13, 14, 15, 17, 18, 20, 22, 23], "mathemati": 23, "mathrm": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "matmul": [3, 4, 7], "matnat": 22, "matplotlib": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "matric": [1, 2, 3, 5, 6, 8, 9, 10, 13, 15, 17], "matrix": [2, 4, 5, 6, 8, 9, 10, 12, 15, 20], "matshow": 3, "matter": [4, 5, 15], "max": [2, 3, 4, 5, 6, 11, 12, 14, 15, 21, 23], "max_depth": [2, 11, 12], "max_diff": 4, "max_diff1": 4, "max_diff2": 4, "max_it": [2, 3, 10, 15, 23], "max_iter": 16, "max_leaf_nod": 12, "max_sampl": 12, "maxdegre": [2, 8, 12], "maxdepth": 12, "maxim": [3, 6, 7, 9, 10, 13], "maximum": [2, 4, 5, 7, 9, 10, 11, 12, 15, 16, 23], "maxpolydegre": [7, 8], "maxpooling2d": 5, "mbox": [7, 8], "mcculloch": 14, "md": 13, "mdoel": 6, "mean": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "mean_absolute_error": [2, 23], "mean_divisor": 16, "mean_i": 20, "mean_matrix": 16, "mean_squared_error": [0, 2, 6, 8, 9, 12, 23], "mean_squared_log_error": [2, 23], "mean_vector": 16, "mean_x": 20, "meaning": [2, 6, 9, 23], "meansquarederror": [2, 23], "meant": [5, 9, 12, 15], "measur": [1, 2, 3, 4, 7, 8, 11, 13, 14, 16, 20, 23], "mechan": [2, 6, 20, 23], "median": [2, 23], "medicin": 14, "medium": [6, 10, 15], "medv": 2, "meet": [2, 21], "mehta": [2, 23], "memori": [5, 6, 13, 14, 15, 18], "mention": [2, 14, 15, 20, 23], "mere": 2, "meshgrid": [4, 7, 8, 10, 11, 12, 13], "mess": 0, "messag": [7, 15], "messi": 4, "met": [2, 5, 10], "meteorolog": 11, "meter": 8, "method": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 16, 17, 18, 20, 22], "metion": 8, "metric": [0, 2, 3, 5, 8, 9, 11, 12, 16, 23], "metropoli": [17, 23], "mev": [2, 20, 23], "mgd": 15, "mglearn": [17, 23], "mgrid": 15, "mhjensen": 23, "mi": 12, "mia": [21, 23], "microsoft": 22, "mid": 3, "midel": 6, "midnight": 0, "midpoint": 11, "might": [0, 2, 3, 4, 6, 8, 11, 15], "mild": 11, "millimet": 8, "million": [2, 23], "mimic": 14, "min": [2, 4, 7, 10, 11], "min_": [2, 4, 7, 16, 23], "min_samples_leaf": 11, "mind": [0, 2, 8, 15, 23], "mindboard": 6, "mine": [17, 23], "mini": [3, 13, 14, 15], "minibatch": [3, 13, 15], "minibathc": 15, "miniforge3": 23, "minim": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "minima": [2, 3, 9, 15, 23], "minimum": [2, 3, 4, 8, 10, 11, 13, 15], "minmaxscal": 2, "minor": 20, "minst": 3, "minu": 9, "mirjalili": 23, "mirror": 11, "misc": 8, "misclassif": [10, 11, 12], "misclassifi": [10, 12], "miser": 2, "mismatch": 3, "miss": [2, 9, 12], "mistak": 6, "mit": 22, "mix": [3, 4, 23], "mixtur": 15, "mk": [11, 18], "mkdir": [2, 8, 9, 11, 23], "ml": [2, 3, 12, 15, 18], "mlab": 20, "mle": [7, 9], "mlp": 3, "mlpclassifi": 3, "mlpregressor": [2, 23], "mm": 18, "mn": [14, 20], "mnist": [3, 13], "mod": 20, "mode": [19, 21, 23], "model": [1, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 20, 22], "model_select": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "moder": 12, "modern": [2, 8, 9, 17, 23], "modif": [4, 14, 15], "modifi": [2, 3, 5, 7, 9, 10, 12, 14, 15, 23], "modul": [1, 2, 18, 23], "modular": 20, "modulo": 20, "moe": 13, "moment": [7, 8, 15, 20], "mondai": [21, 23], "monitor": 15, "monoton": [7, 14, 20], "mont": [2, 8, 17, 20, 22, 23], "montli": 1, "moor": [7, 8], "more": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "moreov": [2, 5], "morten": [21, 23], "mortenhj": 23, "most": [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "mostli": [3, 13], "motion": [2, 15], "motiv": [3, 6], "move": [0, 1, 2, 6, 7, 8, 9, 11, 14, 15, 16, 20], "mpl": [9, 23], "mpl_toolkit": [4, 8, 15], "mplot3d": [4, 8, 15], "mplregressor": 3, "mse": [0, 1, 2, 6, 7, 8, 11, 12, 23], "mse_simpletre": 12, "mselassopredict": 7, "mselassotrain": 7, "mseownridgepredict": 8, "msepredict": 7, "mseridgepredict": [2, 7, 8], "msetrain": 7, "msle": [2, 23], "mt": [9, 14], "mu": [2, 8, 13, 15, 20, 23], "mu0": 20, "mu1": 20, "mu2": 20, "mu_": [8, 20], "mu_i": 8, "mu_n": 13, "mu_x": 20, "much": [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "multi": [2, 3, 5, 9, 17, 23], "multiclass": [3, 9], "multidimension": [13, 14, 23], "multilay": 3, "multinomi": 9, "multipl": [0, 4, 6, 7, 8, 9, 14, 15, 20], "multipli": [5, 7, 8, 13, 15, 18, 20], "multiplum": 10, "multivari": [2, 4, 12, 13, 17, 20, 23], "multivariate_norm": [13, 16], "multpli": 1, "murphi": [13, 22, 23], "must": [0, 3, 4, 7, 8, 10, 12, 14, 15, 16, 20], "mutat": 9, "mutual": [3, 5, 8, 15], "mx_": 20, "my": 23, "myenv": 23, "myriad": [2, 17, 23], "mz1": 20, "mz2": 20, "m\u00f8svatn": 8, "n": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "n1": 18, "n2": 18, "n_": [3, 4, 5, 10, 14, 20], "n_0": [14, 20], "n_boostrap": [8, 12], "n_bootstrap": 8, "n_categori": [3, 5], "n_cluster": 16, "n_compon": 13, "n_epoch": 15, "n_estim": 12, "n_examples_to_gener": 6, "n_featur": 3, "n_filter": 5, "n_hidden": 4, "n_hidden_neuron": [2, 3, 23], "n_i": 20, "n_input": [2, 3, 5], "n_instanc": 11, "n_job": 12, "n_k": 16, "n_l": [14, 20], "n_layer": 3, "n_m": 11, "n_neuron": 3, "n_neurons_connect": 5, "n_neurons_layer1": 3, "n_neurons_layer2": 3, "n_point": 16, "n_sampl": [8, 10, 11, 12, 16], "n_split": 8, "n_step": 6, "n_t": 4, "n_x": 4, "nabla": [3, 15], "nabla_": [4, 15], "nabla_w": 15, "nag": 15, "naimi": [2, 23], "naiv": 9, "naive_kmean": 16, "name": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23], "narrow": 15, "nation": [3, 7], "nativ": [17, 23], "natur": [2, 3, 6, 10, 11, 14, 15, 20, 22, 23], "navier": 14, "navig": 0, "nb": 20, "nb_": 18, "nbconvert": 23, "nd": 16, "ndarrai": 8, "ne": [11, 12, 18, 20], "nearest": [3, 5, 8, 13], "nearli": 15, "neat": 23, "neccesari": 8, "necess": 4, "necessari": [2, 3, 5, 6, 10, 16, 23], "necessarili": [2, 6, 13, 20, 23], "necesserali": 7, "neck": 9, "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "neg": [2, 3, 5, 7, 8, 9, 12, 15, 18, 20, 23], "neg_mean_squared_error": 8, "neglect": 20, "neglig": 20, "neighbor": [5, 8, 13], "neither": [6, 15], "neq": [15, 16, 20], "nervou": 14, "nest": [11, 14], "nesterov": 15, "net": [4, 6, 14], "netlib": [18, 23], "network": [2, 11, 15, 17, 22], "neural": [2, 15, 17, 22], "neural_network": [2, 3, 4, 23], "neuralnetwork": 3, "neuron": [3, 4, 5, 6, 14], "neutral": [2, 23], "neutron": [2, 23], "never": [3, 6, 8, 11, 20], "new": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "new_chang": 15, "new_hobbit": 23, "newaxi": [2, 5, 8, 11], "newli": [2, 23], "newton": [3, 9, 10, 15, 20], "next": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 23], "next_guess": 15, "next_input": 6, "ng": 3, "ni": 16, "nice": [2, 3, 7, 13, 23], "niter": 15, "nitric": 2, "nlambda": [2, 7, 8], "nlp": 22, "nm": 20, "nm_n": [2, 23], "nmse": 8, "nn": [4, 7, 8, 14, 18, 23], "nn_model": 3, "nnmin": 4, "node": [3, 5, 11, 12, 14], "nois": [2, 6, 7, 8, 10, 11, 12, 15, 23], "noise_dimens": 6, "noisi": [3, 8], "non": [2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "none": [2, 3, 4, 6, 7, 11, 12, 15, 20, 23], "nonlinear": [5, 8, 10, 11, 13, 14], "nonneg": [8, 11, 15], "nonparametr": 8, "nonsens": 20, "nonsingular": 18, "nonumb": [5, 9, 10, 15, 18], "nor": [3, 6, 15], "norm": [2, 3, 7, 8, 10, 13, 15, 23], "normal": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "normali": [18, 23], "norwai": [8, 23], "notat": [2, 4, 7, 8, 15, 16, 20, 23], "note": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 22, 23], "notebook": [0, 1, 2, 3, 5, 11, 17, 23], "noth": [3, 4, 7, 10, 14, 16, 20], "notic": [6, 7, 14, 15, 18, 20, 23], "notion": 5, "novel": [5, 8, 12, 23], "novemb": [3, 21, 23], "now": [0, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23], "nowadai": [2, 3, 5, 11, 17, 23], "nox": 2, "np": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "npr": 4, "nsampl": 8, "nt": 4, "nu": 20, "nuclear": 7, "nuclei": [2, 20, 23], "nucleon": [2, 23], "nucleu": [2, 23], "num": 6, "num_coordin": 4, "num_hidden_neuron": 4, "num_it": 4, "num_neuron": 4, "num_neurons_hidden": 4, "num_point": 4, "num_tre": 12, "num_valu": 4, "number": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 23], "numberid": 9, "numberparamet": 5, "numer": [2, 7, 8, 11, 12, 13, 14, 15, 17, 18, 22, 23], "numpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "nunmpi": 7, "nx": 4, "ny": 20, "o": [2, 3, 6, 7, 8, 9, 10, 11, 13, 18, 21, 22, 23], "obei": [8, 13, 15], "object": [0, 2, 3, 6, 10, 12, 18, 23], "obliqu": 7, "observ": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "obtain": [2, 3, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "obviou": [7, 8, 13, 20], "obviouli": 23, "obvious": [2, 6, 7, 8, 18, 23], "occupi": 2, "occur": [2, 8, 10, 11, 18, 20, 23], "octob": [21, 23], "od": 2, "odd": [2, 5, 9, 23], "odenum": 4, "odesi": 4, "oen": 2, "off": [3, 5, 6, 7, 11, 15, 20], "offer": [8, 13, 17, 18, 19, 21, 23], "offic": [21, 23], "offici": [19, 23], "often": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "ofter": [18, 23], "ol": [2, 15], "old": [0, 3, 7, 12, 15], "ols_paramet": 1, "ols_sk": 8, "ols_svd": 8, "olstheta": [2, 7], "omega": [4, 5, 8], "omega_0": 5, "omit": [2, 7, 23], "onc": [3, 8, 11, 13, 15], "one": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 23], "onehot": 3, "onehot_vector": 3, "onehotencod": 11, "ones": [1, 2, 4, 7, 8, 10, 11, 12, 13, 15, 18, 23], "ones_lik": 6, "onl": 5, "onli": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "onlin": [0, 13, 19], "onto": [7, 13], "open": [0, 2, 3, 6, 8, 9, 11, 17, 19, 21, 23], "oper": [0, 1, 2, 3, 5, 7, 8, 12, 13, 14, 15, 17, 20, 23], "operation": 20, "oplu": 20, "opmiz": 15, "opportun": 2, "oppos": [8, 15], "opposit": [3, 7, 10], "opt": [3, 7, 23], "optim": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16], "optimis": [3, 5], "option": [0, 2, 3, 5, 7, 8, 10, 13, 18], "optmiz": [3, 10, 15], "oral": 23, "orang": 2, "order": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "ordinari": [2, 4, 5, 9, 13, 15, 17], "oreilli": [22, 23], "org": [1, 2, 5, 6, 17, 18, 22, 23], "organ": [8, 9, 12, 18], "orient": [3, 7, 20], "origin": [0, 2, 5, 7, 8, 10, 13, 14, 15, 18, 23], "orthogn": 7, "orthogon": [2, 7, 8, 10, 13, 15, 18, 23], "orthonorm": 7, "os": [21, 23], "oscar": 3, "oscil": [5, 15], "oskar": 23, "oskarlei": 23, "oslo": [2, 17, 19, 21, 23], "osx": [2, 17, 23], "other": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 15, 16, 17, 19, 20, 21, 22], "otherwis": [2, 3, 6, 9, 15, 18, 23], "ouput": [7, 9, 14], "our": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20], "ourmodel": 2, "ourselv": [2, 7, 8, 10, 13, 15, 23], "out": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "out_fil": 11, "outcom": [2, 9, 11, 12, 14, 20], "outdoor": 11, "outer": [8, 14, 15], "outfil": 6, "outlier": [2, 10, 23], "outlin": [8, 12, 13], "outlook": 11, "outperform": 12, "output": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "output_bia": 3, "output_bias_gradi": 3, "output_shap": 6, "output_weight": 3, "output_weights_gradi": 3, "outputlayer1": 14, "outputlayer2": 14, "outsid": 6, "over": [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 14, 15, 18, 23], "over1": 15, "overal": [3, 12], "overcast": 11, "overcom": [14, 15], "overdetermin": [2, 23], "overfit": [2, 3, 5, 8, 11, 12, 15], "overflow": 7, "overhead": 14, "overlap": [5, 9, 10, 11], "overlin": [2, 7, 8, 11, 12, 13, 16, 18, 23], "overst": 2, "overtrain": 6, "overview": 5, "own": [1, 6, 7, 8, 10, 14, 15, 17, 18], "owner": 2, "ownmsepredict": 2, "ownmsetrain": 2, "ownridgetheta": [2, 8], "ownypredictridg": 2, "ownytilderidg": 2, "oxid": 2, "p": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "p0": 4, "p1": 4, "p_": [4, 6, 10, 11], "p_hidden": 4, "p_i": [7, 20], "p_j": 20, "p_n": 20, "p_output": 4, "p_x": 20, "pack": [2, 23], "packag": [0, 2, 3, 5, 6, 7, 10, 13, 15, 17, 20], "packtpub": 23, "packtpublish": 23, "pad": [5, 6], "page": [2, 17, 23], "pai": [0, 2, 3, 11, 15], "pair": [2, 4, 5, 11, 17, 20, 23], "paltform": 0, "panda": [2, 6, 7, 8, 9, 11, 13, 17], "panel": 23, "paper": 3, "paradigm": [2, 23], "parallel": [12, 15, 17, 18, 23], "param": 4, "paramat": 4, "paramet": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20], "parameter": [2, 8, 12, 23], "parametr": [2, 8, 23], "paramt": [5, 7], "part": [2, 3, 5, 7, 8, 12, 18, 19, 20, 21, 23], "partial": [1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "particip": [0, 17, 19, 21, 23], "particl": [2, 6, 15, 20, 23], "particular": [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 20, 22, 23], "particularli": [7, 8, 10, 13, 15, 20], "partit": [3, 6, 11], "partli": [8, 23], "partner": 0, "pass": [4, 5, 14, 16], "past": [12, 20], "patch": [8, 20], "path": [2, 6, 8, 9, 11, 17, 23], "patient": 9, "patter": 6, "pattern": [2, 5, 6, 14, 22, 23], "pauli": [2, 23], "pc": [13, 17], "pca": [2, 9, 17, 23], "pd": [2, 6, 7, 8, 9, 11, 13, 23], "pde": 4, "pdf": [0, 1, 2, 5, 6, 7, 8, 11, 22, 23], "pedagog": [2, 23], "penal": 8, "penalti": [8, 15], "penros": [7, 8], "pentagon": 15, "peopl": [2, 3, 11, 15, 17], "per": [2, 3, 8, 19, 21, 23], "percentag": [2, 12, 13, 21], "perceptron": [2, 3, 9, 23], "peregrin": 23, "perfect": [2, 3, 15, 23], "perfectli": [6, 8], "perform": [1, 2, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 20, 23], "performac": 6, "perhap": [2, 7, 15, 23], "perimet": 3, "period": [3, 6, 20], "permiss": 0, "permut": 13, "persist": 15, "person": [1, 7, 8, 9, 19, 21, 23], "perspect": 22, "pertin": [14, 23], "petal": [10, 11], "peter": 22, "phantom": 20, "phase": [8, 14], "phenomena": 20, "phi": 10, "phi_k": 10, "philosophi": 15, "phone": [21, 23], "photo": [6, 23], "phrase": [2, 23], "physic": [2, 3, 6, 9, 14, 15, 20, 21, 22, 23], "pi": [4, 5, 7, 8, 9, 11, 14, 15, 20], "pick": [3, 11, 12, 13, 15, 16], "pickl": 3, "pictur": [2, 23], "pie": [17, 23], "piec": [13, 16], "pillow": [2, 17, 23], "pinv": [7, 8, 15], "pip": [0, 2, 3, 17, 23], "pip3": [2, 3, 23], "pipelin": [2, 8, 10, 12], "pippin": 23, "pit": 6, "pitfal": 8, "pitt": 14, "pixel": [3, 5, 6, 23], "pixel_height": [3, 5], "pixel_width": [3, 5], "place": [0, 2, 6, 8, 10, 15, 18, 23], "plai": [2, 5, 6, 7, 8, 10, 13, 17, 23], "plain": [10, 12, 14, 15, 16], "plan": [8, 11, 21, 22, 23], "plane": [10, 11], "plateau": 7, "platform": [17, 23], "plausibl": 14, "pleas": [15, 21, 23], "plenti": 3, "plethora": [5, 14], "plot": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "plot_confusion_matrix": [9, 12], "plot_count": 8, "plot_cumulative_gain": [9, 12], "plot_data": 3, "plot_dataset": 10, "plot_decision_boundari": [11, 12], "plot_import": 12, "plot_max": 6, "plot_min": 6, "plot_model": 6, "plot_numb": 6, "plot_predict": 10, "plot_regression_predict": 11, "plot_result": 6, "plot_roc": [9, 12], "plot_surfac": [4, 8, 15], "plot_train": 11, "plot_tre": [11, 12], "plt": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "plu": [2, 5, 7, 9, 23], "pm": 10, "pmatrix": 4, "pml": 22, "pn": 5, "png": [2, 6, 8, 9, 11, 23], "point": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "point_1": 6, "point_2": 6, "poisson": [17, 20, 23], "poli": [8, 10], "poly100_kernel_svm_clf": 10, "poly3": 2, "poly3_plot": 2, "poly_featur": [0, 10, 11], "poly_features10": 11, "poly_fit": 11, "poly_fit10": 11, "poly_kernel_svm_clf": 10, "poly_model": 0, "poly_ms": 0, "poly_predict": 0, "polydegre": [2, 7, 8, 12], "polygon": 15, "polym": 14, "polynomi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 23], "polynomial_featur": [0, 1, 8], "polynomial_svm_clf": 10, "polynomialfeatur": [0, 1, 2, 8, 10, 11], "polytrop": [2, 8], "pool": 5, "pool_siz": 5, "poor": [3, 15], "poorli": 2, "popul": [2, 7, 23], "popular": [0, 2, 3, 5, 8, 9, 10, 11, 13, 14, 17, 18, 20], "popularli": [2, 23], "portabl": 12, "portion": [13, 15], "pose": [2, 6, 7, 8, 13, 20, 23], "posit": [2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 18, 20, 23], "possibl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 23], "possibli": [8, 10, 15], "posterior": 7, "postpon": 2, "postul": 7, "potenti": [2, 5, 7, 8, 14, 15], "pott": 14, "power": [2, 3, 7, 8, 10, 11, 14, 15, 23], "pp": [7, 8], "practic": [1, 2, 7, 8, 9, 10, 20], "practition": [2, 3, 5, 23], "pre": 23, "preced": [3, 13, 14, 20], "preceed": 6, "preceq": 10, "precis": [2, 4, 7, 13, 15, 18, 20, 23], "pred": 8, "predicit": 2, "predict": [0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 17, 22, 23], "predict_prob": 3, "predict_proba": [9, 12], "predictor": [2, 7, 8, 9, 11, 12, 13, 23], "prefer": [2, 3, 8, 10, 11, 13, 15, 17, 23], "prepar": [2, 8, 18, 23], "preprocess": [0, 1, 2, 6, 8, 9, 10, 11, 12, 13], "prerequisit": 2, "presenc": 15, "present": [2, 7, 8, 9, 11, 14, 15, 18, 20, 23], "preserv": [5, 13, 18], "press": [0, 15, 22], "pretrain": [3, 6], "pretti": [2, 6, 10, 11, 17, 23], "prev_centroid": 16, "prevent": [15, 20], "previou": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20], "previous": [4, 5, 11, 12, 20], "price": [2, 6, 11, 15], "primal": 10, "primari": [2, 9, 23], "prime": 20, "princip": [2, 7, 9, 17, 23], "principl": [2, 8, 9, 10, 16, 23], "print": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "print_funct": [10, 11], "printout": [2, 23], "prior": [2, 7, 8, 23], "privat": 2, "prob": [3, 20], "probabilist": [2, 22, 23], "probabl": [2, 3, 5, 6, 8, 9, 12, 15, 17, 23], "problem": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20], "probml": 22, "proce": [2, 7, 8, 9, 10, 11, 12, 13, 15, 18, 23], "procedur": [4, 6, 7, 8, 10, 12, 13, 15], "proceed": 18, "process": [2, 4, 6, 8, 11, 12, 14, 15, 17, 18, 20, 22, 23], "prod": 22, "prod_": [3, 7, 9], "produc": [2, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 23], "product": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 18, 23], "profess": [2, 23], "program": [0, 2, 3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 20, 21, 23], "programm": 18, "progress": [3, 6, 16], "prohibit": 8, "project": [0, 2, 3, 4, 5, 7, 13, 15, 17, 19], "project_root_dir": [2, 8, 9, 11, 23], "promin": 14, "promis": 10, "promot": [21, 23], "prone": [0, 11], "pronounc": [15, 17, 23], "proof": [2, 13, 14, 15, 23], "propag": [4, 5, 15], "proper": [2, 4, 8, 9], "properli": [3, 8, 10, 12, 15], "properti": [1, 2, 3, 5, 14, 15, 18, 23], "proport": [2, 3, 7, 11, 13, 15, 20, 23], "propos": [3, 6, 8, 12, 23], "propto": [7, 15], "proton": [2, 23], "prove": [5, 15], "provid": [2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 17, 18, 20, 23], "proxi": [3, 15], "prune": 11, "pseudo": [18, 20], "pseudoinv": 7, "pseudoinvers": [7, 8], "pseudorandom": [8, 20], "psychologi": [2, 23], "pt": 15, "public": [0, 2, 17, 23], "pull": 0, "punish": [2, 3, 23], "pure": [5, 11, 20], "purest": 11, "puriti": 11, "purpos": [2, 5, 12, 14, 16, 23], "push": 0, "put": 3, "py": [7, 23], "pycod": 23, "pydata": 17, "pydot": 11, "pyhton2": 23, "pylab": [9, 23], "pypi": 17, "pyplot": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "pythagora": 7, "python": [3, 4, 5, 7, 8, 10, 13, 14, 15, 16, 20], "python2": 2, "python3": [2, 17, 23], "pytorch": [2, 17, 23], "q": [7, 8, 10, 13, 20], "qp": 10, "qquad": [4, 13, 15, 18], "qr": [7, 8, 18], "quad": [3, 15, 18], "quadrat": [2, 10, 11, 15, 23], "qualit": [6, 11, 20], "qualiti": [2, 11, 17, 23], "quantifi": 3, "quantil": 12, "quantit": [2, 8, 11, 23], "quantiti": [1, 2, 4, 7, 8, 9, 11, 12, 13, 14, 16, 18, 20, 23], "quantum": [6, 14, 22, 23], "quartil": 2, "quench": 7, "queri": 11, "question": [2, 7, 8, 11, 13, 14, 15, 21, 23], "qugan": 6, "quick": [6, 20], "quickli": [3, 5, 11, 13, 15], "quit": [0, 3, 7, 8, 11, 12, 14], "quot": 6, "r": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "r2": [2, 7, 8, 23], "r2_score": [2, 23], "r2score": [2, 23], "r_1": 11, "r_2": 11, "r_j": 11, "r_m": 11, "rad": 2, "radial": [2, 10, 14], "radioact": 20, "radiu": [2, 3], "rain": 11, "ramp": 3, "ran0": 20, "ran1": 20, "ran2": 20, "ran3": 20, "rand": [0, 2, 6, 7, 8, 11, 12, 15, 18, 23], "randint": [8, 11, 15], "randn": [0, 2, 3, 4, 7, 8, 11, 13, 15, 23], "random": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 17, 18, 23], "random_forest_model": 12, "random_index": 15, "random_indic": [3, 5], "random_st": [2, 9, 10, 11, 12, 13], "randomforestclassifi": 12, "randomli": [3, 8, 11, 15, 16], "rang": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "rangl": [2, 8, 13, 20, 23], "rangle_x": 20, "rank": 7, "rankdir": 6, "raphson": [3, 10, 15], "rapidli": 2, "rare": [3, 15], "raschka": 23, "rasckha": 23, "rate": [2, 3, 4, 5, 6, 10, 11, 12, 14, 15], "rather": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "ratio": [6, 9, 11, 12, 13], "rational": [2, 23], "ravel": [7, 8, 9, 10, 11, 12, 13, 15, 18], "raw": 5, "rbf": [10, 13, 14], "rbf_kernel_svm_clf": 10, "rbf_pca": 13, "rc": [2, 20], "rcond": [2, 23], "rcparam": [3, 5, 9, 10, 11, 12, 20, 23], "re": [0, 4, 6, 15], "reach": [3, 6, 7, 8, 11, 12, 14, 15, 16, 23], "read": [1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 18, 20, 22], "read_csv": [2, 8, 9, 11], "read_fwf": [2, 23], "reader": [2, 8, 18, 20, 23], "readi": [2, 3, 7, 8, 10, 12, 13, 14, 18, 23], "readili": 3, "readm": 0, "readthedoc": 17, "real": [1, 2, 3, 6, 9, 12, 13, 14, 18], "real_loss": 6, "real_output": 6, "realist": [10, 23], "realiti": 20, "realiz": [3, 14], "realli": [2, 3, 23], "rearrang": 15, "reason": [2, 3, 5, 6, 12, 15, 22, 23], "reassign": 3, "recal": [7, 8, 11, 12, 13, 14, 18, 20, 23], "recast": 5, "receiv": [3, 5, 12, 14, 20], "recent": [2, 8, 15, 22], "recept": [5, 14], "receptive_field": 5, "recip": [2, 8, 9, 18, 23], "reciproc": 7, "recogn": [2, 6, 7, 12, 23], "recognit": [2, 3, 5, 14, 22, 23], "recommen": 23, "recommend": [0, 2, 4, 5, 6, 7, 8, 10, 15, 17, 18, 22], "reconsid": 11, "reconstruct": 13, "record": [12, 19, 21, 23], "recreat": 0, "rectangl": [11, 15], "rectangular": 7, "rectifi": [3, 5, 14], "recur": [2, 17, 23], "recurr": [2, 3, 17, 23], "recurs": [11, 17, 18, 23], "red": [2, 5, 6, 8, 10, 11], "redefin": [2, 12, 23], "reduc": [3, 5, 7, 8, 11, 12, 13, 15, 23], "reduct": [2, 12, 13, 17, 20, 23], "refer": [2, 3, 4, 5, 7, 8, 13, 14, 15, 16, 18, 22, 23], "referenc": 4, "refin": 14, "refit": 8, "reflect": [2, 3, 6, 7, 20, 23], "refresh": [17, 23], "refreshprogrammingskil": 23, "reg": [12, 13], "regard": [3, 11, 15], "regardless": [1, 14], "region": [5, 6, 8, 11, 14], "regist": [8, 20], "reglasso": 7, "regr_1": [2, 11], "regr_2": [2, 11], "regr_3": [2, 11], "regress": [1, 3, 10, 13, 14, 17, 18], "regressor": [2, 9, 12], "regridg": [2, 7, 8], "regular": [2, 5, 6, 7, 8, 9, 11, 15, 21, 23], "regularli": 0, "reilli": [2, 22, 23], "reinforc": [2, 10, 17, 23], "reiter": 3, "reject": 9, "rel": [2, 6, 8, 9, 11, 14, 15, 20, 23], "relat": [2, 3, 5, 6, 7, 13, 15, 16, 18, 20, 23], "relationship": [2, 6, 11, 23], "relativeerror": [2, 23], "releas": [3, 17, 23], "relev": [2, 3, 7, 9, 13, 17, 20, 23], "reli": [2, 8, 10], "reliabl": [9, 20], "relu": [5, 6, 23], "remain": [3, 4, 6, 8, 14, 18, 20], "remaind": 20, "reman": 4, "remark": 3, "rememb": [2, 10, 15, 18, 23], "remind": [2, 7, 13, 15, 18, 20], "remot": 0, "remov": [2, 6, 7, 8], "renam": 0, "render": [2, 23], "reorder": [7, 9], "reorgan": [2, 23], "repeat": [2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 16, 18, 20, 23], "repeated": 23, "repeatedli": [2, 8, 12, 15], "repet": 5, "repetit": [8, 23], "rephras": 15, "replac": [2, 3, 5, 6, 7, 8, 12, 14, 16, 17, 23], "replica": 8, "repo": 0, "report": 23, "repositori": [2, 6, 23], "repres": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23], "represent": [2, 3, 5, 8, 20, 23], "representd": 5, "reproduc": [0, 1, 2, 7, 8, 11, 14, 17, 20, 23], "repuls": [2, 23], "request": [2, 15], "requir": [0, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 18, 23], "res1": 4, "res2": 4, "res3": 4, "res_analyt": 4, "res_analytical1": 4, "res_analytical2": 4, "res_analytical3": 4, "resaml": 8, "resampl": [2, 9, 12, 17, 23], "rescal": [2, 13, 14], "rescu": 7, "reseach": 8, "research": [2, 6, 15, 17, 22, 23], "resembl": [8, 20], "reserv": [3, 7, 8, 20], "reshap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 18, 23], "residenti": 2, "residu": [2, 7, 15, 23], "resiz": 7, "resourc": 23, "respect": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 20, 23], "respond": 14, "respons": [2, 9, 11, 14, 23], "rest": [2, 7], "restat": [2, 14, 23], "restor": 6, "restored_discrimin": 6, "restored_gener": 6, "restrict": [2, 5, 11, 14, 23], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "retail": 2, "retain": [7, 8], "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 20, 23], "return_data": 16, "return_sequ": 6, "return_x_i": 11, "reus": [3, 5, 8], "reveal": [2, 14, 23], "revers": [3, 18], "review": [17, 18], "revisit": 16, "revolut": 23, "reward": [2, 6, 23], "rewrit": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "rewritten": [4, 8, 10, 12, 20], "rewrot": 15, "rf": 12, "rgb": 5, "rgoj5yh7evk": 17, "rh": 8, "rho": [2, 12, 15], "rho_1": 12, "rho_2": 12, "rho_m": 12, "rich": [2, 23], "ride": 11, "rideclass": 11, "ridedata": 11, "ridg": [9, 13, 15, 17, 23], "ridge_sk": 8, "ridgetheta": 7, "right": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "right_sid": 4, "rightarrow": [2, 3, 7, 8, 10, 13, 14, 15, 20, 23], "rigor": [2, 23], "ring": 8, "rise": [2, 23], "risk": [2, 15, 23], "rival": 6, "river": 2, "rlm": 23, "rm": [2, 20], "rmse": 2, "rmsporp": 15, "rmsprop": [3, 5, 6, 15], "rnd_clf": 12, "rng": 20, "rnn": [6, 14], "rnn1": 6, "rnn2": 6, "rnn_2layer": 6, "rnn_input": 6, "rnn_output": 6, "rnn_train": 6, "rntrick1": 20, "rntrick2": 20, "rntrick3": 20, "rntrick4": 20, "ro": [2, 15, 23], "robert": 22, "robust": [2, 23], "robustscal": 2, "roc": [9, 12], "role": [2, 4, 7, 8, 10, 17, 23], "roll": 8, "room": [2, 21, 23], "root": [0, 2, 7, 11, 15, 20], "rot": 23, "rotat": [3, 10, 11, 12], "rotation_matrix": 11, "roughli": [3, 5], "round": [2, 9, 11, 15], "routin": [15, 18, 23], "row": [1, 2, 3, 4, 7, 8, 11, 13, 18, 23], "rr": 7, "rrr": 7, "rug": 15, "rule": [2, 3, 7, 8, 15, 23], "run": [0, 2, 3, 4, 6, 7, 8, 10, 11, 13, 15, 17, 23], "runtim": [0, 3, 8, 16], "rust": [2, 17, 18, 23], "rvert": 3, "rvert_2": 3, "s_": [5, 8], "s_1": 8, "s_i": [8, 9], "s_j": 8, "s_k": 8, "saddl": 15, "sai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "said": [8, 11, 15], "sake": [2, 7, 9, 13, 23], "sale": [2, 23], "sam": 23, "same": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 23], "samm": 12, "sampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 23], "sample_vari": 16, "sampleexptvari": 20, "samwis": 23, "sastri": 13, "satisfactori": [2, 23], "satisfi": [3, 4, 5, 8, 10, 15, 18, 20], "satur": [3, 8], "save": [2, 6, 8, 9, 11, 15, 23], "save_fig": [2, 8, 9, 11, 12, 23], "savefig": [2, 6, 8, 9, 11, 20, 23], "savetxt": 6, "saw": 7, "scalabl": 12, "scalar": [4, 7, 8, 12], "scale": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 23], "scale_mean": 6, "scale_std": 6, "scaler": [2, 9, 10, 11, 12, 13], "scan": [7, 9], "scari": 7, "scatter": [0, 2, 3, 8, 9, 10, 11, 16, 23], "scenario": [8, 15], "schedul": 15, "scheme": [3, 15], "schrage": 20, "sch\u00f8yen": 8, "scienc": [2, 3, 12, 14, 15, 17, 19, 20, 21, 22], "scientif": [2, 17, 23], "scientist": [2, 23], "scikit": [0, 1, 5, 7, 8, 10, 11, 12, 15, 17, 18, 22], "scikit_learn": 2, "scikitlearn": 23, "scikitplot": [9, 12], "scipi": [2, 5, 7, 8, 15, 17, 18, 23], "scl": 8, "scm": 0, "score": [0, 1, 2, 3, 5, 8, 9, 11, 12, 13, 21, 23], "scores_kfold": 8, "scratch": [1, 3, 15], "sdg": 15, "seaborn": [2, 3, 5, 8, 9, 23], "seamless": [2, 17, 23], "search": [0, 2, 3, 5, 7, 11, 15, 23], "sebastian": 23, "sebastianraschka": 23, "sec": 8, "second": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 23], "secondeigvector": 13, "secondli": 14, "section": [1, 6, 13, 18, 20], "sector": 2, "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 23], "seed": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 20, 23], "seed_imag": 6, "seek": [3, 4, 10], "seem": [3, 5, 6], "seemingli": [2, 23], "seen": [2, 3, 5, 7, 12, 14, 20], "segment": 15, "seismic": 8, "seldomli": [2, 23], "select": [0, 3, 7, 8, 10, 11, 12, 13, 19, 20, 21, 22, 23], "selevet": 0, "self": [3, 7], "sell": 6, "semest": [9, 19], "semi": [10, 15], "semilogx": 8, "send": [7, 14, 15, 21, 23], "senior": [19, 21], "sens": [2, 6, 8, 10, 23], "sensibl": 5, "sensit": [2, 7, 8, 11, 15, 23], "sent": 4, "sentenc": [6, 14], "separ": [2, 3, 4, 6, 8, 10, 11, 14, 16, 17, 20, 23], "septemb": 23, "sequenc": [5, 6, 9, 11, 12, 14, 15, 17, 18, 20, 23], "sequenti": [3, 5, 6, 12, 14, 20], "seri": [2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 18, 23], "serif": [9, 20, 23], "serv": [2, 3, 4, 5, 7, 9, 15, 22, 23], "session": [0, 3, 19, 21, 23], "set": [1, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 20, 21], "set_major_formatt": 8, "set_major_loc": 8, "set_tick": [3, 10], "set_ticklabel": 3, "set_titl": [2, 3, 4, 5, 9, 14, 16, 23], "set_xlabel": [2, 3, 4, 5, 9, 14, 23], "set_xlim": [9, 14], "set_xticklabel": 3, "set_ylabel": [2, 3, 4, 5, 9, 23], "set_ylim": [9, 14], "set_ytick": 9, "set_yticklabel": [3, 8], "set_zlim": 8, "seth": 6, "setminu": 8, "setosa": [10, 11], "setosa_or_versicolor": 10, "setp": 8, "setup": [3, 6, 8, 10, 17, 23], "sever": [1, 2, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "sgd": [3, 5], "sgd_clf": 10, "sgdclassifi": 10, "sgdreg": 15, "sgdregressor": 15, "sgn": 7, "shallow": 15, "shape": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "share": [0, 3, 5, 23], "shareabl": 0, "she": 9, "shift": [0, 3, 8, 14, 20], "ship": 5, "shire": 23, "short": [6, 7], "shortcom": 15, "shorten": 6, "shorter": 20, "shorthand": 23, "shortli": [18, 23], "should": [0, 2, 4, 5, 7, 8, 10, 11, 13, 14, 18, 20, 23], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "show_shap": 6, "shown": [2, 6, 7, 10, 14, 15, 18], "shrink": [5, 7, 8, 10, 13], "shrinkag": [7, 8], "shrunk": 13, "shuffl": [2, 3, 6, 8, 15], "side": [2, 4, 7, 10, 14, 15, 18, 23], "sigh": [17, 23], "sigma": [2, 3, 7, 8, 9, 12, 13, 14, 15, 18, 20, 23], "sigma0": 20, "sigma1": 20, "sigma2": 20, "sigma_": [7, 18, 23], "sigma_0": 7, "sigma_1": 7, "sigma_2": 7, "sigma_fn": [9, 14], "sigma_i": [2, 7, 23], "sigma_j": 7, "sigma_m": [8, 20], "sigma_n": [13, 20], "sigma_t": 15, "sigma_x": 20, "sigmoid": [3, 4, 6, 9, 10, 12, 14], "sigmundson": 8, "sign": [3, 4, 9, 10, 12, 20, 21], "signal": [3, 5, 12, 14], "signifi": 6, "signific": 3, "significantli": [3, 15, 20], "sim": [6, 7, 8, 15, 20], "similar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 23], "similarli": [2, 3, 5, 7, 10, 12, 15, 20, 23], "simpl": [1, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20], "simplepredict": 12, "simpler": [1, 2, 3, 7, 8, 9, 15, 17, 23], "simplernn": 6, "simplest": [2, 3, 5, 6, 11, 12, 14, 16, 23], "simpletre": 12, "simpli": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 23], "simplic": [4, 7, 8, 9, 10, 11, 12, 13, 14, 16], "simplicti": 7, "simplifi": [2, 8, 11, 17, 23], "simplist": [5, 8, 20], "simul": 8, "simultan": 8, "sin": [2, 3, 4, 5, 6, 11, 14, 15, 18, 23], "sinc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 22, 23], "sine": [5, 14], "singl": [2, 3, 4, 5, 7, 8, 9, 10, 11, 14, 15, 18, 20, 23], "singular": [2, 8, 15, 18, 23], "sinusoid": 5, "site": [2, 19, 23], "situat": [2, 6, 7, 9, 15, 20, 23], "six": [5, 20], "size": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 18, 20, 23], "sketch": 12, "ski": 11, "skill": 2, "skip": 13, "skl": [2, 8, 23], "sklearn": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 23], "skplt": [9, 12], "sl": 8, "slack": 10, "slice": [4, 18, 23], "slide": [1, 2, 5, 20, 23], "slight": [8, 15], "slightli": [3, 4, 5, 7, 8, 9, 12, 20], "slope": [10, 13, 14], "slow": [2, 4, 10, 15], "slower": [7, 18, 23], "slowest": 18, "slowli": 14, "slp": 3, "small": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "smaller": [2, 3, 4, 7, 8, 10, 11, 13, 15, 20, 23], "smallest": [2, 6, 16, 23], "smallest_row_index": 16, "smooth": [2, 5, 8, 15, 23], "sn": [2, 3, 5, 8, 9, 23], "sne": 13, "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "soar": 8, "social": 2, "soft": [3, 9, 12, 14], "soften": 10, "softmax": [5, 9], "softwar": [2, 10, 17, 18], "sol": 10, "sole": [2, 8, 23], "solid": [2, 9], "solut": [2, 3, 4, 5, 7, 8, 10, 12, 13, 15, 18, 20, 23], "soluton": 4, "solv": [1, 2, 3, 5, 7, 8, 10, 12, 13, 14, 15, 18, 23], "solve_expdec": 4, "solve_ode_deep_neural_network": 4, "solve_ode_neural_network": 4, "solve_pde_deep_neural_network": 4, "solveod": 4, "solveode_popul": 4, "solver": [4, 9, 10, 11, 12, 18, 23], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "some_model": 8, "somehow": 6, "someon": 1, "someth": [0, 2, 3, 5, 6, 9, 11, 13, 20, 23], "sometim": [2, 3, 13, 14, 15, 16], "soon": [18, 21], "sophist": [2, 23], "sopt": 15, "sort": [7, 8, 11, 13, 20], "sound": [5, 7], "sourc": [2, 3, 5, 8, 17, 18, 20, 23], "space": [2, 3, 6, 7, 10, 11, 13, 14, 15, 16, 20], "span": [2, 5, 7, 11, 13, 18, 23], "spare": 3, "spars": [5, 8, 18, 23], "sparse_mtx": [18, 23], "sparsecategoricalcrossentropi": 5, "sparsiti": 12, "spatial": [3, 4, 5, 14], "speak": 20, "special": [8, 9, 12, 14, 15, 18, 20, 23], "specif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20, 22, 23], "specifi": [2, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "specifici": [2, 12, 23], "spectacular": 5, "spectral": 3, "speech": [2, 3, 5, 6, 14], "speed": [3, 4, 6, 15], "spend": [1, 20], "sphere": 2, "spin": 8, "spite": 2, "spline": 10, "split": [1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 20, 23], "splite": 2, "splitter": [3, 12], "spontan": 20, "spot": 5, "spread": [2, 13, 20, 23], "springer": [22, 23], "spuriou": 15, "sqrsignal": 5, "sqrt": [2, 5, 6, 7, 8, 10, 12, 13, 15, 20], "squar": [0, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 17, 18, 20], "squarederror": 12, "squaredeuclidean": 16, "squash": 14, "srtm": 8, "srtm_data_norway_1": 8, "stabil": 7, "stabl": [1, 2, 6, 7, 8, 11, 17, 23], "stack": [5, 6], "stage": [0, 7, 15], "stai": [2, 4, 6, 7, 13, 23], "stand": [2, 7, 11, 14, 23], "standard": [2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 20, 23], "standardscal": [2, 8, 9, 10, 11, 12, 13], "stanford": 15, "start": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "start_tim": 16, "stat": 8, "state": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "statement": [2, 9, 18, 23], "statist": [2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 16, 18, 22], "statu": [0, 2, 9, 23], "stavang": 8, "std": [2, 6, 8, 23], "steep": 15, "step": [0, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 16, 18, 23], "step_fn": [9, 14], "step_length": 15, "steps_list": 11, "stereo": 5, "still": [2, 4, 5, 7, 8, 13, 15, 20], "stimuli": 14, "stk": [22, 23], "stk2100": [22, 23], "stk3155": [0, 19, 21], "stk4021": [22, 23], "stk4051": [22, 23], "stk4155": [19, 21], "stk5000": 22, "stochast": [2, 3, 7, 8, 10, 13, 14, 23], "stock": 6, "stoke": 14, "stone": [2, 9], "stop": [3, 6, 11, 15, 16], "storag": 7, "store": [2, 3, 4, 5, 8, 13, 15, 20, 23], "storehaug": [21, 23], "str": [3, 5, 6], "straight": [2, 8, 10, 15, 23], "straightforward": [2, 4, 5, 7, 8, 10, 11, 12, 15, 18, 23], "strategi": [2, 3, 11, 23], "stratifi": 8, "strength": [2, 7, 16], "stretch": 13, "strict": [10, 15], "strictli": [10, 15], "stride": [6, 18], "strike": 8, "string": 3, "stroke": 9, "strong": [5, 8, 11, 12, 14, 18, 20], "strongli": [0, 2, 10, 17, 18], "stronli": 2, "structur": [2, 3, 4, 5, 8, 11, 12, 14, 17, 23], "stuck": [3, 15], "student": [0, 2, 19, 21, 22, 23], "studi": [2, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 22, 23], "studier": 22, "style": [9, 11, 18, 23], "st\u00f8land": 21, "sub": [11, 14], "subdivid": [2, 18, 23], "subfield": 2, "subject": [8, 10, 20], "submit": 23, "subplot": [2, 3, 5, 6, 8, 9, 10, 11, 12, 16, 23], "subplots_adjust": [10, 20], "subprogram": [18, 23], "subract": 2, "subroutin": [2, 23], "subscript": 3, "subsequ": [3, 6, 7, 8, 14, 18, 20], "subset": [3, 8, 11, 14, 15, 17, 23], "subspac": [2, 10, 13], "substanti": [11, 12], "substep": 13, "substitut": [1, 5, 8, 14, 18], "subsubset": 11, "subtask": 8, "subtl": 3, "subtract": [2, 6, 7, 8, 13, 15, 18, 20], "subtre": 11, "succeed": [2, 6, 23], "success": [5, 9, 11, 15, 20], "successfulli": [6, 11], "sudo": [2, 17, 23], "suffer": [2, 3, 4, 7, 12, 23], "suffici": [3, 8, 10, 13, 15], "suggest": [3, 15, 22], "suit": [10, 14], "suitabl": [0, 2, 20], "sum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_i": [2, 4, 7, 8, 10, 15], "sum_j": 8, "sum_ja_": 2, "sum_k": [8, 10, 14, 18], "sum_logist": 15, "sum_m": 5, "sum_n": 5, "sum_nx_": 5, "summar": [7, 8, 11], "summari": [3, 5, 6, 12, 19], "summat": [1, 2, 5], "sunni": 11, "super": 7, "superfici": 5, "superscript": [3, 14], "supervis": [2, 7, 8, 9, 11, 14, 17, 23], "supplement": 9, "support": [2, 3, 11, 12, 13, 15, 17, 23], "suppos": [2, 7, 8, 9, 10, 12, 13, 14, 15, 18, 23], "suppress": [7, 15], "sure": [1, 2, 3, 6, 8], "surf": 8, "surfac": [2, 8, 23], "surpass": 8, "surpris": [2, 23], "surround": [5, 17], "survei": [2, 7, 8, 23], "svc": [10, 11, 12], "svd": [2, 8, 13, 23], "svdinv": 7, "svm": [10, 11, 12, 13], "svm_clf": [10, 12], "swath": 7, "switch": 2, "sy": 15, "symbol": [3, 7, 13, 15, 17, 20, 23], "symmeteri": 3, "symmetr": [2, 7, 10, 13, 14, 15, 18, 23], "symmetri": 8, "sympi": [2, 17, 23], "synonim": 20, "syntax": 15, "system": [0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 23], "systemat": [6, 8], "t": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "t0": [5, 8, 15], "t1": [4, 15], "t2": 4, "t3": 4, "t_": 4, "t_0": [4, 11, 15], "t_1": 15, "t_b": 12, "t_i": [3, 4, 7, 14], "t_j": 14, "t_k": 11, "tabl": [11, 20, 21, 23], "tabul": [2, 23], "tabular": 23, "tackl": 6, "tag": [4, 5, 6, 7, 8, 9, 14, 15, 16, 18, 20], "taht": [2, 23], "tail": 20, "tailor": [4, 10, 13, 23], "taiwan": [2, 23], "take": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "taken": [2, 3, 5, 8, 12, 15, 18], "tan": 5, "tangent": [3, 6, 14, 15], "tanh": [3, 6, 9, 10, 14], "target": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 23], "target_nam": 11, "task": [2, 3, 5, 8, 11, 13, 14, 16, 23], "tau": [5, 7, 20], "taught": 23, "tax": 2, "taylor": [4, 15], "taylornr": 15, "tc": 10, "teach": [0, 19, 23], "team": 3, "teaser": 2, "technic": [2, 7, 8, 15], "techniqu": [2, 3, 10, 12, 15, 17, 20, 22, 23], "technologi": [2, 3], "tell": [1, 2, 6, 8, 12, 13, 15, 20], "temp": 3, "temp1": 3, "temp2": 3, "temperatur": [2, 11, 23], "temporarili": 3, "ten": [5, 23], "tend": [5, 7, 8, 10, 11, 12, 14, 15, 16], "tendenc": [2, 23], "tension": 8, "tensor": 5, "tensorflow": [2, 4, 6, 10, 16, 17, 18, 22, 23], "term": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "term1": [7, 8, 13], "term2": [7, 8, 13], "term3": [7, 8, 13], "term4": [7, 8, 13], "termin": [0, 2, 6, 7, 11, 12, 15], "terrain": 8, "terrain1": 8, "test": [1, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 20, 23], "test_acc": 5, "test_accuraci": [3, 5], "test_error": 8, "test_imag": [5, 6], "test_ind": 8, "test_input": 6, "test_label": [5, 6], "test_loss": 5, "test_pr": 3, "test_predict": 3, "test_rnn": 6, "test_scor": [9, 12], "test_siz": [0, 2, 3, 5, 7, 8, 12], "test_split": 11, "testerror": [2, 8], "testi": 6, "testpredict": 6, "testx": 6, "text": [2, 3, 4, 6, 7, 10, 11, 13, 15, 18, 20, 22], "textbook": 1, "textual": 11, "textur": 3, "tf": [3, 5, 6, 15, 16], "th": [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 20, 23], "than": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20, 23], "thank": [6, 8], "theano": [3, 17, 23], "thei": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "them": [2, 3, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18, 23], "theme": [0, 2, 23], "themselv": [2, 20, 23], "thenc": 8, "theorem": [4, 8, 9], "theoret": [2, 6, 12], "theori": [2, 3, 5, 10, 11, 14, 15, 17, 22, 23], "thereaft": [2, 7, 8, 13, 14, 18, 23], "therebi": [2, 7, 9, 13, 23], "therefor": [2, 3, 4, 5, 6, 8, 9, 10, 13, 15, 20, 23], "therein": 13, "thereof": [2, 8, 15, 23], "theta": [1, 2, 3, 6, 7, 8, 9, 15, 20, 23], "theta_": [2, 3, 8, 9, 15, 23], "theta_0": [1, 2, 7, 8, 9, 23], "theta_0x_": [2, 23], "theta_1": [2, 7, 8, 9, 23], "theta_1x_": [2, 23], "theta_1x_0": [2, 23], "theta_1x_1": [2, 9, 23], "theta_1x_2": [2, 23], "theta_1x_i": 9, "theta_2": [2, 23], "theta_2x_": [2, 23], "theta_2x_0": [2, 23], "theta_2x_1": [2, 23], "theta_2x_2": [2, 9, 23], "theta_i": [2, 3, 7, 23], "theta_j": [2, 7, 8, 23], "theta_linreg": 15, "theta_p": 9, "theta_px_p": 9, "theta_t": 15, "thetavalu": 7, "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22], "thing": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 23], "think": [2, 3, 5, 6, 8, 11, 14, 15, 16, 20, 23], "third": [2, 5, 8, 15, 21, 23], "thirti": 9, "thorughout": 23, "those": [2, 5, 7, 8, 10, 11, 12, 13, 18, 23], "though": [1, 3, 4, 5, 6, 15, 18, 20], "thought": [8, 16, 20], "thousand": [2, 3], "three": [2, 3, 5, 7, 8, 10, 11, 14, 18, 19, 20, 21, 23], "threshold": [3, 5, 11, 12, 13, 14, 15], "through": [0, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 20, 23], "throughout": [0, 2, 6, 7, 16, 17, 18, 20, 23], "throw": [5, 8, 20], "thu": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 21, 23], "thumb": [2, 8], "tibshirani": [8, 22, 23], "tick_param": 8, "ticker": [8, 15, 20], "tif": 8, "tight_layout": [3, 9], "tightli": 13, "tild": [2, 7, 8, 9, 13, 20, 23], "till": [2, 6, 9, 10, 11, 12, 14, 18, 23], "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "timeit": 6, "timer": 6, "tini": 3, "tip": 5, "titl": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 20, 23], "tmp": 15, "tn": [4, 5, 9], "to_categor": [3, 5, 6], "to_categorical_numpi": 3, "to_numer": [2, 8, 23], "todai": 5, "togeth": [2, 5, 8, 10, 13, 15, 17, 23], "toi": 16, "told": 15, "toler": [4, 16], "tolist": 6, "tomographi": 14, "too": [2, 4, 6, 7, 8, 11, 13, 15, 20, 22], "took": [10, 23], "tool": [0, 2, 3, 5, 8, 15, 17], "toolbox": 10, "top": [2, 5, 7, 8, 11, 12, 17, 23], "topic": [2, 7, 8, 9, 10, 17], "topolog": [5, 14], "topologi": [3, 14], "torkjellsdatt": [21, 23], "toss": [12, 20], "total": [2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 21, 23], "total_loss": 6, "totalclustervari": 16, "totalscatt": 16, "toward": [0, 3, 4, 9, 14, 15], "town": 2, "tp": [6, 9], "tpng": 11, "tpu": [15, 17, 23], "tqdm": 8, "track": [0, 5, 15, 16, 18], "tract": 2, "tractabl": [2, 23], "trade": [7, 11], "tradeoff": [2, 7, 23], "tradit": [2, 3, 6, 8, 23], "train": [1, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15], "train_accuraci": [2, 3, 5, 23], "train_dataset": 6, "train_end": [2, 3], "train_error": 8, "train_imag": [5, 6], "train_ind": 8, "train_label": [5, 6], "train_pr": 3, "train_siz": [2, 3, 5], "train_step": 6, "train_test_split": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "train_test_split_numpi": [2, 3], "trainable_vari": 6, "trained_model": 8, "trainerror": 2, "traini": 6, "training_checkpoint": 6, "training_dataset": 6, "training_gradi": 15, "trainingerror": 8, "trainpredict": 6, "trainscor": 6, "trainx": 6, "trait": [2, 23], "trajectori": 6, "transfer": [11, 23], "transform": [2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "transit": [8, 14], "translat": [3, 6, 8, 12, 23], "transpos": [3, 7, 13, 18], "travers": [2, 7], "treat": [2, 3, 5, 8, 14, 15, 20, 23], "tree": [2, 3, 17, 23], "tree_clf": [11, 12], "tree_clf_": 11, "tree_clf_sr": 11, "tree_reg": 11, "tree_reg1": 11, "tree_reg2": 11, "trend": 20, "treue": 9, "trevor": 22, "tri": [1, 4, 5, 6, 11, 15], "triain": 2, "trial": [2, 4, 6, 8, 15, 20, 23], "triangl": 15, "triangular": 18, "trick": [5, 6, 10, 13, 15, 20], "trickier": 20, "tridiagon": 18, "trillion": 17, "trivial": [2, 3, 7, 13, 20, 23], "troubl": [0, 2, 10, 14], "truck": 5, "true": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "true_fun": 8, "true_theta": 8, "truli": 23, "try": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23], "tucker": 10, "tuesdai": [21, 23], "tumor": [9, 11], "tumour": 9, "tunabl": 3, "tune": [6, 11, 15, 18, 23], "turn": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "tutori": [3, 6], "tv": 4, "tveito": 4, "tweak": [3, 6, 12, 20], "twice": 15, "twist": 13, "two": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23], "tx": 15, "tx_1": 15, "txt": [0, 6], "ty": 15, "type": [2, 3, 5, 8, 10, 12, 15, 18, 20], "typic": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 20, 23], "u": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "u_": 18, "u_i": 14, "u_m": 12, "ua": [2, 23], "ubuntu": [2, 17, 23], "uci": 2, "uio": [0, 21, 22], "un": 16, "unabl": 0, "unari": [18, 23], "unbalanc": [8, 11], "unbias": [2, 7, 8, 23], "uncent": 8, "uncertainti": [2, 7, 23], "uncertitud": 20, "unchang": [3, 5], "uncorrel": [12, 20], "undefin": 7, "under": [2, 3, 7, 8, 12, 15, 17, 23], "underdetermin": [2, 23], "underfit": [3, 8], "underflowproblem": 7, "undergo": 7, "undergradu": [19, 21], "underli": [2, 3, 11, 15, 20, 23], "underset": [6, 16], "understand": [0, 2, 3, 5, 7, 8, 12, 15, 16, 17, 23], "understood": [10, 15], "undesir": 10, "undetermin": [7, 10], "undo": 6, "unexpect": 8, "unexpected": 20, "unfair": 8, "unfortun": [3, 10, 11, 12], "unicode_liter": [10, 11], "uniform": [2, 3, 7, 8, 13, 15, 20, 23], "uniformli": [15, 20], "unifrompdf": 20, "unimport": 15, "union": [7, 8], "uniqu": [2, 4, 8, 15, 16, 18, 23], "unique_cluster_label": 16, "unit": [2, 3, 5, 6, 7, 12, 14, 20, 23], "unitari": [7, 8, 18], "unitarili": [18, 23], "uniti": 20, "univari": 20, "univers": [2, 3, 4, 15, 17, 19, 21, 23], "unix": 3, "unknow": [2, 18, 23], "unknown": [2, 3, 5, 6, 7, 8, 10, 12, 15, 18, 23], "unknowwn": 14, "unlabel": 3, "unless": [2, 5, 8, 13, 15, 23], "unlik": [3, 5, 10, 15], "unnecessarili": 11, "unord": 5, "unravel": 3, "unrol": [5, 13], "unseen": [0, 2, 9, 11], "unstabl": 3, "unsupervis": [2, 3, 6, 14, 17, 23], "unsymmetr": [18, 23], "until": [3, 4, 6, 11, 14, 15, 16], "untouch": 2, "unusu": 14, "up": [1, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21], "updat": [0, 3, 4, 12, 14, 15, 16], "uploa": 23, "upload": [0, 17, 22], "upon": [2, 3, 8, 9, 13, 18], "upper": [1, 2, 10, 11, 18], "uppercas": [18, 23], "upsampl": 6, "upscal": 6, "url": 23, "us": [0, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 22], "usag": [2, 10, 17, 23], "usd": 2, "usd10000": 2, "use_bia": 6, "usecol": [2, 23], "useless": 3, "user": [2, 3, 4, 6, 8, 9, 17, 18, 23], "usernam": 0, "usetex": 20, "usg": 8, "usr": 20, "usual": [2, 5, 6, 9, 14, 15, 16, 23], "ut": 7, "util": [3, 5, 6, 8, 9, 12, 16, 23], "ux": 18, "v": [0, 2, 4, 6, 7, 8, 13, 15, 17], "v0": 20, "v1": 20, "v2": 20, "v_0": 13, "va": 3, "vahid": 23, "val": 15, "val_accuraci": 5, "val_loss": 6, "vale": 4, "valid": [2, 3, 6, 9, 11, 12, 15, 17, 20, 23], "validation_data": 5, "validation_split": 6, "valu": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23], "valuat": 11, "valy": 6, "van": [2, 23], "vandenbergh": [10, 15], "vandermond": [2, 23], "vanilla": [2, 8, 13, 16], "vanish": [3, 6, 15, 20], "var": [7, 8, 12, 13, 20], "var_x": 20, "varabl": 10, "varepsilon": [7, 8], "varepsilon_": [7, 8], "varepsilon_i": [7, 8], "vari": [2, 3, 5, 7, 8, 12, 23], "variabl": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 23], "varianc": [2, 3, 7, 9, 11, 12, 13, 15, 16, 17, 18, 20, 23], "variance_i": [7, 13], "variance_x": [7, 13], "variant": [2, 3, 8, 10, 14, 15, 23], "variat": [5, 6, 13, 23], "varieti": [2, 5, 14, 17, 23], "variou": [1, 3, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "varydimens": 6, "vastli": 5, "vaue": 3, "vault": 2, "vdot": [4, 15], "vec": 8, "vector": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17], "vector_mean": 16, "ventur": [2, 10, 17, 23], "venv": 0, "verbos": [3, 5, 6], "veri": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 22, 23], "verifi": [5, 13, 18, 23], "versatil": [10, 23], "versicolor": [10, 11], "version": [0, 2, 5, 12, 15, 16, 17, 18, 20, 23], "versu": 3, "vert": [1, 2, 3, 7, 8, 9, 10, 11, 13, 15, 23], "vert_1": [7, 8], "vert_2": [7, 8, 13], "via": [2, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 23], "vidal": 13, "video": [2, 3, 14, 17, 19, 21, 23], "view": [3, 5, 7, 8, 14, 15, 20, 22, 23], "violat": 10, "virginica": 11, "viridi": [2, 3, 4, 5, 23], "virtual": 3, "viscos": 15, "viscou": 15, "visibl": 0, "vision": [2, 5], "visual": [2, 5, 13, 14, 17, 23], "visualis": 3, "visualstudio": [0, 1], "viz": [8, 10, 20], "vmap": 15, "vmax": [3, 8], "vmin": [3, 8], "voic": 5, "volum": [2, 5, 23], "vote": [12, 23], "voting_clf": 12, "votingclassifi": 12, "votingsimpl": 12, "vstack": [7, 13, 18, 20, 23], "vt": 7, "w": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "w1": 10, "w2": [10, 13], "w3": 10, "w_": [3, 14], "w_1": [10, 18], "w_1x_": 10, "w_1x_1": 10, "w_2": [10, 18], "w_2x_": 10, "w_2x_2": 10, "w_3": 18, "w_4": 18, "w_hidden": 4, "w_i": [3, 4, 12], "w_ix_i": 14, "w_j": 18, "w_m": 18, "w_output": 4, "w_px_": 10, "w_px_p": 10, "wa": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 16, 18, 23], "wai": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "walk": 11, "walker": 20, "wang": [2, 23], "want": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "warn": [6, 23], "warrant": 8, "wast": 5, "watch": 17, "wave": 5, "wavelet": 10, "we": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "weak": [11, 12, 16], "weather": [3, 14], "web": [17, 19, 21, 23], "webpag": 23, "websit": [8, 18, 19, 23], "wedg": [10, 20], "wednesdai": [21, 23], "wee": 13, "week": [2, 7, 8, 9, 19, 21], "weekli": [0, 1, 17, 19, 21, 22, 23], "weight": [2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 20], "weigth": 4, "welcom": [0, 10, 17], "well": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 22, 23], "went": 10, "were": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 20, 23], "wessel": [2, 23], "what": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "whatev": 5, "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "whenev": [0, 15, 20], "where": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "wherea": [8, 20], "wherein": [3, 14], "whether": [2, 5, 7, 9, 11, 20, 23], "which": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "whichev": [3, 5], "while": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20, 23], "white": 11, "who": [0, 2], "whole": [3, 5, 6, 7, 11, 13, 15], "whose": [2, 8, 12, 20], "whow": 13, "why": [0, 1, 2, 3, 5, 8, 15], "wide": [2, 3, 5, 8, 9, 14, 17, 18, 23], "widehat": 8, "width": [2, 5, 10, 11, 23], "wieringen": [2, 23], "win": 12, "wind": 11, "wing": [21, 23], "winther": 4, "wiothout": 8, "wiscons": 9, "wisconsin": 12, "wisdom": 8, "wise": [2, 3, 7, 14, 15], "wish": [2, 4, 7, 9, 10, 13, 15, 16, 18, 23], "with_std": 2, "wither": 8, "within": [2, 4, 5, 6, 9, 11, 14, 15, 16, 20, 22, 23], "withinclust": 16, "without": [0, 2, 3, 7, 8, 10, 11, 13, 14, 15, 23], "won": [0, 2, 23], "wonder": 10, "word": [2, 3, 5, 6, 7, 8, 9, 16, 20, 23], "work": [0, 1, 2, 3, 6, 8, 9, 10, 11, 15, 17, 19, 20, 21, 23], "workshop": 23, "world": [1, 2, 10], "worldwid": [2, 23], "worri": 0, "wors": [2, 3, 5, 6, 8, 23], "worth": 11, "would": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "wrap": [8, 18, 23], "write": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 18, 23], "written": [1, 2, 4, 5, 7, 13, 14, 15, 17, 18, 20, 23], "wrong": [0, 3, 10], "wrongli": 12, "wrote": [7, 13], "wrt": [12, 15], "wth": [12, 15], "www": [17, 18, 22, 23], "wx_1": 10, "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x0": 10, "x1": [6, 10, 11, 12, 15], "x1_exampl": 10, "x1d": 10, "x2": [10, 11, 12, 15], "x2d": [10, 13], "x2d_train": 13, "x2dsl": 13, "x3": 10, "x_": [2, 4, 5, 7, 8, 10, 12, 13, 15, 16, 18, 20, 23], "x_0": [2, 7, 13, 18, 23], "x_1": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_2": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_3": [10, 18, 20], "x_4": 18, "x_center": 13, "x_data": 3, "x_data_ful": 3, "x_hidden": 4, "x_i": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x_input": 4, "x_ix_": [2, 23], "x_iy_i": 10, "x_j": [1, 2, 4, 10, 11, 14, 20], "x_jy_j": 10, "x_k": [14, 16, 18, 20], "x_l": 20, "x_m": [8, 14, 18, 20], "x_n": [2, 4, 5, 8, 10, 13, 14, 15, 18, 20, 23], "x_new": [11, 12], "x_offset": 8, "x_output": 4, "x_p": [5, 9, 11], "x_poli": 11, "x_poly10": 11, "x_pred": 6, "x_prev": 4, "x_reduc": 13, "x_scale": 10, "x_small": 15, "x_test": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13], "x_test_own": 8, "x_test_scal": [2, 8, 9, 11, 12, 13], "x_tot": 6, "x_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "x_train_mean": 8, "x_train_own": 8, "x_train_scal": [2, 8, 9, 11, 12, 13], "x_val": 3, "xarrai": [17, 23], "xavier": 3, "xbnew": 15, "xcode": [2, 17, 23], "xdclassiffierconfus": 12, "xdclassiffierroc": 12, "xg_clf": 12, "xgb": 12, "xgbclassifi": 12, "xgboost": 11, "xgboot": 12, "xgbregressor": 12, "xgparam": 12, "xgtree": 12, "xi": [10, 15], "xi_": 10, "xi_1": 10, "xi_i": 10, "xk": 10, "xla": [15, 17, 23], "xlabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "xlim": [8, 12], "xm": 11, "xmesh": 15, "xnew": [2, 15, 23], "xp": 20, "xpanda": 2, "xpd": [7, 13], "xplot": 2, "xscale": 2, "xsr": 11, "xt_x": 15, "xtest": 8, "xtick": [5, 8, 10, 11], "xtrain": 8, "xu": [2, 23], "xx": [2, 18, 23], "xy": [2, 8, 10, 18, 23], "xytext": 10, "xz": [18, 23], "y": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "y1": 6, "y2": 6, "y3": 6, "y_": [2, 3, 7, 8, 12, 13, 18, 23], "y_0": [2, 7, 13, 18, 23], "y_1": [2, 7, 10, 11, 13, 15, 18, 23], "y_1y_1": 10, "y_1y_1k": 10, "y_1y_2": 10, "y_1y_2k": 10, "y_1y_n": 10, "y_1y_nk": 10, "y_2": [2, 7, 10, 11, 13, 18, 23], "y_2y_1": 10, "y_2y_1k": 10, "y_2y_2": 10, "y_2y_2k": 10, "y_3": [2, 11, 18], "y_4": 18, "y_data": [2, 3, 7, 8, 23], "y_data_ful": 3, "y_decis": 10, "y_fit": 2, "y_i": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "y_if_": 12, "y_ix_": [2, 23], "y_ix_i": [9, 10, 15], "y_iy_jk": 10, "y_j": [8, 10, 14], "y_k": 14, "y_m": 18, "y_model": [2, 6, 7, 8, 23], "y_n": [10, 15], "y_ny_1": 10, "y_ny_1k": 10, "y_ny_2": 10, "y_ny_2k": 10, "y_ny_n": 10, "y_ny_nk": 10, "y_offset": 8, "y_plot": 11, "y_pred": [2, 3, 6, 8, 9, 10, 11, 12], "y_pred1": 11, "y_pred2": 11, "y_pred_rf": 12, "y_pred_tre": 12, "y_proba": [9, 12], "y_scaler": 8, "y_test": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13], "y_test_onehot": 3, "y_test_predict": 2, "y_tot": 6, "y_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_train_mean": 8, "y_train_onehot": 3, "y_train_predict": 2, "y_train_scal": 8, "y_val": 3, "ye": [5, 8, 9], "year": [2, 17, 23], "yet": [2, 3, 8, 10, 13, 15, 23], "yi": 15, "yield": [2, 4, 7, 8, 10, 12, 14, 15, 16, 18, 20, 23], "yk": 10, "ylabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "ylim": [5, 8], "ym": 11, "ymesh": 15, "yn": 2, "yo": [10, 11, 12], "yoshua": [3, 22], "you": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 21, 22, 23], "young": 2, "your": [0, 3, 4, 6, 7, 8, 10, 13, 15, 17, 18, 23], "your_model_object": 1, "yourself": [13, 15, 23], "youtub": 17, "ypred": 8, "ypredict": [2, 15, 23], "ypredict2": 15, "ypredictlasso": 7, "ypredictol": [2, 7], "ypredictown": 8, "ypredictownridg": 8, "ypredictridg": [2, 7, 8], "ypredictskl": 8, "ytest": 8, "ytick": [5, 8, 10, 11], "ytild": [2, 8, 23], "ytildelasso": 7, "ytildenp": [2, 23], "ytildeol": [2, 7], "ytildeownridg": 8, "ytilderidg": [7, 8], "ytrain": 8, "yuxi": 23, "yx": [18, 23], "yy": [18, 23], "yz": [18, 23], "z": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "z_": [3, 4, 14, 18, 23], "z_0": [18, 23], "z_1": [18, 23], "z_2": [18, 23], "z_c": 3, "z_h": 3, "z_hidden": 4, "z_i": [3, 14], "z_j": [3, 14], "z_k": 14, "z_m": 3, "z_mod": 11, "z_o": 3, "z_output": 4, "zaman": 20, "zaxi": 8, "zero": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "zeros_lik": 6, "zfill": 6, "zip": [6, 8], "zm_h": [2, 23], "zn": 2, "zone": 2, "zoom": 23, "zx": [18, 23], "zy": [18, 23], "zz": [18, 23], "\u00f8yvind": 8}, "titles": ["Exercises week 34", "Exercises week 35", "<span class=\"section-number\">3. </span>Linear Regression", "<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network", "<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning", "<span class=\"section-number\">16. </span>Convolutional Neural Networks", "<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view", "<span class=\"section-number\">4. </span>Ridge and Lasso Regression", "<span class=\"section-number\">5. </span>Resampling Methods", "<span class=\"section-number\">6. </span>Logistic Regression", "<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims", "<span class=\"section-number\">9. </span>Decision trees, overarching aims", "<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)", "<span class=\"section-number\">13. </span>Neural networks", "<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm", "<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning", "Applied Data Analysis and Machine Learning", "<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features", "Course setting", "<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities"], "titleterms": {"": [10, 12], "1": [0, 1, 2], "2": [0, 1, 2, 23], "2023": 21, "3": [0, 1, 2], "34": [0, 23], "35": 1, "4": [0, 1, 2], "5": [1, 2], "A": [2, 3, 6, 10, 11, 23], "And": 23, "In": 21, "Ising": 8, "The": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 17, 23], "To": 23, "With": 6, "about": 23, "activ": [3, 14], "ad": [2, 8, 23], "adaboost": 12, "adagrad": 15, "adam": 15, "adapt": 12, "adjust": 3, "adversari": 6, "again": [5, 11], "ai": 23, "aim": [10, 11, 23], "aka": 23, "algebra": [18, 23], "algorithm": [11, 12, 13, 14, 23], "algortithm": 15, "all": 10, "an": [0, 2, 6, 12, 23], "analys": 7, "analysi": [2, 7, 8, 13, 17, 20, 23], "analyt": [1, 2], "ani": 15, "anoth": 11, "appli": 17, "approach": [2, 10, 16, 23], "approxim": 14, "architectur": 3, "arrai": [18, 23], "assist": 21, "autocorrel": 20, "autograd": [4, 15], "automat": 15, "back": [3, 13, 14], "background": 17, "bag": 12, "base": 15, "basic": [2, 7, 9, 11, 12, 13, 18], "batch": 3, "bay": 7, "befor": 13, "better": 10, "bia": 8, "binari": 3, "bind": 23, "bird": 12, "boost": 12, "bootstrap": [8, 12], "boston": 2, "breast": 3, "brief": 23, "bring": 14, "build": [3, 5, 11], "c": 23, "can": 23, "cancer": [3, 9, 11, 13], "cart": 11, "case": [10, 12, 20], "central": [15, 17, 20], "chain": 14, "chang": 12, "channel": 23, "chi": [2, 23], "choos": 3, "cifar01": 5, "classic": 13, "classif": [3, 11, 12], "classifi": 10, "clip": 3, "cluster": 16, "cnn": 5, "code": [0, 1, 2, 3, 4, 7, 11, 13, 14, 15, 16, 23], "collect": [3, 5], "commun": 23, "compar": [1, 4, 12], "complex": [2, 8], "complic": 8, "compon": 13, "comput": 11, "computerlab": 23, "con": 11, "concept": 20, "conjug": 15, "contn": 23, "convex": [10, 15], "convolut": [5, 14], "correl": 13, "cost": [3, 12], "cours": [17, 19, 22, 23], "covari": [7, 13, 20], "cover": 23, "creat": 1, "cross": 8, "cython": 23, "data": [0, 2, 3, 5, 8, 9, 11, 13, 17, 20, 23], "dataset": [3, 5], "david": 23, "deadlin": 23, "deadllin": 21, "decai": 4, "decis": [11, 12], "decomposit": [7, 13, 18], "deeep": 23, "deep": [3, 4, 23], "defin": [3, 23], "degre": 2, "deliver": [0, 1], "dens": 2, "deriv": [1, 7, 14], "descent": [4, 12, 15], "detail": [5, 23], "develop": 3, "diagon": 13, "differ": 10, "differenti": [4, 15], "diffus": 4, "dimension": [4, 5, 10], "disadvantag": 11, "discret": 20, "discrimin": 23, "distribut": [7, 20], "do": 3, "domain": 20, "down": 3, "dropout": 3, "element": [2, 20, 23], "elimin": 18, "energi": 23, "ensembl": 12, "entropi": 11, "environ": [0, 2], "equat": [2, 4, 14], "error": [2, 12, 23], "essenti": 23, "etc": 23, "euler": 4, "evalu": 3, "exampl": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "exercis": [0, 1, 2, 8], "expect": 20, "experi": 20, "explor": 2, "exponenti": 4, "express": 1, "extrapol": 6, "extrem": [12, 23], "ey": 12, "fall": 21, "famili": [3, 23], "famou": 18, "featur": [1, 11, 18], "feed": [3, 14], "final": 14, "find": 1, "fine": 3, "first": [6, 14, 23], "fit": [0, 1, 2, 12, 23], "forc": 5, "forest": 12, "format": 23, "forward": [3, 4, 14], "foster": 23, "fourier": 5, "frank": 8, "freedom": 2, "frequentist": [2, 23], "from": [7, 12, 14, 23], "full": 4, "function": [2, 3, 8, 9, 10, 12, 13, 14, 15, 20, 23], "further": [5, 7], "gan": 6, "gaussian": 18, "gd": 15, "gener": [6, 11, 23], "geometr": 13, "gini": 11, "github": 0, "goal": [0, 1], "good": [2, 23], "grade": [21, 23], "gradient": [3, 4, 12, 15], "growth": 4, "ha": 17, "handl": [18, 23], "hidden": 4, "hous": 2, "how": 1, "hyperparamet": 3, "hyperplan": 10, "i": [2, 3, 23], "id3": 11, "idea": 13, "ii": 23, "implement": [1, 3], "implic": 7, "import": [7, 18, 23], "improv": 3, "includ": 15, "increment": 13, "index": 11, "inform": 21, "input": 4, "instal": [17, 23], "instructor": 21, "interpret": [7, 13, 23], "introduc": [13, 15], "introduct": [2, 8, 17, 18, 23], "invers": [7, 18], "iter": 12, "jax": 15, "julia": 23, "jungl": 12, "kera": [3, 5], "kernel": [10, 13], "lagrangian": 10, "lasso": [7, 8], "later": 7, "layer": [3, 4, 5, 14], "learn": [0, 1, 2, 3, 4, 13, 15, 16, 17, 23], "least": [1, 7, 8, 23], "lectur": 23, "level": 12, "librari": [17, 23], "likelihood": 9, "limit": [3, 15, 20], "linear": [0, 2, 10, 15, 18, 23], "link": [7, 13, 22], "logist": [9, 23], "lu": 18, "machin": [2, 10, 15, 17, 23], "main": [20, 23], "make": [2, 11, 12], "mani": [12, 14], "mass": 23, "materi": 23, "math": 7, "mathemat": [5, 7, 10], "matric": [7, 18, 23], "matrix": [1, 3, 7, 13, 14, 18, 23], "matter": 2, "mean": 2, "meet": [7, 12, 20, 23], "mercer": 10, "method": [8, 11, 12, 15, 23], "minim": 23, "ml": 23, "mlp": 14, "mnist": [5, 6], "model": [0, 2, 3, 6, 8, 14, 23], "momentum": 15, "moon": [10, 11], "more": [5, 8, 18, 23], "multilay": 14, "multipl": [3, 5], "multipli": 10, "need": 23, "network": [3, 4, 5, 6, 9, 14, 23], "neural": [3, 4, 5, 6, 9, 14, 23], "new": 6, "non": 10, "normal": [2, 3], "notat": 14, "now": [3, 11, 15], "nuclear": [2, 23], "numba": 23, "number": [2, 4, 20], "numer": [4, 20], "numpi": [18, 23], "object": 5, "obtain": 13, "od": 4, "off": 8, "ol": [0, 1, 7, 8], "one": [4, 14], "oper": 18, "optim": [3, 10, 15, 17, 23], "order": 15, "ordinari": [1, 7, 8, 23], "organ": [2, 23], "oslo": 22, "other": [6, 11, 13, 14, 18, 23], "our": [2, 6, 7, 13, 15, 23], "outcom": [17, 23], "output": 4, "overarch": [2, 6, 10, 11, 23], "overview": [12, 23], "own": [2, 12, 13, 23], "packag": [18, 23], "panda": 23, "paramet": 23, "part": [15, 17], "partial": 4, "pass": 3, "pca": 13, "pdf": 20, "perceptron": 14, "perform": [3, 11], "period": 5, "perspect": 3, "plethora": 23, "point": 6, "poisson": 4, "polynomi": [1, 5], "popul": 4, "popular": 23, "practic": [15, 21, 23], "pre": [3, 5], "predict": 6, "prerequisit": [5, 17, 23], "princip": 13, "principl": 5, "pro": 11, "probabl": [7, 20], "problem": [3, 4, 15, 23], "procedur": [11, 23], "process": [3, 5], "program": [4, 15], "project": [8, 21, 23], "prop": 15, "propag": [3, 14], "properti": [7, 20], "python": [0, 2, 11, 17, 18, 23], "quick": 10, "r": 23, "random": [12, 13, 20], "read": [11, 23], "real": [8, 23], "recommend": 23, "recurr": [6, 14], "reduc": 2, "reduct": 5, "reformul": 4, "regress": [0, 2, 7, 8, 9, 11, 12, 15, 23], "regular": 3, "relev": 22, "relu": 3, "remark": 5, "remind": [8, 10, 23], "replac": 15, "repositori": 0, "requir": [4, 17], "resampl": 8, "rescal": 8, "resourc": 4, "revisit": 15, "rewrit": 23, "ridg": [2, 7, 8], "rm": 15, "rule": 14, "same": 15, "sampl": 13, "schedul": 23, "schemat": 11, "scheme": 4, "scienc": 23, "scikit": [2, 3, 13, 23], "second": 15, "semest": 21, "set": [0, 2, 4, 5, 11, 14, 19, 23], "setup": 0, "sgd": 15, "should": 3, "similar": 15, "simpl": [2, 6, 11, 15, 23], "singl": 12, "singular": [7, 13], "sklearn": 1, "soft": 10, "softmax": 3, "softwar": 23, "solv": 4, "solver": 15, "some": [15, 18], "specifi": 4, "split": [0, 2], "squar": [1, 2, 7, 8, 12, 23], "standard": 15, "state": 2, "statist": [7, 8, 17, 20, 23], "steepest": [12, 15], "stochast": [15, 20], "strongli": 23, "suggest": 23, "summari": [21, 23], "superposit": 5, "supervis": 3, "support": 10, "svd": 7, "systemat": 5, "take": 1, "taken": 23, "teach": 21, "teacher": [21, 23], "techniqu": [8, 13], "technologi": 17, "tensorflow": [3, 5], "tent": [21, 23], "test": [0, 2, 3], "text": 23, "textbook": [22, 23], "theorem": [7, 10, 13, 14, 20], "theori": 20, "thi": 23, "tip": 15, "togeth": 14, "tool": 23, "top": 3, "topic": 23, "toward": 13, "trade": 8, "tradeoff": 8, "train": [0, 2, 3, 6, 23], "transform": 5, "tree": [11, 12], "tune": 3, "two": [5, 10, 17], "type": [4, 6, 14, 23], "uio": 23, "univers": [14, 22], "unsupervis": 16, "up": [0, 2, 4, 11, 14, 23], "us": [1, 2, 3, 4, 5, 9, 15, 17, 23], "v": 5, "valid": 8, "valu": [7, 13, 20], "variabl": 20, "varianc": 8, "variou": 2, "vector": [1, 10, 14, 18, 23], "versu": 23, "view": [2, 6, 12], "virtual": 0, "visual": [3, 11], "wai": 11, "wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12]}})
\ No newline at end of file
+Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare  Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Discriminative Modeling": [[23, "discriminative-modeling"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example of discriminative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise:  Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function  with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function  with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Generative Versus Discriminative Modeling": [[23, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[3, "layers"]], "Layers used to build CNNs": [[5, "layers-used-to-build-cnns"]], "Learning goals": [[0, "learning-goals"], [1, "learning-goals"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[3, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[2, null]], "Linear Regression, basic elements": [[2, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[7, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[7, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[7, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[9, null], [9, "id1"]], "MNIST and GANs": [[6, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[11, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[12, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[7, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[10, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[5, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[7, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix  multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation  and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the  covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning  at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning  at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[13, "other-techniques"]], "Other types of networks": [[14, "other-types-of-networks"]], "Other ways of visualizing the trees": [[11, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[13, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[4, "partial-differential-equations"]], "Practical tips": [[15, "practical-tips"]], "Practicalities": [[21, "practicalities"], [21, "id1"]], "Predicting New Points With A Trained Recurrent Neural Network": [[6, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[5, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[15, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[11, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[15, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[12, "random-forests"]], "Randomized PCA": [[13, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[14, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[6, null]], "Reducing the number of degrees of freedom, overarching view": [[2, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[4, "reformulating-the-problem"]], "Regression Case": [[12, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[3, "regularization"]], "Reminder on Statistics": [[8, "reminder-on-statistics"]], "Replace or not": [[15, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[8, null]], "Resampling methods": [[8, "id1"]], "Resources on differential equations and deep learning": [[4, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[15, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[7, null], [7, "id1"]], "Same code but now with momentum gradient descent": [[15, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[11, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[14, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[4, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[15, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[11, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[2, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations  with Deep Learning": [[4, null]], "Solving the one dimensional Poisson equation": [[4, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[4, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[15, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[2, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[15, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[15, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[15, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[10, null]], "Systematic reduction": [[5, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[21, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[2, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[13, "the-algorithm-before-theorem"]], "The Boston housing data example": [[2, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[3, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[11, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[11, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[5, "the-cifar01-data-set"]], "The MNIST dataset again": [[5, "the-mnist-dataset-again"]], "The RELU function family": [[3, "the-relu-function-family"]], "The Softmax function": [[3, "the-softmax-function"]], "The \\chi^2 function": [[2, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[8, "the-bias-variance-tradeoff"]], "The code for solving the ODE": [[4, "the-code-for-solving-the-ode"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[9, "the-logistic-function"]], "The moons example": [[10, "the-moons-example"]], "The multilayer  perceptron (MLP)": [[14, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[4, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The plethora  of machine learning algorithms/methods": [[23, "the-plethora-of-machine-learning-algorithms-methods"]], "The singular value decomposition": [[7, "the-singular-value-decomposition"]], "The two-dimensional case": [[10, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[13, "towards-the-pca-theorem"]], "Train and test datasets": [[3, "train-and-test-datasets"]], "Two-dimensional Objects": [[5, "two-dimensional-objects"]], "Type of problem": [[4, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[15, "using-autograd"]], "Using forward Euler to solve the ODE": [[4, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[15, "using-gradient-descent-methods-limitations"]], "Visualization": [[3, "visualization"], [3, "id1"]], "Visualizing the Tree, Classification": [[11, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "What Is Generative Modeling?": [[23, "what-is-generative-modeling"]], "What is Machine Learning?": [[2, "what-is-machine-learning"]], "What is a good model?": [[2, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[23, "what-is-a-good-model-can-we-define-it"]], "Which activation function should I use?": [[3, "which-activation-function-should-i-use"]], "Why Linear Regression (aka Ordinary Least Squares and family)": [[23, "why-linear-regression-aka-ordinary-least-squares-and-family"]], "Wisconsin Cancer Data": [[9, "wisconsin-cancer-data"]], "Writing Our First Generative Adversarial Network": [[6, "writing-our-first-generative-adversarial-network"]], "Writing our own PCA code": [[13, "writing-our-own-pca-code"]], "XGBoost: Extreme Gradient Boosting": [[12, "xgboost-extreme-gradient-boosting"]], "scikit-learn implementation": [[3, "scikit-learn-implementation"]]}, "docnames": ["E1", "E2", "chapter1", "chapter10", "chapter11", "chapter12", "chapter13", "chapter2", "chapter3", "chapter4", "chapter5", "chapter6", "chapter7", "chapter8", "chapter9", "chapteroptimization", "clustering", "intro", "linalg", "schedule", "statistics", "teachers", "textbooks", "week34"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["E1.ipynb", "E2.ipynb", "chapter1.ipynb", "chapter10.ipynb", "chapter11.ipynb", "chapter12.ipynb", "chapter13.ipynb", "chapter2.ipynb", "chapter3.ipynb", "chapter4.ipynb", "chapter5.ipynb", "chapter6.ipynb", "chapter7.ipynb", "chapter8.ipynb", "chapter9.ipynb", "chapteroptimization.ipynb", "clustering.ipynb", "intro.md", "linalg.ipynb", "schedule.md", "statistics.ipynb", "teachers.md", "textbooks.md", "week34.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 23], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "00": [2, 3, 7, 13, 23], "000": [3, 5], "00000000e": [], "001": [4, 10, 15], "004": 7, "00727646693": [2, 23], "0086649156": [2, 23], "01": [2, 3, 4, 7, 11, 13, 15, 22, 23], "0110": 20, "01719003e": [], "02": [2, 6, 9, 14, 23], "02334824": [], "02857": 6, "02f": 8, "03077640549": 6, "03097597e": [], "031": 7, "04": 13, "0458": 11, "05": [6, 8], "062292565": 6, "062435": [], "06730814": [], "07": [], "0713": [2, 23], "07285": 5, "08": 20, "08078025e": [], "08336233266": 6, "0917": 11, "0n": [2, 23], "1": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23], "10": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23], "100": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "1000": [2, 3, 4, 6, 7, 10, 13, 15, 16, 17, 20, 23], "10000": [4, 7, 8, 12, 13, 15, 20], "100000": 10, "10001": 12, "1001": 20, "1002": 20, "1003": 20, "1005": 20, "1009": 20, "101": 1, "1011": 20, "1013": 20, "1013904243": 20, "1015": 20, "102": 1, "1023": 20, "1024": 5, "1026": 20, "1027": 20, "103": 3, "1030": 20, "1037": 20, "1038": 20, "1040": 20, "1047": 20, "107": 1, "108": [], "10th": 11, "10x": [2, 23], "11": [1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 22, 23], "110": [], "1100": 20, "1101": 20, "111": [3, 9, 14], "112": 1, "11340253": [], "11590451": [], "116": 1, "117": 1, "118": 1, "12": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 18, 20, 22, 23], "120": 5, "121": [1, 10, 11, 12], "1215pm": [21, 23], "122": [10, 11, 12], "124": [2, 23], "125": 1, "127": [1, 6], "128": [5, 6, 15], "129": 1, "1298": 11, "12pm": [21, 23], "13": [2, 4, 11, 14, 18, 20, 23], "131": 1, "133": 9, "135": 1, "136": 1, "14": [2, 4, 6, 8, 10, 11, 12, 14, 18, 20, 22], "141": 1, "143": 1, "1446729567": 6, "149": 1, "14g": 8, "15": [2, 4, 6, 8, 9, 10, 11, 14, 15, 20, 23], "150": [6, 10], "152": 1, "153760": [], "156": 1, "157": [], "158": [], "159": 1, "15g": 8, "15pm": 23, "16": [3, 4, 5, 6, 7, 10, 11, 12, 20, 23], "160": 1, "1603": 5, "161": 1, "162": 1, "16231451": 6, "163": 1, "16384": 5, "164": 1, "167": 1, "17": [3, 4, 10, 20], "172": 1, "173": 1, "176": 1, "178": 1, "179": 1, "1797": 3, "18": [4, 8, 9, 10, 11, 12, 20, 23], "1807": 6, "18392847": [], "19": [4, 20, 23], "1940": 2, "1943": 14, "1970": [18, 23], "1973": 11, "1979": 8, "1_1": 14, "1_2": 14, "1_3": 14, "1cm": [2, 10, 12, 20, 23], "1d": [3, 4, 5], "1e": [4, 6, 15, 16], "1e10": 16, "1e4": 8, "1f": 3, "1k": 18, "1n": [2, 23], "1x": [2, 23], "2": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "20": [1, 2, 3, 4, 8, 9, 10, 20, 21, 23], "200": [2, 4, 5, 6, 10, 11, 12], "2000": 2, "2004": 15, "2006": 22, "20072279": [], "2008": 23, "2010": 3, "2011": 3, "2014": 6, "2015": 3, "2016": [2, 23], "2018": [2, 8], "2021": [8, 16], "2022": 23, "2025": 23, "21": [2, 3, 7, 9, 11, 14, 18, 23], "2116753732": 6, "215pm": [21, 23], "2167072": [], "22": [2, 3, 7, 14, 15, 18, 23], "221": 10, "225": 6, "22948497": [], "23": [3, 14, 18], "24": [2, 3, 18, 23], "25": [4, 5, 6, 7, 8, 10, 11, 13], "250": [4, 6, 9, 11], "25000": 2, "250154": [], "253775": [], "255": 5, "256": 6, "26": [], "26303845": [], "264": [], "265": [], "265109911": 6, "266": [], "269": [], "27": [2, 3], "270": [], "27n_": 20, "28": [3, 5, 6], "2830637392": 6, "2861": 20, "2873": 11, "2882": 20, "2886": 20, "2890": [2, 23], "2892": 20, "29": [], "2915": 20, "2931": 23, "29364655": [], "294399745619595": [], "296247": [], "2968": 23, "2980": 23, "298273": [], "298375": [], "2990": 23, "2_": 14, "2_1": 14, "2_2": 14, "2_3": 14, "2_i": 14, "2_m": [8, 20], "2_t": 15, "2_x": 20, "2b": 20, "2cm": 10, "2d": [3, 5, 13, 14, 17, 23], "2e": 8, "2f": [2, 9, 11, 12, 13, 14, 23], "2g": 4, "2g_i": 4, "2k": 5, "2m": 8, "2n": [2, 4, 5, 23], "2nd": 11, "2p": 20, "2pt": 6, "2x": [2, 5, 10, 15, 23], "2x_ix_jy_iy_j": 10, "2x_j": 10, "2y_i": 12, "2y_j": 10, "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "30": [2, 3, 6, 8, 9, 12, 15, 21], "30000": [2, 23], "3072": 5, "31": [14, 18, 20], "315": 8, "3155": [2, 7, 8], "32": [5, 6, 8, 14, 15, 18, 20], "3200": 3, "3250": 3, "3297": [], "33": [14, 18, 21], "3303": [], "3310": [], "332331": [], "333": 9, "3331": [], "3337": [], "34": 18, "3436": [2, 23], "3437": [2, 23], "35": [2, 8, 23], "3581341341": 6, "359": 7, "36": [2, 7, 8, 20], "370782966": 6, "38": 20, "39": [2, 21, 23], "3d": [1, 4, 5, 6, 8, 15], "3f": [3, 5, 11], "3n": 18, "3x": [4, 10], "3x_i": 4, "3y": 10, "4": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "40": [3, 8, 21, 23], "400": 6, "4000": 23, "4050": [22, 23], "41": 18, "4155": [0, 4], "41589548": [], "42": [3, 6, 10, 11, 12, 18], "43": [2, 9, 18], "4310": 23, "436462435": 6, "44": [2, 18], "45": [21, 23], "46": [21, 23], "462": 9, "47": [21, 23], "479465113": 6, "47958494": [], "48": [], "48257387": [21, 23], "49": [7, 8, 13], "49152": 5, "4940954": [2, 23], "4990": 20, "4992": 20, "4997": 20, "4c4c7f": [11, 12], "4d": 5, "4f": 8, "4pm": [21, 23], "4y": 10, "4y_i": 12, "5": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "50": [3, 4, 5, 6, 8, 9, 10, 12, 15, 23], "500": [3, 5, 6, 8, 11, 12, 15], "5018": 20, "506": 2, "507d50": [11, 12], "50j": 15, "50x10": 3, "51": 12, "510": 3, "512132": [], "5177783846": 6, "53": 11, "54": [8, 20], "5411205": [], "54894451": [], "55": 3, "56": 3, "56536": [2, 23], "569": 3, "57": [2, 10, 21, 23], "571": 7, "58": [12, 21, 23], "591317992": 6, "5cm": 20, "5f": 10, "5x": 10, "5y": 10, "6": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 21, 23], "60": [3, 5], "60000": 6, "6019067271": 6, "606439": [], "625": 9, "63": [2, 3], "64": [3, 5, 6, 15, 18, 23], "64x50": 3, "65": [3, 10, 11], "6887363571": 6, "69": [1, 20], "69069n_": 20, "691": [], "6n_": 20, "7": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "70": [3, 9], "70653767": 6, "71": 3, "724": 5, "73": [], "7304881": [], "75": [7, 8, 10, 13], "76": [21, 23], "765": 9, "77": [21, 23], "7718": 11, "7782028952": 6, "77893972": [], "78": [], "7d7d58": [11, 12], "8": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 21, 23], "80": [2, 3, 7, 10], "800": [6, 9], "81": 3, "815am": [21, 23], "85": 3, "8702784034": 6, "88": 23, "8f": 8, "8g": 8, "8n": 18, "8x8": 3, "9": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "90": 3, "9040": 11, "91": [21, 23], "92": [21, 23], "93": 1, "931": [2, 23], "933": 7, "937": 20, "938": 20, "939": [2, 20, 23], "94": 20, "95": [3, 13], "954": 20, "955820c21e8b": 6, "96": 8, "960": 20, "961": 20, "962": 20, "9649652536": 6, "96611194e": [], "9780387310732": 22, "9780387848570": 22, "9781098134174": 23, "9781492032632": 22, "9781801819312": 23, "98": [1, 2, 3], "985": 20, "986": 20, "989": 20, "9898ff": [11, 12], "99": [1, 15], "991": 20, "992": 20, "993": 20, "996": 7, "999": [11, 20], "9x": 8, "9y": 8, "A": [0, 1, 4, 5, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22], "AND": 4, "And": [2, 5, 6, 7, 8, 11, 15, 17, 20], "As": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 18, 20, 23], "At": [2, 6, 8, 15, 23], "BE": [2, 23], "Be": [4, 17, 23], "Being": 15, "But": [1, 2, 3, 4, 5, 7, 8, 11, 12, 20], "By": [2, 5, 7, 8, 14, 15, 18, 23], "For": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "IF": 8, "IN": 22, "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "Ising": [7, 14], "It": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "Its": [3, 4, 6, 13], "No": [8, 11, 23], "Not": [2, 3, 7, 8], "OR": 20, "Of": 20, "On": [2, 5, 20, 21, 22, 23], "One": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 20], "Or": [2, 3, 8, 23], "Such": [1, 2, 8, 14, 20], "That": [2, 7, 9, 12, 13, 14, 16, 20, 23], "The": [1, 6, 12, 15, 16, 18, 19, 20, 21, 22], "Then": [0, 1, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "There": [0, 2, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 21, 23], "These": [2, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20], "With": [1, 2, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 23], "_": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "_0": [7, 10, 12, 13, 15], "_1": [4, 7, 8, 10, 12, 13, 14, 15, 16, 18], "_2": [4, 7, 10, 13, 14, 15, 18], "_3": 18, "_4": 18, "_9": 15, "__class__": 12, "__doc__": 8, "__future__": [10, 11], "__init__": 3, "__main__": 4, "__name__": [4, 12], "_auto1": [4, 5, 6, 7, 8, 9, 14, 15, 18, 20], "_auto10": [8, 14], "_auto11": 8, "_auto12": 8, "_auto2": [4, 5, 6, 7, 8, 14, 15, 18, 20], "_auto3": [5, 6, 7, 8, 14, 15, 18], "_auto4": [6, 8, 14, 15, 18], "_auto5": [6, 8, 14, 15, 18], "_auto6": [6, 8, 14, 18], "_auto7": [6, 8, 14, 18], "_auto8": [8, 14], "_auto9": [8, 14], "_build": [2, 17, 22, 23], "_c": 3, "_compon": 13, "_depth": 11, "_export": [0, 1], "_fraction": 11, "_i": [2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 23], "_j": [2, 3, 4, 5, 7, 8, 10, 15], "_k": 15, "_l": 14, "_lambda": 8, "_leaf": 11, "_m": 12, "_multilayer_perceptron": [], "_n": [4, 7, 10, 13, 15], "_node": 11, "_p": [7, 10], "_ratio": 13, "_sampl": 11, "_split": [8, 11], "_t": 15, "_test": 8, "_varianc": 13, "_weight": 11, "a0": 5, "a0faa0": [11, 12], "a1": [2, 23], "a2": [2, 23], "a3": [2, 23], "a4": [2, 23], "a_": [1, 2, 3, 18, 23], "a_0": [2, 23], "a_1a": [2, 23], "a_2a": [2, 23], "a_3": [2, 23], "a_3a": [2, 23], "a_4": [2, 23], "a_4a": [2, 23], "a_h": 3, "a_i": [2, 3, 4, 14, 23], "a_j": [3, 14], "a_k": [2, 3, 14], "aaron": 22, "ab": [2, 4, 7, 15, 16, 23], "ab_channel": 17, "abandon": 3, "abid": 20, "abil": [2, 12], "abl": [1, 2, 3, 6, 7, 8, 9, 12, 14, 15], "about": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21], "abov": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "abovement": [8, 23], "abscissa": 15, "absolut": [2, 4, 7, 8, 15, 23], "abstract": 3, "acceler": 15, "accept": [2, 5, 8, 11], "access": [2, 5, 13, 20, 23], "accid": [6, 8], "accompani": [2, 23], "accomplish": [10, 11, 15], "accord": [2, 3, 4, 7, 8, 11, 14, 15, 16, 20, 23], "accordingli": 13, "account": [0, 1, 2, 5, 7, 15, 20, 23], "accumul": [14, 15, 20], "accur": [2, 5, 6, 8, 12, 15], "accuraci": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 23], "accuracy_scor": [2, 3, 12, 23], "accuracy_score_numpi": 3, "achiev": [2, 3, 7, 8, 10, 14, 18, 23], "aco": 20, "acquaint": 17, "acquir": [3, 17, 23], "acr": 2, "across": [3, 5, 8, 11, 17, 23], "act": [3, 5, 18], "action": 20, "activ": [0, 2, 4, 5, 6, 11, 19, 21, 23], "actual": [0, 1, 2, 3, 6, 7, 8, 10, 13, 18, 20, 23], "ad": [0, 1, 3, 5, 6, 7, 10, 15, 18], "ada_clf": 12, "adaboostclassifi": 12, "adadelta": 15, "adam": [3, 5, 6, 23], "adapt": [6, 8, 15, 22], "add": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 20, 21, 23], "add_subplot": [3, 9, 14, 16], "addendum": 7, "addit": [0, 2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23], "addition": [14, 15], "address": [3, 11, 13, 15, 23], "adjac": [5, 14], "adjoint": 7, "adjust": [2, 7, 14, 15], "admir": [2, 23], "advanc": [6, 8, 14, 22, 23], "advantag": [3, 5, 7, 8, 12, 15, 18], "adversari": 23, "afecionado": 23, "affect": [0, 5], "affin": [2, 5, 10, 13], "afford": 5, "aficionado": 23, "aforement": 16, "african": 2, "after": [0, 1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 17, 18, 20, 23], "afterward": [2, 23], "ag": [2, 9, 23], "ag_0": 4, "again": [2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "against": [3, 6, 9, 12], "agegroup": 9, "agegroupmean": 9, "aggreg": [11, 12], "agorithm": 12, "agre": [7, 8, 20], "agreement": 15, "ahead": 11, "ai": [2, 22], "aid": 13, "aim": [1, 2, 3, 6, 8, 9, 13, 16, 17, 18], "ainv": 7, "airplan": 5, "aka": 7, "al": [1, 2, 4, 6, 22, 23], "alarm": [7, 9], "algebra": [2, 5, 7, 15, 17], "algorithm": [1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 16, 17, 18, 20, 22], "align": [2, 4, 7, 8, 9, 10, 15, 20, 23], "all": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "allevi": [3, 15], "alloc": [5, 18], "allow": [2, 3, 4, 5, 7, 8, 10, 12, 15, 17, 18, 23], "almost": [2, 3, 8, 10, 13, 15, 20], "alon": [4, 11], "along": [0, 4, 5, 6, 7, 8, 11, 12, 13, 17, 18, 23], "alpha": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 23], "alpha_": 12, "alpha_0": 5, "alpha_1": 5, "alpha_2": 5, "alpha_i": [5, 15], "alpha_k": 15, "alpha_m": 12, "alpha_n": 5, "alpha_opt": 15, "alreadi": [0, 4, 5, 6, 7, 8, 12, 14, 17, 18, 20, 23], "also": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "alter": 3, "altern": [2, 3, 6, 7, 8, 10, 11, 13, 15, 18, 23], "although": [1, 2, 3, 7, 8, 10, 12, 15, 23], "alwai": [1, 2, 5, 7, 8, 14, 15, 20, 23], "am": 6, "ame2016": [2, 23], "american": 2, "among": [2, 5, 7, 11, 12, 14, 18, 23], "amongst": 7, "amount": [2, 3, 5, 6, 8, 10, 12, 16, 17], "an": [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22], "an_": 20, "anaconda": [2, 3, 17, 23], "analogi": 15, "analys": 8, "analysi": [3, 5, 6, 9, 16, 18, 22], "analyt": [4, 5, 7, 8, 9, 14, 15, 17, 23], "analyz": [1, 2, 3, 5, 6, 7, 8, 20], "andrew": 3, "angl": [2, 5, 11], "anharmon": 5, "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 23], "anim": [6, 14], "ann": 14, "annot": [2, 3, 5, 9, 10, 23], "announc": 23, "anoth": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "ansatz": [2, 23], "answer": [2, 3, 5, 7, 8, 18, 21, 23], "antialias": [4, 8], "anticip": 6, "anymor": [3, 10], "anyon": [0, 6, 10], "anyth": [0, 1, 3, 20], "anytim": [21, 23], "apach": 3, "apart": [13, 15], "api": [3, 17, 23], "appar": 4, "appear": [2, 3, 5, 15, 18, 20], "append": [3, 5, 6, 10, 11, 15, 23], "appli": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 20, 22, 23], "applic": [1, 2, 3, 5, 6, 7, 8, 9, 11, 14, 15, 18, 20, 22, 23], "apply_gradi": 6, "approach": [0, 1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 22], "appropri": [4, 8, 11, 14, 15, 17, 20], "approv": 23, "approx": [2, 4, 5, 8, 12, 13, 15, 20, 23], "approxim": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 15, 20, 23], "apt": [2, 17, 23], "aq": 20, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "aragorn": 23, "arang": [3, 5, 6, 8, 9, 11, 12, 14, 15, 23], "arbitrari": [3, 6, 8, 10, 14, 15, 20], "arbitrarili": [2, 3, 13, 23], "arc": 8, "architectur": [5, 6, 14], "area": [2, 5, 8, 22, 23], "argmax": [3, 13], "argmin": [6, 12, 16], "argsort": 13, "argu": [3, 15], "argument": [2, 4, 5, 7, 13, 14, 15, 23], "aris": [2, 8, 14, 15, 20, 23], "arithmet": [2, 15, 18, 23], "arm": 8, "armadillo": 18, "around": [2, 3, 6, 7, 8, 13, 20, 23], "arrai": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20], "arrang": [5, 23], "arraybox": 15, "arriv": [2, 8, 11, 13, 18, 20, 23], "arrow": 14, "arrowprop": 10, "art": [2, 3, 17], "articl": [2, 5, 6, 8, 12, 23], "artifici": [2, 4, 9, 14, 22, 23], "artificialneuron": 14, "arug": 15, "arxiv": [5, 6], "asarrai": [2, 8, 11], "ask": [0, 7, 8, 13, 14], "aspect": [2, 8, 17, 23], "assembl": 5, "assembli": [2, 23], "assert": 6, "assess": [2, 8, 23], "assici": 6, "assign": [0, 2, 9, 10, 11, 14, 15, 16, 19, 21, 22, 23], "associ": [2, 8, 11, 14, 16, 20, 23], "assum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "assumpt": [2, 5, 7, 8, 11, 13, 20, 23], "ast": [2, 7, 8, 23], "astyp": [6, 11, 12], "asymmetri": [2, 23], "asymptot": [6, 8], "atom": [2, 23], "attempt": [2, 6, 8, 9, 10, 12, 23], "attend": 23, "attent": [2, 18, 23], "attract": [2, 12, 23], "attribut": [2, 11, 23], "audi": [2, 23], "audio": [5, 6], "august": 23, "aurelien": [2, 22, 23], "austfjel": 8, "auth": 0, "authent": 0, "author": [2, 3, 12, 20], "authour": 23, "auto": [11, 12, 20], "autocor": 20, "autocorrelation_tim": 20, "autocorrelform": 20, "autocovari": 20, "autoencod": [6, 17, 23], "autoencond": 17, "autograd": [17, 23], "autom": [2, 17, 22, 23], "automac": 18, "automag": 23, "automat": [1, 2, 3, 4, 5, 6, 13, 17, 18, 23], "automobil": 5, "autonom": 6, "avail": [2, 3, 6, 8, 12, 13, 17, 18, 19, 21, 22, 23], "averag": [2, 3, 5, 8, 11, 12, 15, 16, 20, 21, 23], "avoid": [2, 6, 7, 8, 11, 13, 15, 18], "awai": [4, 5, 8], "awar": [4, 12], "award": [21, 23], "ax": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 23], "axes3d": [4, 8, 15], "axes_grid1": 8, "axhlin": 10, "axi": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "axiom": 7, "axvlin": [6, 10], "axvspan": 6, "b": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 20, 21, 23], "b1": 10, "b2": 10, "b3": 10, "b_": [2, 3, 18], "b_0": 2, "b_1": [2, 4, 14, 15], "b_2": [2, 15], "b_5": 15, "b_group": 11, "b_i": [2, 3, 4, 14, 23], "b_ia_": [2, 23], "b_ia_i": 2, "b_index": 11, "b_j": [3, 14], "b_k": [2, 3, 14, 15], "b_m": 14, "b_score": 11, "b_valu": 11, "babcock": 23, "bachelor": [19, 21], "back": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 18, 20, 23], "backbon": 18, "backend": [3, 6], "background": [22, 23], "backpropag": 3, "backtrack": 11, "backup": 18, "backward": [3, 4, 6, 14, 18], "bad": 8, "badli": 20, "bag": [11, 17, 23], "bag_clf": 12, "baggin": 23, "baggingboot": 12, "baggingclassifi": 12, "baggingtre": 12, "balanc": 8, "band": 18, "bandwidth": 18, "bar": [2, 8, 13, 23], "barber": 22, "bare": [6, 12], "base": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23], "basi": [7, 9, 10, 12, 13, 14, 15, 18], "basic": [0, 8, 10, 14, 15, 16, 17, 20, 23], "batch": [5, 6, 13, 14, 15], "batch_shap": 6, "batch_siz": [3, 5, 6], "batchnorm": 6, "bay": 9, "bayesian": [7, 17, 22, 23], "becaus": [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 23], "becom": [2, 3, 4, 7, 8, 9, 11, 14, 15, 20, 23], "been": [2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 17, 18, 23], "befor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 18, 20, 23], "beforehand": [2, 20, 23], "begin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 20, 21, 23], "behav": [3, 8, 15], "behavior": [2, 3, 15, 23], "behaviour": 14, "behind": [2, 3, 8, 10, 15, 23], "being": [2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 20, 23], "believ": [11, 18], "belong": [9, 10, 11, 15, 16], "below": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "benchmark": 12, "benefici": [3, 15], "benefit": [2, 3, 6, 13, 15, 17, 23], "bengio": [3, 22, 23], "benign": [3, 9], "besid": [6, 7], "bessel": 7, "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 21, 23], "beta": [1, 2, 3, 5, 12, 13, 15, 23], "beta_": [5, 15], "beta_0": [3, 5, 15], "beta_1": [3, 5, 12, 15], "beta_1x_i": 15, "beta_2": [5, 15], "beta_3": 5, "beta_i": 5, "beta_j": 15, "beta_k": 15, "beta_linreg": 15, "beta_m": 12, "beta_mg_m": 12, "beta_n": 5, "better": [2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 23], "between": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 23], "beyond": [2, 3, 7, 8, 10, 15, 23], "bf": [15, 16, 18, 20], "bg": 23, "bgd": 15, "bia": [2, 3, 4, 5, 7, 10, 11, 12, 14, 15, 23], "bias": [3, 4, 5, 7, 8, 11, 14], "big": [2, 3, 4, 7, 8, 16], "bigger": [3, 8], "bigr": 14, "bike": 11, "bilbo": 23, "billion": [5, 14, 17], "bin": [2, 9, 20], "binari": [2, 5, 7, 9, 11, 12, 14, 23], "binarycrossentropi": 6, "bind": 2, "binomi": [17, 20, 23], "binsboot": 8, "bioinformat": 2, "biolog": [3, 14], "bios1100": [17, 23], "bird": [2, 5], "birth": 23, "bishop": [22, 23], "bit": [3, 6, 18, 20, 23], "bitwis": 20, "bivari": 4, "bk": [2, 15], "bla": [18, 23], "black": [10, 11, 16], "block": [8, 12, 17, 18, 20, 23], "blog": 23, "blogpost": 6, "blue": [2, 5], "bmatrix": [2, 3, 5, 7, 9, 10, 13, 15, 18, 23], "bmi": 3, "bodi": [2, 3, 6, 14], "bold": 3, "boldfac": [1, 2, 7], "boldsymbol": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 23], "boltzmann": [14, 17, 23], "book": [22, 23], "book1": 22, "boolean": 6, "boost": [3, 11, 17, 23], "boostrap": 12, "bootstrap": [3, 15, 17, 23], "borrow": 23, "boston_dataset": 2, "bot": 10, "both": [0, 1, 2, 3, 6, 7, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23], "bottl": 9, "bound": [2, 10, 14], "boundari": [4, 6, 10, 13, 14], "box": [6, 11], "boyd": [10, 15], "bracket": [6, 20], "brain": [3, 9, 14], "branch": [11, 23], "break": [2, 6, 8, 13, 16, 23], "breast": [7, 9, 13], "breviti": 15, "brew": [2, 17, 23], "brg": 10, "briefli": [1, 2, 23], "bring": [2, 7, 8, 12], "britt": [21, 23], "broad": 2, "broadli": 23, "brought": [15, 17, 23], "brownle": 6, "browser": [0, 23], "brute": [5, 7, 13], "buffer_s": 6, "bui": 6, "build": [1, 2, 6, 7, 8, 12, 18, 20, 23], "built": [2, 3, 5, 6, 8], "bunch": 13, "busi": 2, "byte": [18, 23], "c": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "c1": [10, 13], "c2": [10, 13], "c_": [2, 10, 11, 12, 15, 20], "c_0": 20, "c_1": 14, "c_2": 14, "c_3": 14, "c_4": 14, "c_i": [14, 15], "c_k": 20, "ca": [3, 23], "cach": 12, "cal": [2, 10, 12, 14, 15], "calcul": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "call": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "calor": 2, "cambridg": [15, 22], "can": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "cancel": [2, 15, 23], "cancer": [7, 12], "cancerpd": 9, "candid": [10, 11, 12], "cannot": [2, 3, 6, 7, 8, 9, 10, 11, 20], "canopi": [2, 17, 23], "canva": [0, 1, 23], "cap": 7, "capabl": [2, 3, 10, 15, 17, 23], "capac": [4, 21], "capita": 2, "captur": [6, 13, 14, 23], "car": [5, 6], "card": [2, 9, 23], "cardin": 3, "care": [0, 13], "carefulli": 15, "carlo": [2, 8, 17, 20, 22, 23], "carri": [4, 8, 9], "cart": 12, "case": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 17, 18, 23], "casella": 22, "cast": 3, "cat": [5, 6], "catch": 2, "categor": [2, 3, 5, 11, 13, 23], "categori": [2, 3, 5, 9, 12, 14, 16, 23], "categorical_crossentropi": [3, 5], "caus": [2, 7, 8, 20, 23], "causal": 2, "causat": [2, 23], "cax": 3, "cb": [8, 23], "cbar": 3, "cc": [2, 3, 7, 15, 23], "ccc": [7, 14], "cdf": 20, "cdot": [2, 4, 8, 14, 15, 16, 18, 20, 23], "celebr": 15, "cell": 6, "center": [2, 3, 8, 9, 10, 11, 13, 16, 20, 23], "central": [1, 2, 5, 7, 8, 10, 18, 23], "centroid": [16, 20], "centroid_differ": 16, "centuri": 5, "certain": [2, 5, 8, 9, 11, 20, 23], "cg": 15, "cha": 2, "chain": [2, 3, 15, 17, 20, 23], "challeng": 0, "chanc": [3, 7, 15, 20], "chang": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "channel": 5, "chapter": [1, 2, 8, 12, 13, 18, 22, 23], "chapter3": 2, "charact": [2, 5, 7, 23], "character": [10, 11, 12, 14, 20], "characterist": [2, 3, 5, 12, 15, 23], "charg": [2, 23], "charl": 2, "chase": 6, "chatgpt": 0, "chd": 9, "chddata": 9, "cheap": 7, "cheaper": [3, 15], "check": [0, 1, 2, 3, 5, 6, 7, 13, 15, 18, 23], "checkmark": 5, "checkpoint": 6, "checkpoint_dir": 6, "checkpoint_prefix": 6, "chen": 12, "chiaramont": 4, "childcar": 1, "children": 1, "choic": [2, 3, 4, 5, 6, 8, 11, 14, 15, 16, 18, 23], "choleski": [7, 18], "choos": [0, 4, 5, 8, 11, 12, 13, 15, 16], "chosen": [1, 2, 3, 4, 8, 10, 11, 12, 15, 20, 23], "chosen_datapoint": 3, "christian": 22, "christoph": [22, 23], "cifar": 5, "cifar10": 5, "circ": [3, 14], "circl": [2, 10, 14], "circuit": 5, "circumfer": 11, "circumv": [3, 7, 15], "ckpt": 6, "clariti": 20, "class": [2, 3, 5, 6, 8, 9, 10, 11, 13, 14, 15, 20, 23], "class_nam": [5, 11], "class_val": 11, "class_valu": 11, "classic": [9, 11, 15], "classif": [2, 5, 7, 8, 9, 10, 13, 14, 17, 22, 23], "classifi": [2, 3, 6, 9, 11, 12, 13, 23], "classificaton": 3, "classifii": 12, "clean": 3, "clear": [3, 7, 12, 14, 15], "clearli": [2, 5, 7, 8, 9, 10, 20], "clever": [3, 12], "clf": [2, 8, 10, 11, 12, 23], "clf3": 2, "clf_lasso": 8, "clf_ridg": 8, "cli": 0, "clip": [5, 20], "clone": [0, 21], "close": [2, 3, 4, 6, 8, 10, 11, 13, 14, 15, 16, 20, 22, 23], "closer": [5, 7, 15], "closest": [10, 13, 15, 16], "closur": [17, 23], "cloud": [17, 23], "cluster": [2, 3, 6, 8, 13, 17, 23], "cluster_label": 16, "cm": [3, 4, 5, 8, 10, 15], "cmap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 23], "cmap_arg": 8, "cmd": [0, 11], "cn_": 20, "cnn": 14, "cnn_kera": 5, "cntk": [17, 23], "co": [2, 4, 5, 8, 11, 15, 23], "code": [5, 6, 8, 9, 10, 17, 18, 20, 22], "coef": [2, 23], "coef0": 10, "coef_": [1, 2, 7, 8, 10, 11, 15, 23], "coeff": 7, "coeffici": [2, 5, 7, 8, 9, 10, 11, 15, 18, 23], "coerc": [2, 8, 23], "coin": [12, 20], "coin_toss": 12, "col": [2, 13, 23], "colab": [17, 23], "cold": 11, "colinear": 2, "collaps": 10, "collect": [4, 8, 12, 13, 17, 20, 22, 23], "collinear": 7, "color": [2, 5, 6, 8, 10, 11, 12, 20], "color_channel": 5, "color_cod": 8, "colorbar": [3, 8], "colsample_bytre": 12, "colsaobject": 12, "column": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 18, 23], "columntransform": 11, "com": [0, 1, 6, 8, 17, 22, 23], "combin": [3, 4, 7, 8, 9, 12, 20], "come": [0, 2, 3, 5, 6, 7, 14, 15, 16, 23], "command": [0, 2, 3], "comment": [2, 6, 7, 8], "commerci": [2, 17, 23], "commit": 0, "commod": [2, 23], "common": [1, 2, 3, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "commonli": [2, 3, 6, 8, 9, 11, 15, 16], "commun": [2, 14], "commut": 5, "commutatitav": 5, "compact": [2, 3, 5, 7, 8, 9, 11, 13, 14, 15, 16, 23], "compair": 2, "compar": [2, 5, 6, 7, 8, 13, 15, 18, 23], "comparison": [4, 6, 15], "compat": 9, "compet": 2, "competit": 12, "compil": [2, 3, 5, 6, 15, 17, 18, 23], "complet": [0, 1, 2, 4, 5, 6, 11, 14, 23], "completenn": 14, "complex": [1, 3, 7, 10, 11, 13, 14, 15, 23], "complic": [2, 3, 11, 15, 23], "compon": [1, 2, 3, 5, 6, 7, 8, 9, 11, 16, 17, 23], "components_": 13, "compos": [11, 14, 15, 16, 17, 23], "compphys": [1, 2, 8, 17, 19, 21, 22, 23], "compress": [2, 23], "compris": 8, "compromis": 7, "compulsori": [17, 23], "comput": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23], "computation": [2, 5, 8, 11, 15, 20, 23], "computationalscienceuio": 23, "concaten": [4, 6, 8, 16], "concav": [3, 15], "concentr": [2, 12], "concept": [2, 4, 17, 23], "conceptu": [14, 15], "concern": [2, 3, 6, 9, 23], "concic": 23, "conclud": [2, 7, 15], "conclus": 3, "cond": 4, "conda": [2, 3, 17, 23], "condit": [2, 4, 6, 7, 8, 10, 11, 13, 15, 20, 23], "conduct": 17, "condwav": 4, "confid": [2, 7, 8, 9, 10, 23], "configur": 5, "confirm": [7, 14], "confus": [7, 8, 9, 12, 18], "confusion_matrix": 11, "congruenti": 20, "conjug": [6, 10], "conjugaci": 15, "conjunct": 5, "connect": [2, 3, 5, 6, 11, 13, 14, 15, 18, 23], "consequ": [7, 8, 10, 12, 14, 15], "conserv": [7, 16], "consid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "consider": [2, 3, 7, 15, 23], "consist": [2, 3, 4, 5, 6, 8, 14, 15, 20], "constant": [1, 2, 4, 6, 7, 8, 10, 14, 15, 20, 23], "constitu": [2, 23], "constitut": [4, 8], "constrain": [3, 5, 7, 9, 13], "constraint": [7, 8, 10, 15], "construct": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 20, 23], "contact": [2, 23], "contain": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "contemporari": 23, "content": [0, 3, 17, 18, 23], "context": [8, 12, 15], "contigu": 18, "continu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "contour": [11, 12, 15], "contourf": [10, 11, 12], "contrast": [3, 6, 11, 12, 14, 23], "contribut": [2, 5, 7, 15, 20, 23], "contributor": 2, "control": [0, 2, 3, 5, 11, 15, 17, 23], "conv": [5, 6], "conv2d": [5, 6], "conv2dtranspos": 6, "convei": 23, "conveni": [7, 8, 14, 15, 18, 23], "convent": 14, "converg": [3, 4, 6, 7, 10, 15, 16], "convergencewarn": [], "convert": [2, 3, 6, 7, 11, 13, 15, 18, 23], "converttomatrix": 6, "convex": [6, 7, 9], "convinc": 15, "convolut": [3, 6, 17, 23], "cool": [6, 11], "coolwarm": 8, "coordin": [7, 14, 16], "coorel": 2, "copi": [0, 2, 3, 16], "core": 12, "corel": 23, "coronari": 9, "corr": [2, 7, 9, 13], "correalt": [13, 17], "correct": [0, 2, 3, 4, 5, 6, 7, 9, 15, 18, 20, 23], "correctli": [3, 4, 8, 9, 12], "correl": [2, 3, 5, 7, 8, 9, 12, 14, 15, 17, 20, 23], "correlation_matrix": [2, 7, 9, 13], "correspond": [2, 5, 7, 8, 10, 11, 13, 14, 17, 18, 20, 23], "cortex": 14, "cosin": [5, 8], "cost": [1, 2, 4, 5, 7, 8, 9, 10, 11, 14, 15, 23], "cost_deep_grad": 4, "cost_funct": 4, "cost_function_deep": 4, "cost_function_deep_grad": 4, "cost_function_grad": 4, "cost_grad": 4, "cost_sum": 4, "costol": 15, "could": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "coulomb": [2, 23], "count": [0, 2, 11, 19, 20, 21, 23], "counterpart": 23, "countor": 15, "coupl": [6, 7, 8], "cours": [0, 1, 2, 3, 5, 7, 13, 21], "coursework": 0, "courvil": [22, 23], "cov": [7, 8, 13, 18, 20, 23], "cov_xi": [7, 13], "cov_xx": [7, 13], "cov_yi": [7, 13], "covari": [2, 9, 17, 18, 23], "covariance_matrix": [7, 13, 16], "cover": [2, 7, 17, 21, 22], "covert": [2, 23], "covxi": 20, "covxx": 20, "covxz": 20, "covyi": 20, "covyz": 20, "covzz": 20, "cpu": 3, "craft": 5, "creat": [0, 3, 5, 6, 7, 11, 12, 13, 14, 17, 23], "create_biases_and_weight": 3, "create_convolutional_neural_network_kera": 5, "create_neural_network_kera": 3, "create_x": [7, 13], "credit": [2, 9, 21, 23], "crim": 2, "crime": 2, "criteria": [2, 6, 11, 12, 16, 20, 23], "criterion": [11, 12, 15], "critic": 8, "cross": [0, 2, 3, 5, 9, 11, 12, 15, 17, 20, 23], "cross_entropi": 6, "cross_val_scor": 8, "cross_valid": [9, 12], "crossvalid": 8, "crucial": [3, 20], "cs231": 5, "csr_matrix": [18, 23], "csv": [2, 6, 8, 9, 11], "ctnk": 3, "cubic": 2, "cumbersom": 7, "cumsum": [12, 13, 23], "cumul": [9, 12, 20], "cumulative_heads_ratio": 12, "cup": 7, "current": [0, 1, 3, 4, 5, 6, 15, 16, 22], "curs": 2, "curv": [8, 9, 12, 14], "curvatur": 15, "custom": [8, 16], "custom_cmap": [11, 12], "custom_cmap2": [11, 12], "cutpoint": 11, "cv": [8, 9, 12], "cvxbook": 15, "cvxopt": [7, 10], "cycl": [3, 14], "d": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "d2_g_t": 4, "d_f": 15, "d_g_t": 4, "d_net_out": 4, "da": 5, "dagger": [7, 18], "dai": [3, 11, 17], "damp": 5, "darget": 11, "darkr": 20, "dat": [2, 23], "dat_id": [2, 8, 9, 11, 23], "data": [1, 4, 6, 7, 10, 12, 14, 15, 16, 18, 22], "data1": 16, "data2": 16, "data3": 16, "data4": 16, "data_id": [2, 8, 9, 11, 23], "data_indic": 3, "data_panda": 23, "data_path": [2, 8, 9, 11, 23], "databas": 3, "datafil": [2, 8, 9, 11, 23], "datafram": [2, 6, 7, 9, 11, 13, 23], "datapoint": [1, 3, 7, 8, 9, 13, 15], "datasci": [0, 1], "dataset": [1, 2, 6, 8, 9, 10, 11, 12, 13, 15, 16, 23], "datatyp": 6, "date": [0, 23], "daughter": 12, "david": 22, "dbh": 3, "dbo": 3, "dcomposit": 18, "ddot": 4, "dead": 3, "deadlin": 0, "deal": [2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 20, 23], "dealt": 2, "debt": 9, "debug": [2, 7, 8], "decad": [2, 5], "decai": [2, 15, 20, 23], "decemb": [21, 23], "decent": 12, "decid": [2, 4, 5, 7, 8, 11], "decim": [2, 23], "decis": [2, 3, 10, 13, 17, 22, 23], "decision_funct": 10, "decision_tre": 11, "decisiontreeclassifi": [11, 12], "decisiontreeregressor": [2, 11, 12], "declar": [2, 6, 18, 23], "decompos": [7, 8, 18], "decomposit": [2, 8, 14, 23], "decompost": 7, "deconvolut": 5, "decorrel": [12, 15], "decreas": [3, 4, 6, 7, 8, 12, 13, 15], "deduc": [2, 23], "deep": [5, 9, 14, 15, 17, 22], "deep_neural_network": 4, "deep_param": 4, "deep_tree_clf": [11, 12], "deep_tree_clf1": 11, "deep_tree_clf2": 11, "deepen": [7, 17, 23], "deeper": [2, 5, 6, 23], "deeplearningbook": [22, 23], "deer": 5, "def": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 20, 23], "def_covari": 20, "default": [2, 3, 4, 6, 8, 9, 18, 23], "default_tim": 6, "defect": 7, "defici": 7, "defin": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "definit": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "defint": 20, "degre": [0, 1, 5, 7, 8, 10, 11, 12, 13, 20, 23], "del": 3, "delet": [0, 8], "delimit": 6, "deliv": [0, 19, 23], "delta": [2, 4, 5, 8, 10, 14, 15, 16, 23], "delta_": [3, 18], "delta_0": 5, "delta_1": 5, "delta_2": 5, "delta_3": 5, "delta_4": 5, "delta_5": 5, "delta_h": [2, 3, 23], "delta_j": [5, 14], "delta_k": 14, "delta_l": [3, 5], "delta_momentum": 15, "delta_n": [2, 5, 23], "delug": 17, "delv": 2, "demand": 15, "demonstr": [2, 5, 7, 8, 9, 13, 14, 17, 23], "den": 6, "denomin": [3, 7], "denot": [3, 4, 8, 9, 15, 20], "dens": [3, 5, 6], "densiti": [2, 4, 8, 20], "depart": [21, 23], "depend": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 23], "depict": 20, "deploy": [2, 17, 23], "depth": [2, 5, 11, 12, 18], "deriv": [2, 3, 4, 8, 9, 10, 12, 13, 15, 17, 23], "derivati": 15, "derivative_fn": 15, "descend": [7, 11, 13], "descent": [2, 3, 5, 9, 10, 14, 23], "describ": [2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 18, 23], "descript": [2, 10, 11, 23], "design": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 23], "designmatrix": [2, 23], "desir": [2, 4, 6, 7, 15, 16, 23], "desktop": 0, "despit": [3, 14], "destroi": 18, "det": [7, 18], "detail": [2, 8, 13, 15, 16, 18], "detect": [5, 10, 14], "determin": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "determinist": [9, 15, 20], "dev": 3, "develop": [2, 5, 7, 10, 12, 13, 14, 17, 18, 23], "deviat": [2, 3, 4, 6, 7, 8, 20, 23], "devis": 14, "df": [6, 10, 13, 15, 23], "df1": 23, "di": 2, "diag": [7, 10], "diagnost": [3, 12], "diagon": [2, 7, 9, 15, 18, 20, 23], "diagonaliz": 7, "diagram": 12, "diagsvd": 8, "dice": [8, 20], "dict": [8, 10], "dictionari": 2, "did": [1, 2, 3, 7, 8, 9, 12, 13, 16, 23], "die": 3, "diff": 4, "diff1": 4, "diff2": 4, "diff_ag": 4, "diffeent": 10, "differ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "differenti": [1, 2, 5, 17, 18, 23], "difficult": [2, 3, 8, 12, 15, 20, 23], "difficulti": [2, 3, 15, 23], "diffonedim": 4, "digit": [2, 3, 5, 6, 8, 21, 23], "dilemma": 15, "dilut": 3, "dim": [6, 13, 16, 18], "dimens": [1, 2, 3, 4, 5, 6, 7, 10, 13, 16, 18, 23], "dimension": [2, 6, 7, 8, 11, 13, 15, 16, 17, 18, 23], "dimensionless": [2, 5, 23], "diment": 18, "dimnsion": 6, "diod": 5, "direct": [2, 3, 4, 6, 13, 14, 15, 16, 23], "directli": [3, 6, 7, 8, 20], "disadvantag": [2, 23], "disappear": [5, 8], "disc_loss": 6, "disc_tap": 6, "discard": [8, 13], "disciplin": [2, 5, 14], "disclaim": 20, "discord": 23, "discourag": [0, 15], "discov": [2, 23], "discover": 7, "discret": [3, 5, 7, 9, 15], "discrimin": [6, 9, 12, 13], "discriminator_loss": 6, "discriminator_loss_list": 6, "discriminator_model": 6, "discriminator_optim": 6, "discuss": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "diseas": 9, "disguis": 8, "disord": [3, 9], "displai": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "displaystyl": [2, 7, 23], "disregard": [2, 23], "dissimilar": [13, 16], "dist": 16, "distanc": [2, 10, 11, 13, 16, 20], "distance_list": 11, "distinct": [5, 9, 10, 11, 12, 16], "distinctli": 10, "distinguish": [2, 6, 9, 10, 20, 23], "distplot": 2, "distribut": [2, 3, 6, 8, 9, 12, 13, 15, 16, 17, 18, 23], "distrubut": [2, 17, 23], "dive": [2, 10, 18, 23], "diverg": [3, 15], "divid": [2, 3, 5, 7, 8, 9, 10, 11, 13, 14, 20, 23], "divis": [8, 10, 11, 15, 18, 20], "dna": 9, "dnn": [2, 3, 4, 6, 14, 23], "dnn1": 6, "dnn2_gru2": 6, "dnn_kera": 3, "dnn_model": 3, "dnn_numpi": 3, "dnn_scikit": [2, 3, 23], "do": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "doc": [0, 1, 2, 17, 19, 21, 22, 23], "document": [0, 6, 15], "doe": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20, 23], "doesn": [5, 11, 14, 23], "dog": [3, 5, 6], "domain": [7, 10, 15], "domin": [2, 23], "don": [0, 1, 2, 3, 5, 7, 8, 10, 13, 15, 17, 23], "done": [1, 2, 4, 5, 6, 7, 8, 11, 12, 13, 15, 18, 23], "dot": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "doubl": [1, 5, 6, 18, 23], "doubli": 3, "down": [2, 5, 8, 11, 13, 14, 15], "download": [0, 2, 3, 5, 7, 8, 18, 22, 23], "downsampl": 5, "dozen": 3, "dq": 8, "drag": 15, "dramat": 13, "drastic": 6, "draw": [6, 8, 12, 15], "drawback": [2, 3, 5, 15], "drawn": [3, 6, 8, 9, 13, 20, 23], "drive": [5, 6], "driven": 5, "drop": [2, 3, 7, 8, 13, 15, 20, 23], "dropna": [2, 8, 23], "dropout": 6, "dt": [4, 5, 15, 20], "dtype": [2, 3, 5, 6, 16, 18, 23], "dub": [2, 23], "due": [3, 4, 7, 8, 10, 12, 14, 15, 21, 23], "dummi": 2, "dure": [2, 3, 5, 6, 10, 11, 13, 17, 23], "dwell": 2, "dwh": 3, "dwo": 3, "dx": [4, 5, 10, 20], "dx_1": 20, "dx_1p": 8, "dx_2p": 8, "dx_mp": 8, "dx_n": 20, "dxp": 8, "dy": [3, 10, 20], "dynam": 6, "dz": 10, "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 21, 23], "e_": [2, 4, 23], "each": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "eapprox": [2, 23], "earli": [3, 15], "earlier": [2, 7, 9, 10, 11, 13, 14, 15, 23], "earthexplor": 8, "eas": [8, 11, 16], "easi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "easier": [0, 7, 8, 10, 11, 15, 20, 23], "easiest": 15, "easili": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "eastern": [21, 23], "ebind": [2, 23], "eblock": 11, "econometr": 23, "economi": 7, "ecosystem": [17, 23], "ect": 19, "edg": 5, "edgecolor": 8, "edu": 15, "educ": [2, 23], "eff": 20, "effect": [1, 3, 6, 12, 15, 20], "effic": 3, "effici": [2, 5, 12, 15, 17, 18, 20, 23], "efron": 8, "egrad": 15, "eig": [7, 13, 15, 18, 20, 23], "eigen": 20, "eigenpair": [7, 13], "eigenvalu": [2, 7, 10, 13, 15, 18, 23], "eigenvector": [7, 13, 15], "eight": [18, 23], "eigval": [18, 20, 23], "eigvalu": [13, 15], "eigvec": [18, 20, 23], "eigvector": [13, 15], "eir": [21, 23], "eispack": [18, 23], "either": [3, 7, 8, 9, 10, 11, 12, 13, 15, 20, 23], "eivind": 21, "eivinsto": 21, "ekstr\u00f8m": 6, "elabor": 20, "elarn": 5, "electr": [2, 5, 14, 23], "electron": 23, "eleg": 13, "element": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 22], "elementari": [12, 15, 18], "elementwis": [5, 15], "elementwise_grad": [4, 15], "elessar": 23, "elif": 16, "elim": 18, "elimin": [5, 10], "elin": [21, 23], "ellipsi": 1, "els": [1, 3, 5, 6, 9, 11, 14, 15, 18], "elu": 3, "elus": [2, 23], "email": [19, 21, 23], "embed": [2, 13], "embodi": 8, "emit": 20, "emner": 22, "emphas": [2, 12, 17, 23], "emphasi": [2, 17, 22, 23], "empir": [3, 13, 20], "emploi": [2, 3, 7, 8, 13, 15, 20, 23], "employ": 2, "empti": [0, 8, 12], "emul": 14, "en": [17, 22], "enabl": 13, "enbodi": 8, "encod": [2, 5, 7, 11, 13, 16, 23], "encompass": [2, 20], "encount": [0, 2, 3, 7, 9, 15, 20, 23], "encourag": 0, "end": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "endpoint": [5, 8], "energi": [2, 6, 8], "enforc": 14, "eng": 22, "engin": [2, 3, 5, 6, 17, 23], "enorm": 5, "enough": [2, 8, 15, 23], "ensembl": [3, 11, 23], "ensur": [2, 3, 4, 5, 7, 8, 13, 15, 20], "entail": 23, "enter": [7, 8], "enthought": [2, 17, 23], "entir": [3, 5, 9, 11, 17, 20, 23], "entiti": [11, 14, 18, 23], "entri": [2, 7, 10, 13, 14, 18, 23], "entropi": [3, 5, 9, 12, 15, 23], "enumer": [2, 3, 4, 5, 6, 8, 10, 23], "env": 20, "environ": [4, 17, 23], "environemnt": 0, "eo": [2, 8], "eol": 2, "eosfit": 2, "epoch": [2, 3, 5, 6, 14, 15, 23], "epsilon": [2, 7, 8, 9, 15, 23], "epsilon_": [2, 23], "epsilon_0": [2, 23], "epsilon_1": [2, 23], "epsilon_2": [2, 23], "epsilon_i": [2, 23], "eq": [5, 15, 16, 18, 20], "eqnarrai": [5, 7, 8], "equal": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "equat": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "equilibrium": [4, 14], "equiv": [5, 15, 18, 20], "equival": [2, 3, 7, 9, 10, 13, 15, 17, 18, 23], "erf": 20, "eriador": 23, "err": [2, 12], "err_": 8, "err_sqr": 4, "errat": 15, "erron": 4, "error": [0, 1, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20], "error_estimate_corr_tim": 20, "error_hidden": 3, "error_output": 3, "escap": 15, "especi": [0, 3, 5, 11, 14, 15], "essenti": [0, 2, 7, 8, 11, 12, 14, 16, 20], "establish": [1, 2, 8, 12, 13], "estim": [2, 3, 7, 8, 9, 12, 13, 15, 17, 20, 23], "estimated_mse_fold": 8, "estimated_mse_kfold": 8, "estimated_mse_sklearn": 8, "et": [1, 2, 4, 6, 22, 23], "eta": [2, 3, 5, 10, 14, 15, 23], "eta0": [10, 15], "eta_": 15, "eta_t": 15, "eta_v": [2, 3, 5, 23], "etc": [2, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20], "ethic": 17, "euclidean": [2, 16], "evalu": [0, 1, 2, 4, 5, 6, 7, 8, 11, 15, 20, 23], "evalut": 15, "even": [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "evenli": 6, "event": [7, 9, 12, 20], "eventu": [2, 7, 8, 13, 14, 15, 21], "everi": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23], "everyth": [1, 6, 14], "everywher": [6, 15], "evolv": 2, "exact": [2, 7, 13, 14, 15, 18, 20, 23], "exactli": [2, 5, 6, 8, 14, 17], "exam": 23, "examin": 8, "exampl": [0, 1, 7, 13, 14, 15, 17, 18, 20, 22], "exce": [3, 14, 15], "excel": [2, 3, 6, 7, 12, 23], "except": [5, 6, 8, 10, 11, 18], "excess": [2, 23], "excit": 2, "exclud": [3, 8, 14], "exclus": [2, 3, 5, 8, 20, 23], "execut": [0, 4, 7, 15], "exemplifi": 15, "exercic": [21, 23], "exercis": [7, 17, 19, 21, 23], "exhaust": 8, "exhibit": [2, 7, 8, 10, 23], "exist": [2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 18, 23], "exit": [7, 18], "exp": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 20], "exp_term": 3, "expand": [7, 9, 13, 15], "expans": [2, 5, 7, 10, 12, 14, 15, 23], "expect": [0, 2, 3, 7, 8, 9, 13, 14, 15, 17, 23], "expectation_value_of_h_wrt_p": 20, "expens": [1, 8, 12, 15], "experi": [0, 2, 3, 8, 10, 15, 17, 23], "experiment": [2, 6, 8, 11, 20, 23], "expert": [3, 11], "explain": [1, 2, 8, 11, 12, 13, 15, 23], "explained_variance_ratio_": 13, "explanatori": [2, 23], "explicit": [2, 5, 8, 15, 18, 23], "explicitli": [2, 6], "explod": 3, "exploit": [2, 5, 14, 15, 23], "explor": [3, 6, 8, 10, 15, 17, 23], "expon": 3, "exponenti": [2, 3, 7, 8, 12, 15, 20, 23], "export": [0, 1, 11], "export_graphviz": 11, "export_text": 11, "exporttext": 11, "expos": 17, "express": [2, 4, 5, 7, 8, 9, 12, 14, 15, 18, 20, 23], "exptmean": 20, "exptvari": 20, "extend": [2, 4, 9, 13, 15, 17, 23], "extens": [0, 2, 14, 17, 23], "extent": [2, 3, 8, 22], "extern": [5, 8, 11], "extra": [0, 3, 5, 7, 21, 23], "extract": [1, 2, 5, 7, 8, 9, 10, 13, 15, 18, 23], "extrapol": [2, 23], "extrem": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 18], "extremum": 15, "extrins": 13, "ey": [2, 7, 8, 15, 16, 18, 23], "f": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 23], "f1": 15, "f11": [2, 23], "f12": [2, 23], "f13": [2, 23], "f1_grad": 15, "f1d": 15, "f2": 15, "f2_grad_x1": 15, "f2_grad_x1_analyt": 15, "f2_grad_x2": 15, "f2_grad_x2_analyt": 15, "f3": 15, "f3_grad": 15, "f3_grad_analyt": 15, "f4": 15, "f4_grad": 15, "f4_grad_analyt": 15, "f5": 15, "f5_grad": 15, "f6": 15, "f6_for": 15, "f6_for_grad": 15, "f6_grad_analyt": 15, "f6_while": 15, "f6_while_grad": 15, "f7": 15, "f7_grad": 15, "f7_grad_analyt": 15, "f8": 15, "f8_grad": 15, "f9": [2, 15, 23], "f9_altern": 15, "f9_alternative_grad": 15, "f9_grad": 15, "f_": 12, "f_0": [5, 12], "f_1": [12, 15], "f_2": [14, 15], "f_3": 14, "f_d": 20, "f_grad": 15, "f_grad_analyt": 15, "f_i": [1, 2, 8, 14], "f_m": [5, 12], "f_n": 5, "f_vec": 4, "face": [15, 23], "facecolor": [8, 10, 20], "facil": [2, 17], "facilit": 14, "fact": [2, 3, 5, 7, 11, 13, 14, 15, 23], "factor": [2, 3, 5, 7, 8, 11, 12, 13, 15, 18, 20, 23], "factori": 15, "fade": 8, "fafab0": [11, 12], "fail": [2, 8, 15, 21, 23], "failur": 9, "fairli": [3, 4, 20], "faisal": 1, "fake": 6, "fake_loss": 6, "fake_output": 6, "fall": [10, 11, 19], "fals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 16, 18, 23], "famili": [2, 9, 10, 20], "familiar": [0, 2, 5, 7, 8, 10, 17, 18, 20, 23], "famou": [8, 14], "far": [1, 2, 5, 6, 7, 8, 10, 13, 14, 15, 16, 23], "fashion": [2, 11, 12, 23], "fast": [3, 5, 8, 12, 14, 15, 17, 20, 23], "faster": [3, 13, 15], "fastest": [15, 18], "favor": 9, "favorit": 20, "fc": 5, "featur": [0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "feature_nam": [2, 3, 9, 11], "feautur": 11, "fed": 3, "feed": [2, 4, 5, 13, 17, 23], "feed_forward": 3, "feed_forward_out": 3, "feed_forward_train": 3, "feedback": [6, 23], "feeddorward": 6, "feedforward": [3, 6, 14], "feel": [0, 1, 2, 7, 8, 13, 15, 17, 21, 23], "feet": 2, "fetch": [0, 8], "few": [3, 5, 6, 7, 11, 20, 23], "fewer": [2, 11, 13, 23], "ffnn": [3, 14], "field": [2, 5, 8, 14, 17], "fifth": [2, 8, 23], "fig": [2, 3, 4, 5, 6, 8, 9, 14, 15, 16, 23], "fig_id": [2, 8, 9, 11, 23], "figaxi": 20, "figsiz": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "figur": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 23], "figure_id": [2, 8, 9, 11, 23], "figurefil": [2, 8, 9, 11, 23], "file": [0, 2, 6, 7, 8, 9, 11, 23], "file_prefix": 6, "filenam": 23, "fill": [7, 11], "filter": [5, 6], "final": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 23], "financ": 2, "find": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "fine": [2, 16], "finish": 4, "finit": [5, 7, 8, 14, 15, 20], "finnicki": 0, "first": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 22], "firsteigvector": 13, "fit": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20], "fit_intercept": [1, 2, 7, 8], "fit_mod": 11, "fit_theta": 8, "fit_transform": [0, 2, 8, 10, 11, 13], "fiti": [2, 23], "five": [2, 11, 23], "fix": [2, 5, 6, 8, 12, 13, 14, 15, 23], "flag": 6, "flat": [14, 15], "flatten": [3, 5, 6, 7, 18], "flexibl": [3, 8, 10, 12, 14, 23], "flip": [21, 23], "float": [2, 5, 6, 7, 11, 13, 15, 16, 18, 23], "float32": [6, 11], "float64": [6, 18, 23], "flop": [7, 18], "flow": [3, 6, 14], "fluctuat": 7, "fly": 13, "fm": 2, "fmax": 5, "fmesh": 15, "fn": 9, "focu": [0, 2, 5, 6, 7, 8, 17, 22, 23], "focus": [3, 8, 9, 18], "fold": [8, 11], "folder": [0, 2, 6, 8, 23], "follow": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "font": [9, 20, 23], "fontdict": 20, "fontsiz": [3, 8, 10, 11, 12, 20], "fontweight": 3, "footprint": 5, "foral": 10, "forc": [2, 7, 8, 12, 13], "forcast": 6, "forecast": [6, 14], "forest": [2, 3, 11, 17, 23], "forget": 13, "form": [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "formal": [5, 6, 16, 20], "format": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 17, 20, 22], "format_data": 6, "formatstrformatt": [8, 15], "formul": [6, 8, 13, 16], "formula": [5, 15, 20], "forth": [6, 14], "fortran": [2, 17, 18, 23], "fortran2003": [17, 23], "fortran90": 20, "fortun": [2, 13], "forward": [2, 5, 8, 17, 18, 23], "found": [3, 4, 6, 7, 8, 14, 15, 23], "foundat": [17, 23], "four": [6, 7, 8, 10, 14, 18, 19, 21, 23], "fourier": [2, 23], "fourierdef1": 5, "fourierdef2": 5, "fourierseriessign": 5, "fourth": [14, 23], "fp": 9, "frac": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "fraction": 11, "frame": 9, "framework": [3, 10, 12, 20], "frank": [7, 13], "frankefunct": [7, 8, 13], "fredli": [21, 23], "free": [0, 1, 2, 8, 13, 15, 17, 18, 20, 21, 22, 23], "freecodecamp": 17, "freedom": 7, "freeli": 2, "freez": 0, "frequenc": [5, 8, 9, 20], "frequent": [2, 10, 11, 15], "frequentist": 17, "fresh": 12, "fridai": [0, 21, 23], "friedman": [8, 22, 23], "friendli": 6, "frodo": 23, "frog": 5, "from": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 22], "from_cod": 11, "from_logit": [5, 6], "from_tensor_slic": 6, "front": [2, 6, 7, 23], "frustrat": 0, "fulfil": [4, 7, 14], "full": [2, 3, 5, 7, 9, 11, 12, 15, 20, 23], "full_matric": 7, "fulli": [5, 8, 14, 20], "fun": [17, 23], "func": 4, "function": [0, 1, 4, 5, 6, 7, 11, 16, 17, 18], "functionali": 13, "fundament": [2, 8, 17, 23], "funtion": 4, "further": [4, 9, 11, 23], "furthermor": [2, 5, 7, 8, 9, 13, 14, 15, 17, 23], "futur": [2, 6, 10, 11, 23], "fy": [0, 19, 21, 22, 23], "fys5419": [22, 23], "fys5429": [22, 23], "f\u00f8470": [21, 23], "g": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 20, 23], "g0": 4, "g_": [4, 11, 12], "g_0": 4, "g_1": [4, 12], "g_2": [4, 12], "g_analyt": 4, "g_dnn_ag": 4, "g_euler": 4, "g_i": 4, "g_m": [5, 12], "g_n": 5, "g_re": 4, "g_t": 4, "g_t_d2t": 4, "g_t_d2x": 4, "g_t_dt": 4, "g_t_hessian": 4, "g_t_hessian_func": 4, "g_t_jacobian": 4, "g_t_jacobian_func": 4, "g_trial": 4, "g_trial_deep": 4, "g_vec": 4, "gain": [3, 7, 9, 11, 12, 15], "galleri": [2, 23], "game": 6, "gamge": 23, "gamma": [2, 4, 10, 11, 12, 13, 15, 23], "gamma1": 10, "gamma2": 10, "gamma_": [2, 23], "gamma_0": 12, "gamma_1": 12, "gamma_1x": 12, "gamma_i": [2, 10, 20, 23], "gamma_j": 15, "gamma_k": 15, "gamma_m": 12, "gamma_x": [2, 23], "gap": 10, "gate": [6, 14], "gather": [2, 3, 14], "gaug": 14, "gaussbacksub": 18, "gaussian": [6, 7, 8, 10, 16, 20, 23], "gaussian_point": 16, "gaussian_rbf": 10, "gave": 15, "gavra": 23, "gbc": 23, "gca": [4, 8, 10, 15], "gd": 3, "gd_clf": 12, "gdclassiffiercgain": 12, "gdclassiffierconfus": 12, "gdclassiffierroc": 12, "gdm": 15, "gdregress": 12, "ge": [3, 7, 9, 20], "gen_loss": 6, "gen_tap": 6, "gender": [2, 23], "genener": 6, "gener": [0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22], "generaliz": 1, "generallay": 14, "generate_and_save_imag": 6, "generate_imag": 6, "generate_latent_point": 6, "generate_simple_clustering_dataset": 16, "generated_imag": 6, "generator_loss": 6, "generator_loss_list": 6, "generator_model": 6, "generator_optim": 6, "genom": 17, "geodes": 13, "geometr": [2, 15, 23], "geometri": 7, "georg": 22, "geotif": 8, "geq": [4, 7, 10, 11, 15], "geron": [2, 22, 23], "get": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20, 21, 23], "get_dummi": 11, "get_paramet": 4, "get_split": 11, "get_yaxi": 10, "get_yticklabel": 8, "gh": 0, "gibb": [17, 23], "gif": 6, "gini": 12, "gini_index": 11, "ginvers": 15, "git": [0, 2, 17, 23], "giter": 15, "github": [2, 17, 19, 21, 22, 23], "gitignor": 0, "gitlab": [2, 17, 23], "give": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 23], "given": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "global": [8, 9, 15], "glorot": 3, "gnew": 15, "go": [0, 1, 2, 3, 5, 7, 8, 10, 11, 13, 14, 15, 23], "goal": [2, 9, 11, 23], "goe": [0, 2, 3, 4, 7, 8, 15, 16, 18, 23], "golden": 15, "gone": 7, "gong": 3, "good": [0, 3, 5, 6, 7, 8, 11, 12, 13, 15, 17, 20, 22], "goodfellow": [6, 22, 23], "googl": [3, 6, 17, 23], "got": [3, 8], "gotten": 23, "gov": 8, "govern": 23, "gp": 22, "gpu": [3, 15, 17, 23], "grad": [4, 15], "grad_analyt": 15, "grade": 19, "gradient": [2, 5, 6, 9, 10, 11, 14, 17, 23], "gradientboostingclassifi": 12, "gradientboostingregressor": 12, "gradients_of_discrimin": 6, "gradients_of_gener": 6, "gradienttap": 6, "gradual": [3, 16], "grai": [6, 8], "graph": [1, 3, 11, 13, 14, 15], "graph_from_dot_data": 11, "graphic": [2, 3, 11, 23], "grasp": 2, "gray_r": [3, 5], "grayscal": 5, "great": [0, 7, 15], "greater": [3, 9, 20], "greatli": 15, "greedi": 11, "green": [2, 5, 11, 20], "grei": 6, "grid": [3, 5, 8, 9, 10, 14, 20], "grossli": 15, "ground": [2, 23], "group": [0, 2, 8, 9, 11, 16, 17, 19, 21, 23], "groupbi": [2, 23], "grow": [3, 5, 11, 12], "growth": [2, 23], "gru": 6, "guarante": [2, 6, 15, 20, 23], "guess": [3, 6, 12, 15, 16], "guestrin": 12, "guid": 3, "h": [0, 2, 3, 7, 8, 10, 15, 20, 21, 22, 23], "h1": 4, "h_": [2, 15, 23], "h_1": [4, 15], "h_2": [4, 15], "h_m": 12, "ha": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "haanen": [21, 23], "habit": 2, "had": [2, 3, 8, 9, 15, 23], "hadamard": [3, 14, 15], "half": [3, 10, 11], "halv": 12, "hand": [2, 3, 4, 5, 7, 13, 14, 15, 17, 18, 20, 21, 22, 23], "handi": 5, "handl": [0, 2, 3, 4, 7, 11, 13, 17], "handle_unknown": 11, "handsid": 14, "handwrit": 14, "handwritten": [3, 7], "happen": [3, 4, 5, 6, 7, 8, 12, 15, 20], "hard": [3, 9, 10, 12, 15], "hardcopi": [17, 23], "harder": [2, 3], "harmon": 5, "hasn": [], "hassl": [2, 17, 23], "hast": [17, 23], "hasti": [1, 2, 8, 22, 23], "hat": [1, 2, 3, 7, 8, 9, 11, 12, 13, 14, 15, 18], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "haven": 3, "he": 9, "head": [2, 6, 12, 20], "header": [2, 23], "heads_proba": 12, "health": 2, "hear": [2, 15, 23], "heart": [2, 9, 23], "heatmap": [2, 3, 5, 9, 23], "heavili": 2, "heavisid": 3, "height": [3, 5, 8], "held": 15, "help": [0, 1, 2, 3, 6, 14, 15, 23], "helper": [6, 16], "henc": [2, 7, 8, 10, 11, 12, 14, 15, 23], "henrik": [21, 23], "her": 9, "here": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hereaft": [2, 10, 14, 23], "hermitian": 18, "hessenberg": 18, "hessian": [2, 4, 7, 15], "heterogen": [11, 12], "hi": 9, "hidden": [3, 5, 6, 14], "hidden_bia": 3, "hidden_bias_gradi": 3, "hidden_layer_s": [2, 3, 23], "hidden_neuron": 6, "hidden_weight": 3, "hidden_weights_gradi": 3, "hierarch": 7, "high": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 23], "higher": [2, 3, 5, 7, 8, 10, 15, 23], "highest": [3, 4], "highli": [2, 5, 6, 12, 17, 18, 22, 23], "highwai": 2, "hing": 10, "hint": [0, 1, 15], "hip": 17, "hire": 2, "hist": [6, 8, 9, 20], "histogram": [2, 8, 9, 20], "histor": [9, 13], "histori": [0, 5, 6, 14], "hitherto": 7, "hjorth": [21, 23], "hobbi": 20, "hoc": 7, "hoff": 22, "hold": [3, 5, 8, 15, 16], "holder": [2, 23], "home": 2, "homepag": 23, "homework": [8, 15], "homogen": [3, 5, 11, 12, 15], "honchar": 4, "hopefulli": [0, 2, 13, 20, 23], "horizont": 13, "horlyk": [21, 23], "hors": [5, 9, 23], "hot": [3, 11], "hour": [3, 17, 19, 20, 21, 23], "how": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "howev": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hspace": [2, 6, 10, 12, 20, 23], "hstack": 3, "htf": 23, "html": [1, 2, 17, 19, 21, 22, 23], "http": [0, 1, 2, 5, 6, 8, 15, 17, 18, 19, 21, 22, 23], "huang": [2, 23], "huber": [2, 23], "huge": [3, 5, 6, 17], "human": [2, 3, 5, 8, 11, 14], "humid": 11, "hundr": 3, "hungri": 3, "hybrid": 19, "hydrogen": [2, 23], "hyperbol": [3, 6, 14], "hyperparam": 10, "hyperparamet": [5, 6, 7, 8, 11, 15], "hyperplan": 13, "h\u00f8rlyk": [21, 23], "i": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "i0": [2, 23], "i1": [2, 8, 10, 14, 23], "i2": [2, 10, 14, 23], "i3": [2, 14, 23], "i5": [2, 23], "i_": 15, "i_1": [7, 8], "i_2": [7, 8], "ian": 22, "ic": 3, "id": [9, 15], "ida": [21, 23], "idea": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 18], "ideal": [2, 4, 8, 10, 15, 20, 23], "idem": 8, "ident": [7, 8, 14, 15, 18], "identifi": [2, 3, 9, 11, 13, 14, 15, 16, 23], "ieor": 20, "ifi": 22, "ifs": [17, 23], "ignor": [0, 2, 3, 5, 11], "ii": [18, 20], "iii": [18, 23], "ij": [1, 2, 3, 5, 8, 10, 14, 16, 18, 20, 23], "ik": [2, 18, 23], "illustr": [7, 9, 12, 14, 15, 16, 17, 23], "im": 8, "imag": [3, 5, 6, 8, 11, 13, 14, 16, 22, 23], "image_at_epoch_": 6, "image_batch": 6, "image_height": 5, "image_path": [2, 8, 9, 11, 23], "image_width": 5, "imageio": 8, "images_from_seed_imag": 6, "imagin": 3, "immedi": [2, 5, 6, 8, 17, 23], "implement": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 20, 23], "impli": [5, 7, 8, 9, 15, 18], "implicit": 5, "implicitli": [13, 20], "import": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "importantli": 5, "impos": [2, 8, 13, 14, 23], "imposs": [2, 7, 23], "impress": [2, 14, 23], "improv": [0, 2, 6, 7, 11, 12, 13, 15], "impur": 11, "imread": 8, "imshow": [3, 5, 6, 8], "in3050": [22, 23], "in3310": 23, "in4080": [22, 23], "in4300": [22, 23], "in4310": 22, "in5400": 5, "in5550": 22, "in_out_neuron": 6, "inaccur": 15, "inact": 14, "inadequ": [2, 23], "inch": 8, "includ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 17, 20, 21, 22, 23], "include_bia": [8, 11], "incom": [1, 14], "incorrect": 3, "incoveni": 10, "increas": [2, 3, 5, 6, 7, 8, 11, 14, 15, 20, 23], "increasingli": 20, "ind": 8, "inde": [2, 4, 6, 7, 8, 15, 23], "indefinit": 6, "independ": [2, 7, 8, 9, 10, 14, 15, 20, 23], "index": [2, 3, 5, 6, 12, 16, 17, 18, 20, 22, 23], "index_col": [2, 23], "indic": [1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 23], "indispens": 8, "individu": [3, 8, 9, 12, 14, 20, 23], "indu": 2, "indx": 18, "indx1": 4, "indx2": 4, "indx3": 4, "ineffici": [5, 15], "inequ": [10, 15], "inertia": 15, "inf1000": [17, 23], "inf1100": [17, 23], "inf1100l": [17, 23], "inf1110": [17, 23], "inf3000": 23, "infeas": 11, "infer": [2, 3, 6, 8, 22, 23], "inferenc": 3, "infil": [2, 8, 9, 11, 23], "infin": [7, 8, 9, 13], "infinit": 5, "infinitesim": 20, "influenc": [8, 12], "influenti": 3, "info": 23, "inform": [2, 3, 5, 6, 8, 11, 13, 14, 15, 16, 18, 22, 23], "inforom": 0, "infti": [5, 8, 15, 20], "ingeni": 15, "ingredi": [2, 11, 23], "inher": 8, "inherit": [18, 23], "initi": [2, 3, 4, 8, 12, 15, 16, 18, 20, 23], "inject": 16, "inlin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "inner": [2, 15], "inp": 6, "inplac": 15, "input": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 20, 23], "input_dim": 3, "input_shap": [5, 6], "inputs": 3, "inputs_shuffl": [2, 3], "insert": [5, 7, 8, 10, 12, 20], "insid": [2, 6, 9], "insight": [2, 3, 7, 17, 23], "insist": [8, 15], "inspir": [2, 3, 14, 23], "instabl": 4, "instal": [0, 2, 3, 7, 8, 11], "instanc": [1, 2, 3, 4, 6, 8, 11, 13, 15, 23], "instanti": 12, "instead": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 20, 23], "institut": 3, "instruct": [2, 3], "int": [2, 3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 20], "int32": 12, "int_": [5, 8, 20], "int_0": 20, "int_a": 20, "intak": 2, "integ": [3, 4, 15, 16, 18, 20, 23], "integer_vector": 3, "integr": [5, 8, 20, 23], "intellig": [2, 16, 22, 23], "intend": 12, "intens": 3, "intention": 16, "interact": [2, 8, 11, 14, 17, 23], "intercept": [1, 2, 8, 10, 13, 15, 23], "intercept_": [2, 8, 10, 11, 15, 23], "interchang": [7, 14, 18], "interconnect": 3, "interest": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 20, 23], "interfac": [2, 3, 18], "interior": [2, 11, 23], "intermedi": 18, "intern": [3, 12, 14], "interpol": [3, 5, 6, 8, 14], "interpr": 7, "interpret": [0, 1, 2, 3, 8, 11, 12, 14, 15, 18, 20], "interv": [2, 5, 7, 8, 9, 15, 20, 23], "intial": 15, "intract": [2, 6], "intrins": [5, 13, 18, 20, 23], "intro": [17, 22, 23], "introduc": [2, 3, 7, 8, 10, 12, 14, 18, 20, 23], "introduct": [3, 4, 6, 15, 22], "introductori": [2, 6, 18, 22, 23], "intuit": [2, 7, 8, 10, 14, 15, 23], "inv": [2, 7, 15, 23], "invalu": [2, 15, 17, 23], "invari": 3, "invd": 7, "inver": 10, "invers": [2, 5, 8, 15, 23], "inverse_transform": 10, "invert": [1, 2, 7, 9, 12, 15, 23], "invh": 15, "invok": [2, 10], "involv": [2, 4, 8, 9, 13, 14, 23], "io": [2, 17, 19, 21, 22, 23], "ip": [2, 10, 20, 23], "ipca": 13, "ipynb": [17, 23], "ipython": [2, 7, 9, 11, 13, 16, 17, 23], "iq": 8, "iri": [10, 11], "irreduc": 8, "irrelev": 7, "irrespect": [2, 23], "isn": 7, "isnul": 2, "isomap": 13, "issu": [0, 3, 11, 18], "it_arrai": 15, "item": [2, 15, 23], "items": [18, 23], "iter": [3, 4, 6, 8, 10, 15, 16, 20], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "itself": [7, 8, 14, 20, 23], "j": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 22, 23], "j1": 18, "j_": 8, "j_lasso_sk": 8, "j_ridge_sk": 8, "j_sk": 8, "jackknif": [8, 17, 23], "jacobian": [4, 15], "jason": 6, "jax": [17, 23], "jensen": [21, 23], "jerom": 22, "ji": [14, 18], "jit": 15, "jj": [2, 7, 8, 23], "jk": [2, 3, 8, 14, 18, 23], "jl": [2, 23], "jm": 18, "jnp": 15, "job": [0, 4, 10, 12], "join": [2, 6, 8, 9, 11, 23], "joint": [6, 7], "judg": 15, "judgement": 8, "julia": [17, 18], "jump": 20, "junk": 6, "jupit": 23, "jupyt": [0, 1, 2, 17, 22, 23], "just": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "justif": 2, "justifi": [5, 12], "k": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "k0": 9, "k1": 9, "kaggl": 8, "kappa_d": 20, "karl": [21, 23], "karush": 10, "katrin": [21, 23], "keep": [0, 2, 3, 6, 7, 8, 13, 15, 16, 18, 23], "keepdim": [3, 8, 12, 18], "kei": [2, 3, 5, 8, 14], "kept": [6, 8, 16], "kera": [2, 6, 17, 23], "kernel": [2, 3, 5, 17, 23], "kernel_regular": [3, 5], "kernel_s": 6, "kernelpca": 13, "kev": [2, 23], "kevin": [22, 23], "keyword": [18, 23], "kfold": 8, "kg": 3, "ki": 18, "kick": [3, 15], "kiener": 4, "kilomet": 8, "kind": [2, 4, 5, 6, 10, 14, 15, 16, 23], "kj": [8, 14, 18], "kjm": [17, 23], "kkt": 10, "kl": 20, "km": [14, 23], "kmean": 16, "kmeanspoint": 16, "kn_k": 16, "know": [0, 1, 2, 3, 4, 7, 8, 10, 15, 17, 23], "knowledg": [2, 17, 23], "known": [3, 5, 6, 7, 8, 9, 10, 11, 14, 18, 20, 22], "kondev": [2, 23], "kp": 20, "kpca": 13, "kroneck": 16, "kuhn": 10, "kvalsund": [21, 23], "kwown": [2, 23], "l": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "l0": 9, "l1": [2, 3, 5, 9, 23], "l1_l2": [3, 5], "l1regl": 7, "l2": [3, 5], "l_": 18, "l_1": 9, "l_2": [9, 15], "l_j": 14, "la": 15, "la_i": 14, "la_k": 14, "lab": [17, 23], "label": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 23], "labelencod": [9, 12], "labels": [8, 10, 11], "labels_shuffl": [2, 3], "laboratori": 19, "lack": [2, 23], "lagari": 4, "lagrang": [10, 13], "lambda": [2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 20, 23], "lambda_": 13, "lambda_0": 13, "lambda_1": [7, 10, 13], "lambda_2": [10, 13], "lambda_i": [10, 13], "lambda_iy_i": 10, "lambda_jy_iy_j": 10, "lambda_k": 10, "lambda_n": [7, 10], "lamda": 3, "land": [2, 10], "landmark": 10, "landscap": 15, "langl": [2, 8, 13, 20, 23], "languag": [2, 3, 6, 10, 17, 18, 22, 23], "lapack": [18, 23], "laplac": 7, "laptop": [0, 17], "larg": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 22, 23], "larger": [2, 5, 7, 8, 10, 12, 13, 15, 20, 23], "largest": [6, 10, 13], "lasso": [2, 9, 17, 23], "lasso_sk": 8, "last": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 18, 20, 21, 23], "latent": 6, "latent_dim": 6, "latent_point": 6, "latent_space_value_rang": 6, "later": [0, 2, 3, 6, 9, 10, 14, 15, 16, 17, 23], "latest": [0, 6, 17], "latest_checkpoint": 6, "latex": 23, "latter": [2, 5, 8, 9, 10, 13, 15, 18, 20, 23], "lattic": 14, "law": 2, "layer": [2, 6, 15, 23], "lbfg": [9, 11, 12], "lcc": [7, 8], "lda": 13, "ldot": [2, 8, 13, 23], "le": [7, 9, 12, 15, 20], "lead": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "leaf": 11, "leaki": 3, "leakyrelu": 6, "lear": 15, "learn": [5, 6, 7, 8, 9, 10, 11, 12, 14, 18, 21, 22], "learnabl": 5, "learner": 12, "learnig": 23, "learning_r": [10, 12], "learning_rate_init": [2, 3, 23], "learning_schedul": 15, "least": [2, 9, 10, 12, 13, 17, 18, 20], "leat": 15, "leav": [2, 3, 5, 7, 8, 11, 13, 23], "lectur": [2, 3, 7, 12, 13, 14, 15, 17, 18, 19, 21, 22], "lecturenot": [2, 17, 22, 23], "left": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "leftarrow": [10, 14], "legend": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 23], "leinonen": 23, "len": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 18, 23], "length": [1, 2, 3, 5, 6, 10, 11, 15, 17, 23], "length_of_sequ": 6, "leq": [2, 7, 9, 10, 15, 16, 20, 23], "less": [2, 3, 5, 6, 7, 8, 10, 11, 15, 17, 20, 23], "lessen": 3, "let": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "letter": [1, 2, 18, 20, 23], "level": [2, 3, 7, 8, 11, 17, 18, 19, 21, 23], "li": [10, 13], "lib": [], "liblinear": 12, "librari": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 18, 20, 22], "licens": [2, 3, 17, 23], "lie": [2, 8, 13, 20, 23], "life": [2, 3, 10, 14, 23], "lifetim": 15, "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23], "likelihood": [2, 3, 7, 11, 23], "lim_": 20, "limit": [2, 7, 8, 10, 14, 18, 23], "lin_clf": 10, "lin_model": 2, "lin_reg": 11, "linalg": [2, 4, 7, 8, 10, 13, 15, 18, 20, 23], "line": [0, 1, 2, 5, 8, 10, 13, 15, 23], "line1": 10, "line2": 10, "line3": 10, "line_model": 0, "line_ms": 0, "line_predict": 0, "linear": [1, 3, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20], "linear_model": [0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 15, 23], "linear_regress": 8, "linearli": 7, "linearloc": [8, 15], "linearregress": [0, 1, 2, 8, 9, 11, 23], "linearsvc": 10, "liner": [3, 5], "linerar": 12, "linewidth": [2, 4, 6, 8, 10, 11, 12], "link": [0, 2, 6, 11, 14, 17, 19, 21, 23], "linlag": 7, "linpack": [18, 23], "linreg": [2, 23], "linspac": [1, 2, 4, 5, 6, 8, 10, 11, 12, 15, 18, 20, 23], "linu": 6, "linux": [2, 3, 17, 23], "liquid": [2, 23], "list": [0, 2, 3, 4, 5, 6, 11, 17, 23], "listedcolormap": [11, 12], "literatur": [3, 9, 16, 22], "littl": [3, 5, 11, 14], "live": [1, 10], "ll": [2, 20, 23], "lle": 2, "lloyd": [6, 16], "lmb": [2, 4, 7, 8], "lmbd": [2, 3, 5, 23], "lmbd_val": [2, 3, 5, 23], "lmbda": 15, "ln": [3, 15], "load": [2, 3, 6, 8, 9, 11, 12], "load_boston": 2, "load_breast_canc": [3, 9, 11, 12, 13], "load_data": [5, 6], "load_digit": [3, 5], "load_iri": [10, 11], "loc": [5, 8, 9, 10, 11, 12, 23], "local": [0, 2, 3, 5, 9, 14, 15], "locat": [0, 4, 5, 10], "log": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 18, 23], "log10": [2, 7, 8], "log_": [2, 23], "log_clf": 12, "logarithm": [2, 7, 9, 18, 23], "logic": [2, 3, 11, 23], "login": 0, "logist": [2, 3, 4, 10, 11, 12, 13, 14, 15, 17], "logisticregress": [9, 11, 12, 13], "logit": 9, "logreg": [9, 11, 12, 13], "logspac": [2, 3, 5, 7, 8, 23], "long": [2, 3, 5, 6, 14, 15, 23], "longer": [4, 5, 10, 12, 16, 18, 20, 23], "loocv": 8, "look": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "loop": [1, 3, 6, 8, 12, 14, 16, 17, 18, 23], "lose": 3, "loss": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 18, 23], "loss_fil": 6, "lossfil": 6, "lost": 6, "lot": [1, 2, 3, 6, 8], "low": [2, 8, 11, 12, 13, 23], "lower": [1, 2, 3, 5, 8, 11, 12, 18], "lowercas": [18, 23], "lowest": [11, 15, 20], "lr": [3, 5, 6, 12], "lstat": 2, "lstm": 6, "lstm_2layer": 6, "lstsq": [2, 23], "lt": 8, "lu": [2, 7, 23], "lubksb": 18, "luckili": 4, "ludcmp": 18, "lux": 18, "lvert": 3, "lw": [2, 23], "m": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23], "m_": [11, 14], "m_1": 16, "m_h": [2, 23], "m_k": 16, "m_l": 14, "m_n": [2, 23], "m_p": [2, 23], "m_t": 15, "ma": 13, "machin": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 22], "machinelearn": [1, 2, 8, 17, 19, 21, 22, 23], "mackai": 22, "made": [2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 23], "mae": [2, 23], "magic": 6, "magnitud": [3, 8, 9, 15], "mai": [2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "mail": [19, 21], "main": [2, 3, 5, 6, 7, 8, 9, 11, 18, 22], "mainli": [2, 7, 8, 9, 11, 23], "maintain": 8, "major": [3, 8, 11, 12, 15, 18, 23], "make": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 22, 23], "make_axes_locat": 8, "make_moon": [10, 11, 12], "make_pipelin": [2, 8, 12], "makedir": [2, 8, 9, 11, 23], "malcondit": 18, "malign": [3, 9, 11], "mammographi": 7, "manag": [0, 2, 4, 5, 17, 23], "mandatori": [21, 23], "mani": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 22, 23], "manifold": 13, "manner": 5, "manual": 8, "map": [2, 3, 4, 8, 9, 10, 13, 14, 16, 20, 23], "margin": [2, 7, 10], "marit": [2, 23], "mark": 23, "marker": [2, 9, 18, 23], "markov": [17, 23], "marsaglia": 20, "mass": [2, 3, 7, 15], "massag": [2, 23], "masses2016": [2, 23], "masses2016ol": [2, 23], "masses2016tre": 2, "masseval2016": [2, 23], "master": [19, 21], "mat": [17, 23], "mat1100": [17, 23], "mat1110": [17, 23], "mat1120": [17, 23], "match": [0, 3, 6, 7, 15, 16], "materi": [0, 6, 7, 9, 15, 18, 19, 21], "math": [5, 9, 14, 15, 18, 20, 22, 23], "mathbb": [2, 6, 7, 8, 9, 10, 13, 14, 15, 16, 18, 20, 23], "mathbf": [2, 7, 8, 9, 10, 15, 18, 23], "mathcal": [3, 7, 8, 9, 15], "matheemat": 5, "mathemat": [2, 8, 13, 14, 15, 17, 18, 20, 22, 23], "mathemati": 23, "mathrm": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "matmul": [3, 4, 7], "matnat": 22, "matplotlib": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "matric": [1, 2, 3, 5, 6, 8, 9, 10, 13, 15, 17], "matrix": [2, 4, 5, 6, 8, 9, 10, 12, 15, 20], "matshow": 3, "matter": [4, 5, 15], "max": [2, 3, 4, 5, 6, 11, 12, 14, 15, 21, 23], "max_depth": [2, 11, 12], "max_diff": 4, "max_diff1": 4, "max_diff2": 4, "max_it": [2, 3, 10, 15, 23], "max_iter": 16, "max_leaf_nod": 12, "max_sampl": 12, "maxdegre": [2, 8, 12], "maxdepth": 12, "maxim": [3, 6, 7, 9, 10, 13], "maximum": [2, 4, 5, 7, 9, 10, 11, 12, 15, 16, 23], "maxpolydegre": [7, 8], "maxpooling2d": 5, "mbox": [7, 8], "mcculloch": 14, "md": 13, "mdoel": 6, "mean": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "mean_absolute_error": [2, 23], "mean_divisor": 16, "mean_i": 20, "mean_matrix": 16, "mean_squared_error": [0, 2, 6, 8, 9, 12, 23], "mean_squared_log_error": [2, 23], "mean_vector": 16, "mean_x": 20, "meaning": [2, 6, 9, 23], "meansquarederror": [2, 23], "meant": [5, 9, 12, 15], "measur": [1, 2, 3, 4, 7, 8, 11, 13, 14, 16, 20, 23], "mechan": [2, 6, 20, 23], "median": [2, 23], "medicin": 14, "medium": [6, 10, 15], "medv": 2, "meet": [2, 21], "mehta": [2, 23], "memori": [5, 6, 13, 14, 15, 18], "mention": [2, 14, 15, 20, 23], "mere": 2, "meshgrid": [4, 7, 8, 10, 11, 12, 13], "mess": 0, "messag": [7, 15], "messi": 4, "met": [2, 5, 10], "meteorolog": 11, "meter": 8, "method": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 16, 17, 18, 20, 22], "metion": 8, "metric": [0, 2, 3, 5, 8, 9, 11, 12, 16, 23], "metropoli": [17, 23], "mev": [2, 20, 23], "mgd": 15, "mglearn": [17, 23], "mgrid": 15, "mhjensen": [], "mi": 12, "mia": [21, 23], "microsoft": 22, "mid": 3, "midel": 6, "midnight": 0, "midpoint": 11, "might": [0, 2, 3, 4, 6, 8, 11, 15], "mild": 11, "millimet": 8, "million": [2, 23], "mimic": 14, "min": [2, 4, 7, 10, 11], "min_": [2, 4, 7, 16, 23], "min_samples_leaf": 11, "mind": [0, 2, 8, 15, 23], "mindboard": 6, "mine": [17, 23], "mini": [3, 13, 14, 15], "minibatch": [3, 13, 15], "minibathc": 15, "miniforge3": [], "minim": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "minima": [2, 3, 9, 15, 23], "minimum": [2, 3, 4, 8, 10, 11, 13, 15], "minmaxscal": 2, "minor": 20, "minst": 3, "minu": 9, "mirjalili": 23, "mirror": 11, "misc": 8, "misclassif": [10, 11, 12], "misclassifi": [10, 12], "miser": 2, "mismatch": 3, "miss": [2, 9, 12], "mistak": 6, "mit": 22, "mix": [3, 4, 23], "mixtur": 15, "mk": [11, 18], "mkdir": [2, 8, 9, 11, 23], "ml": [2, 3, 12, 15, 18], "mlab": 20, "mle": [7, 9], "mlp": 3, "mlpclassifi": 3, "mlpregressor": [2, 23], "mm": 18, "mn": [14, 20], "mnist": [3, 13], "mod": 20, "mode": [19, 21, 23], "model": [1, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 20, 22], "model_select": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "moder": 12, "modern": [2, 8, 9, 17, 23], "modif": [4, 14, 15], "modifi": [2, 3, 5, 7, 9, 10, 12, 14, 15, 23], "modul": [1, 2, 18, 23], "modular": 20, "modulo": 20, "moe": 13, "moment": [7, 8, 15, 20], "mondai": [21, 23], "monitor": 15, "monoton": [7, 14, 20], "mont": [2, 8, 17, 20, 22, 23], "montli": 1, "moor": [7, 8], "more": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "moreov": [2, 5], "morten": [21, 23], "mortenhj": 23, "most": [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "mostli": [3, 13], "motion": [2, 15], "motiv": [3, 6], "move": [0, 1, 2, 6, 7, 8, 9, 11, 14, 15, 16, 20], "mpl": [9, 23], "mpl_toolkit": [4, 8, 15], "mplot3d": [4, 8, 15], "mplregressor": 3, "mse": [0, 1, 2, 6, 7, 8, 11, 12, 23], "mse_simpletre": 12, "mselassopredict": 7, "mselassotrain": 7, "mseownridgepredict": 8, "msepredict": 7, "mseridgepredict": [2, 7, 8], "msetrain": 7, "msle": [2, 23], "mt": [9, 14], "mu": [2, 8, 13, 15, 20, 23], "mu0": 20, "mu1": 20, "mu2": 20, "mu_": [8, 20], "mu_i": 8, "mu_n": 13, "mu_x": 20, "much": [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "multi": [2, 3, 5, 9, 17, 23], "multiclass": [3, 9], "multidimension": [13, 14, 23], "multilay": 3, "multinomi": 9, "multipl": [0, 4, 6, 7, 8, 9, 14, 15, 20], "multipli": [5, 7, 8, 13, 15, 18, 20], "multiplum": 10, "multivari": [2, 4, 12, 13, 17, 20, 23], "multivariate_norm": [13, 16], "multpli": 1, "murphi": [13, 22, 23], "must": [0, 3, 4, 7, 8, 10, 12, 14, 15, 16, 20], "mutat": 9, "mutual": [3, 5, 8, 15], "mx_": 20, "my": 23, "myenv": [], "myriad": [2, 17, 23], "mz1": 20, "mz2": 20, "m\u00f8svatn": 8, "n": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "n1": 18, "n2": 18, "n_": [3, 4, 5, 10, 14, 20], "n_0": [14, 20], "n_boostrap": [8, 12], "n_bootstrap": 8, "n_categori": [3, 5], "n_cluster": 16, "n_compon": 13, "n_epoch": 15, "n_estim": 12, "n_examples_to_gener": 6, "n_featur": 3, "n_filter": 5, "n_hidden": 4, "n_hidden_neuron": [2, 3, 23], "n_i": 20, "n_input": [2, 3, 5], "n_instanc": 11, "n_job": 12, "n_k": 16, "n_l": [14, 20], "n_layer": 3, "n_m": 11, "n_neuron": 3, "n_neurons_connect": 5, "n_neurons_layer1": 3, "n_neurons_layer2": 3, "n_point": 16, "n_sampl": [8, 10, 11, 12, 16], "n_split": 8, "n_step": 6, "n_t": 4, "n_x": 4, "nabla": [3, 15], "nabla_": [4, 15], "nabla_w": 15, "nag": 15, "naimi": [2, 23], "naiv": 9, "naive_kmean": 16, "name": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23], "narrow": 15, "nation": [3, 7], "nativ": [17, 23], "natur": [2, 3, 6, 10, 11, 14, 15, 20, 22, 23], "navier": 14, "navig": 0, "nb": 20, "nb_": 18, "nbconvert": 23, "nd": 16, "ndarrai": 8, "ne": [11, 12, 18, 20], "nearest": [3, 5, 8, 13], "nearli": 15, "neat": 23, "neccesari": 8, "necess": 4, "necessari": [2, 3, 5, 6, 10, 16, 23], "necessarili": [2, 6, 13, 20, 23], "necesserali": 7, "neck": 9, "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "neg": [2, 3, 5, 7, 8, 9, 12, 15, 18, 20, 23], "neg_mean_squared_error": 8, "neglect": 20, "neglig": 20, "neighbor": [5, 8, 13], "neither": [6, 15], "neq": [15, 16, 20], "nervou": 14, "nest": [11, 14], "nesterov": 15, "net": [4, 6, 14], "netlib": [18, 23], "network": [2, 11, 15, 17, 22], "neural": [2, 15, 17, 22], "neural_network": [2, 3, 4, 23], "neuralnetwork": 3, "neuron": [3, 4, 5, 6, 14], "neutral": [2, 23], "neutron": [2, 23], "never": [3, 6, 8, 11, 20], "new": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "new_chang": 15, "new_hobbit": 23, "newaxi": [2, 5, 8, 11], "newli": [2, 23], "newton": [3, 9, 10, 15, 20], "next": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 23], "next_guess": 15, "next_input": 6, "ng": 3, "ni": 16, "nice": [2, 3, 7, 13, 23], "niter": 15, "nitric": 2, "nlambda": [2, 7, 8], "nlp": 22, "nm": 20, "nm_n": [2, 23], "nmse": 8, "nn": [4, 7, 8, 14, 18, 23], "nn_model": 3, "nnmin": 4, "node": [3, 5, 11, 12, 14], "nois": [2, 6, 7, 8, 10, 11, 12, 15, 23], "noise_dimens": 6, "noisi": [3, 8], "non": [2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "none": [2, 3, 4, 6, 7, 11, 12, 15, 20, 23], "nonlinear": [5, 8, 10, 11, 13, 14], "nonneg": [8, 11, 15], "nonparametr": 8, "nonsens": 20, "nonsingular": 18, "nonumb": [5, 9, 10, 15, 18], "nor": [3, 6, 15], "norm": [2, 3, 7, 8, 10, 13, 15, 23], "normal": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "normali": [18, 23], "norwai": [8, 23], "notat": [2, 4, 7, 8, 15, 16, 20, 23], "note": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 22, 23], "notebook": [0, 1, 2, 3, 5, 11, 17, 23], "noth": [3, 4, 7, 10, 14, 16, 20], "notic": [6, 7, 14, 15, 18, 20, 23], "notion": 5, "novel": [5, 8, 12, 23], "novemb": [3, 21, 23], "now": [0, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23], "nowadai": [2, 3, 5, 11, 17, 23], "nox": 2, "np": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "npr": 4, "nsampl": 8, "nt": 4, "nu": 20, "nuclear": 7, "nuclei": [2, 20, 23], "nucleon": [2, 23], "nucleu": [2, 23], "num": 6, "num_coordin": 4, "num_hidden_neuron": 4, "num_it": 4, "num_neuron": 4, "num_neurons_hidden": 4, "num_point": 4, "num_tre": 12, "num_valu": 4, "number": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 23], "numberid": 9, "numberparamet": 5, "numer": [2, 7, 8, 11, 12, 13, 14, 15, 17, 18, 22, 23], "numpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "nunmpi": 7, "nx": 4, "ny": 20, "o": [2, 3, 6, 7, 8, 9, 10, 11, 13, 18, 21, 22, 23], "obei": [8, 13, 15], "object": [0, 2, 3, 6, 10, 12, 18, 23], "obliqu": 7, "observ": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "obtain": [2, 3, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "obviou": [7, 8, 13, 20], "obviouli": 23, "obvious": [2, 6, 7, 8, 18, 23], "occupi": 2, "occur": [2, 8, 10, 11, 18, 20, 23], "octob": [21, 23], "od": 2, "odd": [2, 5, 9, 23], "odenum": 4, "odesi": 4, "oen": 2, "off": [3, 5, 6, 7, 11, 15, 20], "offer": [8, 13, 17, 18, 19, 21, 23], "offic": [21, 23], "offici": [19, 23], "often": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "ofter": [18, 23], "ol": [2, 15], "old": [0, 3, 7, 12, 15], "ols_paramet": 1, "ols_sk": 8, "ols_svd": 8, "olstheta": [2, 7], "omega": [4, 5, 8], "omega_0": 5, "omit": [2, 7, 23], "onc": [3, 8, 11, 13, 15], "one": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 23], "onehot": 3, "onehot_vector": 3, "onehotencod": 11, "ones": [1, 2, 4, 7, 8, 10, 11, 12, 13, 15, 18, 23], "ones_lik": 6, "onl": 5, "onli": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "onlin": [0, 13, 19], "onto": [7, 13], "open": [0, 2, 3, 6, 8, 9, 11, 17, 19, 21, 23], "oper": [0, 1, 2, 3, 5, 7, 8, 12, 13, 14, 15, 17, 20, 23], "operation": 20, "oplu": 20, "opmiz": 15, "opportun": 2, "oppos": [8, 15], "opposit": [3, 7, 10], "opt": [3, 7, 23], "optim": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16], "optimis": [3, 5], "option": [0, 2, 3, 5, 7, 8, 10, 13, 18], "optmiz": [3, 10, 15], "oral": 23, "orang": 2, "order": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "ordinari": [2, 4, 5, 9, 13, 15, 17], "oreilli": [22, 23], "org": [1, 2, 5, 6, 17, 18, 22, 23], "organ": [8, 9, 12, 18], "orient": [3, 7, 20], "origin": [0, 2, 5, 7, 8, 10, 13, 14, 15, 18, 23], "orthogn": 7, "orthogon": [2, 7, 8, 10, 13, 15, 18, 23], "orthonorm": 7, "os": [21, 23], "oscar": 3, "oscil": [5, 15], "oskar": 23, "oskarlei": 23, "oslo": [2, 17, 19, 21, 23], "osx": [2, 17, 23], "other": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 15, 16, 17, 19, 20, 21, 22], "otherwis": [2, 3, 6, 9, 15, 18, 23], "ouput": [7, 9, 14], "our": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20], "ourmodel": 2, "ourselv": [2, 7, 8, 10, 13, 15, 23], "out": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "out_fil": 11, "outcom": [2, 9, 11, 12, 14, 20], "outdoor": 11, "outer": [8, 14, 15], "outfil": 6, "outlier": [2, 10, 23], "outlin": [8, 12, 13], "outlook": 11, "outperform": 12, "output": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "output_bia": 3, "output_bias_gradi": 3, "output_shap": 6, "output_weight": 3, "output_weights_gradi": 3, "outputlayer1": 14, "outputlayer2": 14, "outsid": 6, "over": [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 14, 15, 18, 23], "over1": 15, "overal": [3, 12], "overcast": 11, "overcom": [14, 15], "overdetermin": [2, 23], "overfit": [2, 3, 5, 8, 11, 12, 15], "overflow": 7, "overhead": 14, "overlap": [5, 9, 10, 11], "overlin": [2, 7, 8, 11, 12, 13, 16, 18, 23], "overst": 2, "overtrain": 6, "overview": 5, "own": [1, 6, 7, 8, 10, 14, 15, 17, 18], "owner": 2, "ownmsepredict": 2, "ownmsetrain": 2, "ownridgetheta": [2, 8], "ownypredictridg": 2, "ownytilderidg": 2, "oxid": 2, "p": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "p0": 4, "p1": 4, "p_": [4, 6, 10, 11], "p_hidden": 4, "p_i": [7, 20], "p_j": 20, "p_n": 20, "p_output": 4, "p_x": 20, "pack": [2, 23], "packag": [0, 2, 3, 5, 6, 7, 10, 13, 15, 17, 20], "packtpub": 23, "packtpublish": 23, "pad": [5, 6], "page": [2, 17, 23], "pai": [0, 2, 3, 11, 15], "pair": [2, 4, 5, 11, 17, 20, 23], "paltform": 0, "panda": [2, 6, 7, 8, 9, 11, 13, 17], "panel": 23, "paper": 3, "paradigm": [2, 23], "parallel": [12, 15, 17, 18, 23], "param": 4, "paramat": 4, "paramet": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20], "parameter": [2, 8, 12, 23], "parametr": [2, 8, 23], "paramt": [5, 7], "part": [2, 3, 5, 7, 8, 12, 18, 19, 20, 21, 23], "partial": [1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "particip": [0, 17, 19, 21, 23], "particl": [2, 6, 15, 20, 23], "particular": [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 20, 22, 23], "particularli": [7, 8, 10, 13, 15, 20], "partit": [3, 6, 11], "partli": [8, 23], "partner": 0, "pass": [4, 5, 14, 16], "past": [12, 20], "patch": [8, 20], "path": [2, 6, 8, 9, 11, 17, 23], "patient": 9, "patter": 6, "pattern": [2, 5, 6, 14, 22, 23], "pauli": [2, 23], "pc": [13, 17], "pca": [2, 9, 17, 23], "pd": [2, 6, 7, 8, 9, 11, 13, 23], "pde": 4, "pdf": [0, 1, 2, 5, 6, 7, 8, 11, 22, 23], "pedagog": [2, 23], "penal": 8, "penalti": [8, 15], "penros": [7, 8], "pentagon": 15, "peopl": [2, 3, 11, 15, 17], "per": [2, 3, 8, 19, 21, 23], "percentag": [2, 12, 13, 21], "perceptron": [2, 3, 9, 23], "peregrin": 23, "perfect": [2, 3, 15, 23], "perfectli": [6, 8], "perform": [1, 2, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 20, 23], "performac": 6, "perhap": [2, 7, 15, 23], "perimet": 3, "period": [3, 6, 20], "permiss": 0, "permut": 13, "persist": 15, "person": [1, 7, 8, 9, 19, 21, 23], "perspect": 22, "pertin": [14, 23], "petal": [10, 11], "peter": 22, "phantom": 20, "phase": [8, 14], "phenomena": 20, "phi": 10, "phi_k": 10, "philosophi": 15, "phone": [21, 23], "photo": [6, 23], "phrase": [2, 23], "physic": [2, 3, 6, 9, 14, 15, 20, 21, 22, 23], "pi": [4, 5, 7, 8, 9, 11, 14, 15, 20], "pick": [3, 11, 12, 13, 15, 16], "pickl": 3, "pictur": [2, 23], "pie": [17, 23], "piec": [13, 16], "pillow": [2, 17, 23], "pinv": [7, 8, 15], "pip": [0, 2, 3, 17, 23], "pip3": [2, 3, 23], "pipelin": [2, 8, 10, 12], "pippin": 23, "pit": 6, "pitfal": 8, "pitt": 14, "pixel": [3, 5, 6, 23], "pixel_height": [3, 5], "pixel_width": [3, 5], "place": [0, 2, 6, 8, 10, 15, 18, 23], "plai": [2, 5, 6, 7, 8, 10, 13, 17, 23], "plain": [10, 12, 14, 15, 16], "plan": [8, 11, 21, 22, 23], "plane": [10, 11], "plateau": 7, "platform": [17, 23], "plausibl": 14, "pleas": [15, 21, 23], "plenti": 3, "plethora": [5, 14], "plot": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "plot_confusion_matrix": [9, 12], "plot_count": 8, "plot_cumulative_gain": [9, 12], "plot_data": 3, "plot_dataset": 10, "plot_decision_boundari": [11, 12], "plot_import": 12, "plot_max": 6, "plot_min": 6, "plot_model": 6, "plot_numb": 6, "plot_predict": 10, "plot_regression_predict": 11, "plot_result": 6, "plot_roc": [9, 12], "plot_surfac": [4, 8, 15], "plot_train": 11, "plot_tre": [11, 12], "plt": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "plu": [2, 5, 7, 9, 23], "pm": 10, "pmatrix": 4, "pml": 22, "pn": 5, "png": [2, 6, 8, 9, 11, 23], "point": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "point_1": 6, "point_2": 6, "poisson": [17, 20, 23], "poli": [8, 10], "poly100_kernel_svm_clf": 10, "poly3": 2, "poly3_plot": 2, "poly_featur": [0, 10, 11], "poly_features10": 11, "poly_fit": 11, "poly_fit10": 11, "poly_kernel_svm_clf": 10, "poly_model": 0, "poly_ms": 0, "poly_predict": 0, "polydegre": [2, 7, 8, 12], "polygon": 15, "polym": 14, "polynomi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 23], "polynomial_featur": [0, 1, 8], "polynomial_svm_clf": 10, "polynomialfeatur": [0, 1, 2, 8, 10, 11], "polytrop": [2, 8], "pool": 5, "pool_siz": 5, "poor": [3, 15], "poorli": 2, "popul": [2, 7, 23], "popular": [0, 2, 3, 5, 8, 9, 10, 11, 13, 14, 17, 18, 20], "popularli": [2, 23], "portabl": 12, "portion": [13, 15], "pose": [2, 6, 7, 8, 13, 20, 23], "posit": [2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 18, 20, 23], "possibl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 23], "possibli": [8, 10, 15], "posterior": 7, "postpon": 2, "postul": 7, "potenti": [2, 5, 7, 8, 14, 15], "pott": 14, "power": [2, 3, 7, 8, 10, 11, 14, 15, 23], "pp": [7, 8], "practic": [1, 2, 7, 8, 9, 10, 20], "practition": [2, 3, 5, 23], "pre": 23, "preced": [3, 13, 14, 20], "preceed": 6, "preceq": 10, "precis": [2, 4, 7, 13, 15, 18, 20, 23], "pred": 8, "predicit": 2, "predict": [0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 17, 22, 23], "predict_prob": 3, "predict_proba": [9, 12], "predictor": [2, 7, 8, 9, 11, 12, 13, 23], "prefer": [2, 3, 8, 10, 11, 13, 15, 17, 23], "prepar": [2, 8, 18, 23], "preprocess": [0, 1, 2, 6, 8, 9, 10, 11, 12, 13], "prerequisit": 2, "presenc": 15, "present": [2, 7, 8, 9, 11, 14, 15, 18, 20, 23], "preserv": [5, 13, 18], "press": [0, 15, 22], "pretrain": [3, 6], "pretti": [2, 6, 10, 11, 17, 23], "prev_centroid": 16, "prevent": [15, 20], "previou": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20], "previous": [4, 5, 11, 12, 20], "price": [2, 6, 11, 15], "primal": 10, "primari": [2, 9, 23], "prime": 20, "princip": [2, 7, 9, 17, 23], "principl": [2, 8, 9, 10, 16, 23], "print": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "print_funct": [10, 11], "printout": [2, 23], "prior": [2, 7, 8, 23], "privat": 2, "prob": [3, 20], "probabilist": [2, 22, 23], "probabl": [2, 3, 5, 6, 8, 9, 12, 15, 17, 23], "problem": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20], "probml": 22, "proce": [2, 7, 8, 9, 10, 11, 12, 13, 15, 18, 23], "procedur": [4, 6, 7, 8, 10, 12, 13, 15], "proceed": 18, "process": [2, 4, 6, 8, 11, 12, 14, 15, 17, 18, 20, 22, 23], "prod": 22, "prod_": [3, 7, 9], "produc": [2, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 23], "product": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 18, 23], "profess": [2, 23], "program": [0, 2, 3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 20, 21, 23], "programm": 18, "progress": [3, 6, 16], "prohibit": 8, "project": [0, 2, 3, 4, 5, 7, 13, 15, 17, 19], "project_root_dir": [2, 8, 9, 11, 23], "promin": 14, "promis": 10, "promot": [21, 23], "prone": [0, 11], "pronounc": [15, 17, 23], "proof": [2, 13, 14, 15, 23], "propag": [4, 5, 15], "proper": [2, 4, 8, 9], "properli": [3, 8, 10, 12, 15], "properti": [1, 2, 3, 5, 14, 15, 18, 23], "proport": [2, 3, 7, 11, 13, 15, 20, 23], "propos": [3, 6, 8, 12, 23], "propto": [7, 15], "proton": [2, 23], "prove": [5, 15], "provid": [2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 17, 18, 20, 23], "proxi": [3, 15], "prune": 11, "pseudo": [18, 20], "pseudoinv": 7, "pseudoinvers": [7, 8], "pseudorandom": [8, 20], "psychologi": [2, 23], "pt": 15, "public": [0, 2, 17, 23], "pull": 0, "punish": [2, 3, 23], "pure": [5, 11, 20], "purest": 11, "puriti": 11, "purpos": [2, 5, 12, 14, 16, 23], "push": 0, "put": 3, "py": 7, "pycod": 23, "pydata": 17, "pydot": 11, "pyhton2": 23, "pylab": [9, 23], "pypi": 17, "pyplot": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "pythagora": 7, "python": [3, 4, 5, 7, 8, 10, 13, 14, 15, 16, 20], "python2": 2, "python3": [2, 17, 23], "pytorch": [2, 17, 23], "q": [7, 8, 10, 13, 20], "qp": 10, "qquad": [4, 13, 15, 18], "qr": [7, 8, 18], "quad": [3, 15, 18], "quadrat": [2, 10, 11, 15, 23], "qualit": [6, 11, 20], "qualiti": [2, 11, 17, 23], "quantifi": 3, "quantil": 12, "quantit": [2, 8, 11, 23], "quantiti": [1, 2, 4, 7, 8, 9, 11, 12, 13, 14, 16, 18, 20, 23], "quantum": [6, 14, 22, 23], "quartil": 2, "quench": 7, "queri": 11, "question": [2, 7, 8, 11, 13, 14, 15, 21, 23], "qugan": 6, "quick": [6, 20], "quickli": [3, 5, 11, 13, 15], "quit": [0, 3, 7, 8, 11, 12, 14], "quot": 6, "r": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "r2": [2, 7, 8, 23], "r2_score": [2, 23], "r2score": [2, 23], "r_1": 11, "r_2": 11, "r_j": 11, "r_m": 11, "rad": 2, "radial": [2, 10, 14], "radioact": 20, "radiu": [2, 3], "rain": 11, "ramp": 3, "ran0": 20, "ran1": 20, "ran2": 20, "ran3": 20, "rand": [0, 2, 6, 7, 8, 11, 12, 15, 18, 23], "randint": [8, 11, 15], "randn": [0, 2, 3, 4, 7, 8, 11, 13, 15, 23], "random": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 17, 18, 23], "random_forest_model": 12, "random_index": 15, "random_indic": [3, 5], "random_st": [2, 9, 10, 11, 12, 13], "randomforestclassifi": 12, "randomli": [3, 8, 11, 15, 16], "rang": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "rangl": [2, 8, 13, 20, 23], "rangle_x": 20, "rank": 7, "rankdir": 6, "raphson": [3, 10, 15], "rapidli": 2, "rare": [3, 15], "raschka": 23, "rasckha": 23, "rate": [2, 3, 4, 5, 6, 10, 11, 12, 14, 15], "rather": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "ratio": [6, 9, 11, 12, 13], "rational": [2, 23], "ravel": [7, 8, 9, 10, 11, 12, 13, 15, 18], "raw": 5, "rbf": [10, 13, 14], "rbf_kernel_svm_clf": 10, "rbf_pca": 13, "rc": [2, 20], "rcond": [2, 23], "rcparam": [3, 5, 9, 10, 11, 12, 20, 23], "re": [0, 4, 6, 15], "reach": [3, 6, 7, 8, 11, 12, 14, 15, 16], "read": [1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 18, 20, 22], "read_csv": [2, 8, 9, 11], "read_fwf": [2, 23], "reader": [2, 8, 18, 20, 23], "readi": [2, 3, 7, 8, 10, 12, 13, 14, 18, 23], "readili": 3, "readm": 0, "readthedoc": 17, "real": [1, 2, 3, 6, 9, 12, 13, 14, 18], "real_loss": 6, "real_output": 6, "realist": [10, 23], "realiti": 20, "realiz": [3, 14], "realli": [2, 3, 23], "rearrang": 15, "reason": [2, 3, 5, 6, 12, 15, 22, 23], "reassign": 3, "recal": [7, 8, 11, 12, 13, 14, 18, 20, 23], "recast": 5, "receiv": [3, 5, 12, 14, 20], "recent": [2, 8, 15, 22], "recept": [5, 14], "receptive_field": 5, "recip": [2, 8, 9, 18, 23], "reciproc": 7, "recogn": [2, 6, 7, 12, 23], "recognit": [2, 3, 5, 14, 22, 23], "recommen": 23, "recommend": [0, 2, 4, 5, 6, 7, 8, 10, 15, 17, 18, 22], "reconsid": 11, "reconstruct": 13, "record": [12, 19, 21, 23], "recreat": 0, "rectangl": [11, 15], "rectangular": 7, "rectifi": [3, 5, 14], "recur": [2, 17, 23], "recurr": [2, 3, 17, 23], "recurs": [11, 17, 18, 23], "red": [2, 5, 6, 8, 10, 11], "redefin": [2, 12, 23], "reduc": [3, 5, 7, 8, 11, 12, 13, 15, 23], "reduct": [2, 12, 13, 17, 20, 23], "refer": [2, 3, 4, 5, 7, 8, 13, 14, 15, 16, 18, 22, 23], "referenc": 4, "refin": 14, "refit": 8, "reflect": [2, 3, 6, 7, 20, 23], "refresh": [17, 23], "refreshprogrammingskil": 23, "reg": [12, 13], "regard": [3, 11, 15], "regardless": [1, 14], "region": [5, 6, 8, 11, 14], "regist": [8, 20], "reglasso": 7, "regr_1": [2, 11], "regr_2": [2, 11], "regr_3": [2, 11], "regress": [1, 3, 10, 13, 14, 17, 18], "regressor": [2, 9, 12], "regridg": [2, 7, 8], "regular": [2, 5, 6, 7, 8, 9, 11, 15, 21, 23], "regularli": 0, "reilli": [2, 22, 23], "reinforc": [2, 10, 17, 23], "reiter": 3, "reject": 9, "rel": [2, 6, 8, 9, 11, 14, 15, 20, 23], "relat": [2, 3, 5, 6, 7, 13, 15, 16, 18, 20, 23], "relationship": [2, 6, 11, 23], "relativeerror": [2, 23], "releas": [3, 17, 23], "relev": [2, 3, 7, 9, 13, 17, 20, 23], "reli": [2, 8, 10], "reliabl": [9, 20], "relu": [5, 6, 23], "remain": [3, 4, 6, 8, 14, 18, 20], "remaind": 20, "reman": 4, "remark": 3, "rememb": [2, 10, 15, 18, 23], "remind": [2, 7, 13, 15, 18, 20], "remot": 0, "remov": [2, 6, 7, 8], "renam": 0, "render": [2, 23], "reorder": [7, 9], "reorgan": [2, 23], "repeat": [2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 16, 18, 20, 23], "repeated": 23, "repeatedli": [2, 8, 12, 15], "repet": 5, "repetit": [8, 23], "rephras": 15, "replac": [2, 3, 5, 6, 7, 8, 12, 14, 16, 17, 23], "replica": 8, "repo": 0, "report": 23, "repositori": [2, 6, 23], "repres": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23], "represent": [2, 3, 5, 8, 20, 23], "representd": 5, "reproduc": [0, 1, 2, 7, 8, 11, 14, 17, 20, 23], "repuls": [2, 23], "request": [2, 15], "requir": [0, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 18, 23], "res1": 4, "res2": 4, "res3": 4, "res_analyt": 4, "res_analytical1": 4, "res_analytical2": 4, "res_analytical3": 4, "resaml": 8, "resampl": [2, 9, 12, 17, 23], "rescal": [2, 13, 14], "rescu": 7, "reseach": 8, "research": [2, 6, 15, 17, 22, 23], "resembl": [8, 20], "reserv": [3, 7, 8, 20], "reshap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 18, 23], "residenti": 2, "residu": [2, 7, 15, 23], "resiz": 7, "resourc": 23, "respect": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 20, 23], "respond": 14, "respons": [2, 9, 11, 14, 23], "rest": [2, 7], "restat": [2, 14, 23], "restor": 6, "restored_discrimin": 6, "restored_gener": 6, "restrict": [2, 5, 11, 14, 23], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "retail": 2, "retain": [7, 8], "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 20, 23], "return_data": 16, "return_sequ": 6, "return_x_i": 11, "reus": [3, 5, 8], "reveal": [2, 14, 23], "revers": [3, 18], "review": [17, 18], "revisit": 16, "revolut": 23, "reward": [2, 6, 23], "rewrit": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "rewritten": [4, 8, 10, 12, 20], "rewrot": 15, "rf": 12, "rgb": 5, "rgoj5yh7evk": 17, "rh": 8, "rho": [2, 12, 15], "rho_1": 12, "rho_2": 12, "rho_m": 12, "rich": [2, 23], "ride": 11, "rideclass": 11, "ridedata": 11, "ridg": [9, 13, 15, 17, 23], "ridge_sk": 8, "ridgetheta": 7, "right": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "right_sid": 4, "rightarrow": [2, 3, 7, 8, 10, 13, 14, 15, 20, 23], "rigor": [2, 23], "ring": 8, "rise": [2, 23], "risk": [2, 15, 23], "rival": 6, "river": 2, "rlm": 23, "rm": [2, 20], "rmse": 2, "rmsporp": 15, "rmsprop": [3, 5, 6, 15], "rnd_clf": 12, "rng": 20, "rnn": [6, 14], "rnn1": 6, "rnn2": 6, "rnn_2layer": 6, "rnn_input": 6, "rnn_output": 6, "rnn_train": 6, "rntrick1": 20, "rntrick2": 20, "rntrick3": 20, "rntrick4": 20, "ro": [2, 15, 23], "robert": 22, "robust": [2, 23], "robustscal": 2, "roc": [9, 12], "role": [2, 4, 7, 8, 10, 17, 23], "roll": 8, "room": [2, 21, 23], "root": [0, 2, 7, 11, 15, 20], "rot": 23, "rotat": [3, 10, 11, 12], "rotation_matrix": 11, "roughli": [3, 5], "round": [2, 9, 11, 15], "routin": [15, 18, 23], "row": [1, 2, 3, 4, 7, 8, 11, 13, 18, 23], "rr": 7, "rrr": 7, "rug": 15, "rule": [2, 3, 7, 8, 15, 23], "run": [0, 2, 3, 4, 6, 7, 8, 10, 11, 13, 15, 17, 23], "runtim": [0, 3, 8, 16], "rust": [2, 17, 18, 23], "rvert": 3, "rvert_2": 3, "s_": [5, 8], "s_1": 8, "s_i": [8, 9], "s_j": 8, "s_k": 8, "saddl": 15, "sai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "said": [8, 11, 15], "sake": [2, 7, 9, 13, 23], "sale": [2, 23], "sam": 23, "same": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 23], "samm": 12, "sampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 23], "sample_vari": 16, "sampleexptvari": 20, "samwis": 23, "sastri": 13, "satisfactori": [2, 23], "satisfi": [3, 4, 5, 8, 10, 15, 18, 20], "satur": [3, 8], "save": [2, 6, 8, 9, 11, 15, 23], "save_fig": [2, 8, 9, 11, 12, 23], "savefig": [2, 6, 8, 9, 11, 20, 23], "savetxt": 6, "saw": 7, "scalabl": 12, "scalar": [4, 7, 8, 12], "scale": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 23], "scale_mean": 6, "scale_std": 6, "scaler": [2, 9, 10, 11, 12, 13], "scan": [7, 9], "scari": 7, "scatter": [0, 2, 3, 8, 9, 10, 11, 16, 23], "scenario": [8, 15], "schedul": 15, "scheme": [3, 15], "schrage": 20, "sch\u00f8yen": 8, "scienc": [2, 3, 12, 14, 15, 17, 19, 20, 21, 22], "scientif": [2, 17, 23], "scientist": [2, 23], "scikit": [0, 1, 5, 7, 8, 10, 11, 12, 15, 17, 18, 22], "scikit_learn": 2, "scikitlearn": 23, "scikitplot": [9, 12], "scipi": [2, 5, 7, 8, 15, 17, 18, 23], "scl": 8, "scm": 0, "score": [0, 1, 2, 3, 5, 8, 9, 11, 12, 13, 21, 23], "scores_kfold": 8, "scratch": [1, 3, 15], "sdg": 15, "seaborn": [2, 3, 5, 8, 9, 23], "seamless": [2, 17, 23], "search": [0, 2, 3, 5, 7, 11, 15, 23], "sebastian": 23, "sebastianraschka": 23, "sec": 8, "second": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 23], "secondeigvector": 13, "secondli": 14, "section": [1, 6, 13, 18, 20], "sector": 2, "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 23], "seed": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 20, 23], "seed_imag": 6, "seek": [3, 4, 10], "seem": [3, 5, 6], "seemingli": [2, 23], "seen": [2, 3, 5, 7, 12, 14, 20], "segment": 15, "seismic": 8, "seldomli": [2, 23], "select": [0, 3, 7, 8, 10, 11, 12, 13, 19, 20, 21, 22, 23], "selevet": 0, "self": [3, 7], "sell": 6, "semest": [9, 19], "semi": [10, 15], "semilogx": 8, "send": [7, 14, 15, 21, 23], "senior": [19, 21], "sens": [2, 6, 8, 10, 23], "sensibl": 5, "sensit": [2, 7, 8, 11, 15, 23], "sent": 4, "sentenc": [6, 14], "separ": [2, 3, 4, 6, 8, 10, 11, 14, 16, 17, 20, 23], "septemb": 23, "sequenc": [5, 6, 9, 11, 12, 14, 15, 17, 18, 20, 23], "sequenti": [3, 5, 6, 12, 14, 20], "seri": [2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 18, 23], "serif": [9, 20, 23], "serv": [2, 3, 4, 5, 7, 9, 15, 22, 23], "session": [0, 3, 19, 21, 23], "set": [1, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 20, 21], "set_major_formatt": 8, "set_major_loc": 8, "set_tick": [3, 10], "set_ticklabel": 3, "set_titl": [2, 3, 4, 5, 9, 14, 16, 23], "set_xlabel": [2, 3, 4, 5, 9, 14, 23], "set_xlim": [9, 14], "set_xticklabel": 3, "set_ylabel": [2, 3, 4, 5, 9, 23], "set_ylim": [9, 14], "set_ytick": 9, "set_yticklabel": [3, 8], "set_zlim": 8, "seth": 6, "setminu": 8, "setosa": [10, 11], "setosa_or_versicolor": 10, "setp": 8, "setup": [3, 6, 8, 10, 17, 23], "sever": [1, 2, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "sgd": [3, 5], "sgd_clf": 10, "sgdclassifi": 10, "sgdreg": 15, "sgdregressor": 15, "sgn": 7, "shallow": 15, "shape": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "share": [0, 3, 5, 23], "shareabl": 0, "she": 9, "shift": [0, 3, 8, 14, 20], "ship": 5, "shire": 23, "short": [6, 7], "shortcom": 15, "shorten": 6, "shorter": 20, "shorthand": 23, "shortli": [18, 23], "should": [0, 2, 4, 5, 7, 8, 10, 11, 13, 14, 18, 20, 23], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "show_shap": 6, "shown": [2, 6, 7, 10, 14, 15, 18], "shrink": [5, 7, 8, 10, 13], "shrinkag": [7, 8], "shrunk": 13, "shuffl": [2, 3, 6, 8, 15], "side": [2, 4, 7, 10, 14, 15, 18, 23], "sigh": [17, 23], "sigma": [2, 3, 7, 8, 9, 12, 13, 14, 15, 18, 20, 23], "sigma0": 20, "sigma1": 20, "sigma2": 20, "sigma_": [7, 18, 23], "sigma_0": 7, "sigma_1": 7, "sigma_2": 7, "sigma_fn": [9, 14], "sigma_i": [2, 7, 23], "sigma_j": 7, "sigma_m": [8, 20], "sigma_n": [13, 20], "sigma_t": 15, "sigma_x": 20, "sigmoid": [3, 4, 6, 9, 10, 12, 14], "sigmundson": 8, "sign": [3, 4, 9, 10, 12, 20, 21], "signal": [3, 5, 12, 14], "signifi": 6, "signific": 3, "significantli": [3, 15, 20], "sim": [6, 7, 8, 15, 20], "similar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 23], "similarli": [2, 3, 5, 7, 10, 12, 15, 20, 23], "simpl": [1, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20], "simplepredict": 12, "simpler": [1, 2, 3, 7, 8, 9, 15, 17, 23], "simplernn": 6, "simplest": [2, 3, 5, 6, 11, 12, 14, 16, 23], "simpletre": 12, "simpli": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 23], "simplic": [4, 7, 8, 9, 10, 11, 12, 13, 14, 16], "simplicti": 7, "simplifi": [2, 8, 11, 17, 23], "simplist": [5, 8, 20], "simul": 8, "simultan": 8, "sin": [2, 3, 4, 5, 6, 11, 14, 15, 18, 23], "sinc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 22, 23], "sine": [5, 14], "singl": [2, 3, 4, 5, 7, 8, 9, 10, 11, 14, 15, 18, 20, 23], "singular": [2, 8, 15, 18, 23], "sinusoid": 5, "site": [2, 19], "situat": [2, 6, 7, 9, 15, 20, 23], "six": [5, 20], "size": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 18, 20, 23], "sketch": 12, "ski": 11, "skill": 2, "skip": 13, "skl": [2, 8, 23], "sklearn": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 23], "skplt": [9, 12], "sl": 8, "slack": 10, "slice": [4, 18, 23], "slide": [1, 2, 5, 20, 23], "slight": [8, 15], "slightli": [3, 4, 5, 7, 8, 9, 12, 20], "slope": [10, 13, 14], "slow": [2, 4, 10, 15], "slower": [7, 18, 23], "slowest": 18, "slowli": 14, "slp": 3, "small": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "smaller": [2, 3, 4, 7, 8, 10, 11, 13, 15, 20, 23], "smallest": [2, 6, 16, 23], "smallest_row_index": 16, "smooth": [2, 5, 8, 15, 23], "sn": [2, 3, 5, 8, 9, 23], "sne": 13, "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "soar": 8, "social": 2, "soft": [3, 9, 12, 14], "soften": 10, "softmax": [5, 9], "softwar": [2, 10, 17, 18], "sol": 10, "sole": [2, 8, 23], "solid": [2, 9], "solut": [2, 3, 4, 5, 7, 8, 10, 12, 13, 15, 18, 20, 23], "soluton": 4, "solv": [1, 2, 3, 5, 7, 8, 10, 12, 13, 14, 15, 18, 23], "solve_expdec": 4, "solve_ode_deep_neural_network": 4, "solve_ode_neural_network": 4, "solve_pde_deep_neural_network": 4, "solveod": 4, "solveode_popul": 4, "solver": [4, 9, 10, 11, 12, 18, 23], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "some_model": 8, "somehow": 6, "someon": 1, "someth": [0, 2, 3, 5, 6, 9, 11, 13, 20, 23], "sometim": [2, 3, 13, 14, 15, 16], "soon": [18, 21], "sophist": [2, 23], "sopt": 15, "sort": [7, 8, 11, 13, 20], "sound": [5, 7], "sourc": [2, 3, 5, 8, 17, 18, 20, 23], "space": [2, 3, 6, 7, 10, 11, 13, 14, 15, 16, 20], "span": [2, 5, 7, 11, 13, 18, 23], "spare": 3, "spars": [5, 8, 18, 23], "sparse_mtx": [18, 23], "sparsecategoricalcrossentropi": 5, "sparsiti": 12, "spatial": [3, 4, 5, 14], "speak": 20, "special": [8, 9, 12, 14, 15, 18, 20, 23], "specif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20, 22, 23], "specifi": [2, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "specifici": [2, 12, 23], "spectacular": 5, "spectral": 3, "speech": [2, 3, 5, 6, 14], "speed": [3, 4, 6, 15], "spend": [1, 20], "sphere": 2, "spin": 8, "spite": 2, "spline": 10, "split": [1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 20, 23], "splite": 2, "splitter": [3, 12], "spontan": 20, "spot": 5, "spread": [2, 13, 20, 23], "springer": [22, 23], "spuriou": 15, "sqrsignal": 5, "sqrt": [2, 5, 6, 7, 8, 10, 12, 13, 15, 20], "squar": [0, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 17, 18, 20], "squarederror": 12, "squaredeuclidean": 16, "squash": 14, "srtm": 8, "srtm_data_norway_1": 8, "stabil": 7, "stabl": [1, 2, 6, 7, 8, 11, 17, 23], "stack": [5, 6], "stage": [0, 7, 15], "stai": [2, 4, 6, 7, 13, 23], "stand": [2, 7, 11, 14, 23], "standard": [2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 20, 23], "standardscal": [2, 8, 9, 10, 11, 12, 13], "stanford": 15, "start": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "start_tim": 16, "stat": 8, "state": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "statement": [2, 9, 18, 23], "statist": [2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 16, 18, 22], "statu": [0, 2, 9, 23], "stavang": 8, "std": [2, 6, 8, 23], "steep": 15, "step": [0, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 16, 18, 23], "step_fn": [9, 14], "step_length": 15, "steps_list": 11, "stereo": 5, "still": [2, 4, 5, 7, 8, 13, 15, 20], "stimuli": 14, "stk": [22, 23], "stk2100": [22, 23], "stk3155": [0, 19, 21], "stk4021": [22, 23], "stk4051": [22, 23], "stk4155": [19, 21], "stk5000": 22, "stochast": [2, 3, 7, 8, 10, 13, 14], "stock": 6, "stoke": 14, "stone": [2, 9], "stop": [3, 6, 11, 15, 16], "storag": 7, "store": [2, 3, 4, 5, 8, 13, 15, 20, 23], "storehaug": [21, 23], "str": [3, 5, 6], "straight": [2, 8, 10, 15, 23], "straightforward": [2, 4, 5, 7, 8, 10, 11, 12, 15, 18, 23], "strategi": [2, 3, 11, 23], "stratifi": 8, "strength": [2, 7, 16], "stretch": 13, "strict": [10, 15], "strictli": [10, 15], "stride": [6, 18], "strike": 8, "string": 3, "stroke": 9, "strong": [5, 8, 11, 12, 14, 18, 20], "strongli": [0, 2, 10, 17, 18], "stronli": 2, "structur": [2, 3, 4, 5, 8, 11, 12, 14, 17, 23], "stuck": [3, 15], "student": [0, 2, 19, 21, 22, 23], "studi": [2, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 22, 23], "studier": 22, "style": [9, 11, 18, 23], "st\u00f8land": 21, "sub": [11, 14], "subdivid": [2, 18, 23], "subfield": 2, "subject": [8, 10, 20], "submit": 23, "subplot": [2, 3, 5, 6, 8, 9, 10, 11, 12, 16, 23], "subplots_adjust": [10, 20], "subprogram": [18, 23], "subract": 2, "subroutin": [2, 23], "subscript": 3, "subsequ": [3, 6, 7, 8, 14, 18, 20], "subset": [3, 8, 11, 14, 15, 17, 23], "subspac": [2, 10, 13], "substanti": [11, 12], "substep": 13, "substitut": [1, 5, 8, 14, 18], "subsubset": 11, "subtask": 8, "subtl": 3, "subtract": [2, 6, 7, 8, 13, 15, 18, 20], "subtre": 11, "succeed": [2, 6, 23], "success": [5, 9, 11, 15, 20], "successfulli": [6, 11], "sudo": [2, 17, 23], "suffer": [2, 3, 4, 7, 12, 23], "suffici": [3, 8, 10, 13, 15], "suggest": [3, 15, 22], "suit": [10, 14], "suitabl": [0, 2, 20], "sum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_i": [2, 4, 7, 8, 10, 15], "sum_j": 8, "sum_ja_": 2, "sum_k": [8, 10, 14, 18], "sum_logist": 15, "sum_m": 5, "sum_n": 5, "sum_nx_": 5, "summar": [7, 8, 11], "summari": [3, 5, 6, 12, 19], "summat": [1, 2, 5], "sunni": 11, "super": 7, "superfici": 5, "superscript": [3, 14], "supervis": [2, 7, 8, 9, 11, 14, 17, 23], "supplement": 9, "support": [2, 3, 11, 12, 13, 15, 17, 23], "suppos": [2, 7, 8, 9, 10, 12, 13, 14, 15, 18, 23], "suppress": [7, 15], "sure": [1, 2, 3, 6, 8], "surf": 8, "surfac": [2, 8, 23], "surpass": 8, "surpris": [2, 23], "surround": [5, 17], "survei": [2, 7, 8, 23], "svc": [10, 11, 12], "svd": [2, 8, 13, 23], "svdinv": 7, "svm": [10, 11, 12, 13], "svm_clf": [10, 12], "swath": 7, "switch": 2, "sy": 15, "symbol": [3, 7, 13, 15, 17, 20, 23], "symmeteri": 3, "symmetr": [2, 7, 10, 13, 14, 15, 18, 23], "symmetri": 8, "sympi": [2, 17, 23], "synonim": 20, "syntax": 15, "system": [0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 23], "systemat": [6, 8], "t": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "t0": [5, 8, 15], "t1": [4, 15], "t2": 4, "t3": 4, "t_": 4, "t_0": [4, 11, 15], "t_1": 15, "t_b": 12, "t_i": [3, 4, 7, 14], "t_j": 14, "t_k": 11, "tabl": [11, 20, 21, 23], "tabul": [2, 23], "tabular": 23, "tackl": 6, "tag": [4, 5, 6, 7, 8, 9, 14, 15, 16, 18, 20], "taht": [2, 23], "tail": 20, "tailor": [4, 10, 13, 23], "taiwan": [2, 23], "take": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "taken": [2, 3, 5, 8, 12, 15, 18], "tan": 5, "tangent": [3, 6, 14, 15], "tanh": [3, 6, 9, 10, 14], "target": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 23], "target_nam": 11, "task": [2, 3, 5, 8, 11, 13, 14, 16, 23], "tau": [5, 7, 20], "taught": 23, "tax": 2, "taylor": [4, 15], "taylornr": 15, "tc": 10, "teach": [0, 19, 23], "team": 3, "teaser": 2, "technic": [2, 7, 8, 15], "techniqu": [2, 3, 10, 12, 15, 17, 20, 22, 23], "technologi": [2, 3], "tell": [1, 2, 6, 8, 12, 13, 15, 20], "temp": 3, "temp1": 3, "temp2": 3, "temperatur": [2, 11, 23], "temporarili": 3, "ten": [5, 23], "tend": [5, 7, 8, 10, 11, 12, 14, 15, 16], "tendenc": [2, 23], "tension": 8, "tensor": 5, "tensorflow": [2, 4, 6, 10, 16, 17, 18, 22, 23], "term": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "term1": [7, 8, 13], "term2": [7, 8, 13], "term3": [7, 8, 13], "term4": [7, 8, 13], "termin": [0, 2, 6, 7, 11, 12, 15], "terrain": 8, "terrain1": 8, "test": [1, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 20, 23], "test_acc": 5, "test_accuraci": [3, 5], "test_error": 8, "test_imag": [5, 6], "test_ind": 8, "test_input": 6, "test_label": [5, 6], "test_loss": 5, "test_pr": 3, "test_predict": 3, "test_rnn": 6, "test_scor": [9, 12], "test_siz": [0, 2, 3, 5, 7, 8, 12], "test_split": 11, "testerror": [2, 8], "testi": 6, "testpredict": 6, "testx": 6, "text": [2, 3, 4, 6, 7, 10, 11, 13, 15, 18, 20, 22], "textbook": 1, "textual": 11, "textur": 3, "tf": [3, 5, 6, 15, 16], "th": [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 20, 23], "than": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20, 23], "thank": [6, 8], "theano": [3, 17, 23], "thei": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "them": [2, 3, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18, 23], "theme": [0, 2, 23], "themselv": [2, 20, 23], "thenc": 8, "theorem": [4, 8, 9], "theoret": [2, 6, 12], "theori": [2, 3, 5, 10, 11, 14, 15, 17, 22, 23], "thereaft": [2, 7, 8, 13, 14, 18, 23], "therebi": [2, 7, 9, 13, 23], "therefor": [2, 3, 4, 5, 6, 8, 9, 10, 13, 15, 20, 23], "therein": 13, "thereof": [2, 8, 15, 23], "theta": [1, 2, 3, 6, 7, 8, 9, 15, 20, 23], "theta_": [2, 3, 8, 9, 15, 23], "theta_0": [1, 2, 7, 8, 9, 23], "theta_0x_": [2, 23], "theta_1": [2, 7, 8, 9, 23], "theta_1x_": [2, 23], "theta_1x_0": [2, 23], "theta_1x_1": [2, 9, 23], "theta_1x_2": [2, 23], "theta_1x_i": 9, "theta_2": [2, 23], "theta_2x_": [2, 23], "theta_2x_0": [2, 23], "theta_2x_1": [2, 23], "theta_2x_2": [2, 9, 23], "theta_i": [2, 3, 7, 23], "theta_j": [2, 7, 8, 23], "theta_linreg": 15, "theta_p": 9, "theta_px_p": 9, "theta_t": 15, "thetavalu": 7, "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22], "thing": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 23], "think": [2, 3, 5, 6, 8, 11, 14, 15, 16, 20, 23], "third": [2, 5, 8, 15, 21, 23], "thirti": 9, "thorughout": 23, "those": [2, 5, 7, 8, 10, 11, 12, 13, 18, 23], "though": [1, 3, 4, 5, 6, 15, 18, 20], "thought": [8, 16, 20], "thousand": [2, 3], "three": [2, 3, 5, 7, 8, 10, 11, 14, 18, 19, 20, 21, 23], "threshold": [3, 5, 11, 12, 13, 14, 15], "through": [0, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 20, 23], "throughout": [0, 2, 6, 7, 16, 17, 18, 20, 23], "throw": [5, 8, 20], "thu": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 21, 23], "thumb": [2, 8], "tibshirani": [8, 22, 23], "tick_param": 8, "ticker": [8, 15, 20], "tif": 8, "tight_layout": [3, 9], "tightli": 13, "tild": [2, 7, 8, 9, 13, 20, 23], "till": [2, 6, 9, 10, 11, 12, 14, 18, 23], "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "timeit": 6, "timer": 6, "tini": 3, "tip": 5, "titl": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 20, 23], "tmp": 15, "tn": [4, 5, 9], "to_categor": [3, 5, 6], "to_categorical_numpi": 3, "to_numer": [2, 8, 23], "todai": 5, "togeth": [2, 5, 8, 10, 13, 15, 17, 23], "toi": 16, "told": 15, "toler": [4, 16], "tolist": 6, "tomographi": 14, "too": [2, 4, 6, 7, 8, 11, 13, 15, 20, 22], "took": [10, 23], "tool": [0, 2, 3, 5, 8, 15, 17], "toolbox": 10, "top": [2, 5, 7, 8, 11, 12, 17, 23], "topic": [2, 7, 8, 9, 10, 17], "topolog": [5, 14], "topologi": [3, 14], "torkjellsdatt": [21, 23], "toss": [12, 20], "total": [2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 21, 23], "total_loss": 6, "totalclustervari": 16, "totalscatt": 16, "toward": [0, 3, 4, 9, 14, 15], "town": 2, "tp": [6, 9], "tpng": 11, "tpu": [15, 17, 23], "tqdm": 8, "track": [0, 5, 15, 16, 18], "tract": 2, "tractabl": [2, 23], "trade": [7, 11], "tradeoff": [2, 7, 23], "tradit": [2, 3, 6, 8, 23], "train": [1, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15], "train_accuraci": [2, 3, 5, 23], "train_dataset": 6, "train_end": [2, 3], "train_error": 8, "train_imag": [5, 6], "train_ind": 8, "train_label": [5, 6], "train_pr": 3, "train_siz": [2, 3, 5], "train_step": 6, "train_test_split": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "train_test_split_numpi": [2, 3], "trainable_vari": 6, "trained_model": 8, "trainerror": 2, "traini": 6, "training_checkpoint": 6, "training_dataset": 6, "training_gradi": 15, "trainingerror": 8, "trainpredict": 6, "trainscor": 6, "trainx": 6, "trait": [2, 23], "trajectori": 6, "transfer": [11, 23], "transform": [2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "transit": [8, 14], "translat": [3, 6, 8, 12, 23], "transpos": [3, 7, 13, 18], "travers": [2, 7], "treat": [2, 3, 5, 8, 14, 15, 20, 23], "tree": [2, 3, 17, 23], "tree_clf": [11, 12], "tree_clf_": 11, "tree_clf_sr": 11, "tree_reg": 11, "tree_reg1": 11, "tree_reg2": 11, "trend": 20, "treue": 9, "trevor": 22, "tri": [1, 4, 5, 6, 11, 15], "triain": 2, "trial": [2, 4, 6, 8, 15, 20, 23], "triangl": 15, "triangular": 18, "trick": [5, 6, 10, 13, 15, 20], "trickier": 20, "tridiagon": 18, "trillion": 17, "trivial": [2, 3, 7, 13, 20, 23], "troubl": [0, 2, 10, 14], "truck": 5, "true": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "true_fun": 8, "true_theta": 8, "truli": 23, "try": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23], "tucker": 10, "tuesdai": [21, 23], "tumor": [9, 11], "tumour": 9, "tunabl": 3, "tune": [6, 11, 15, 18, 23], "turn": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "tutori": [3, 6], "tv": 4, "tveito": 4, "tweak": [3, 6, 12, 20], "twice": 15, "twist": 13, "two": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23], "tx": 15, "tx_1": 15, "txt": [0, 6], "ty": 15, "type": [2, 3, 5, 8, 10, 12, 15, 18, 20], "typic": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 20, 23], "u": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "u_": 18, "u_i": 14, "u_m": 12, "ua": [2, 23], "ubuntu": [2, 17, 23], "uci": 2, "uio": [0, 21, 22], "un": 16, "unabl": 0, "unari": [18, 23], "unbalanc": [8, 11], "unbias": [2, 7, 8, 23], "uncent": 8, "uncertainti": [2, 7, 23], "uncertitud": 20, "unchang": [3, 5], "uncorrel": [12, 20], "undefin": 7, "under": [2, 3, 7, 8, 12, 15, 17, 23], "underdetermin": [2, 23], "underfit": [3, 8], "underflowproblem": 7, "undergo": 7, "undergradu": [19, 21], "underli": [2, 3, 11, 15, 20, 23], "underset": [6, 16], "understand": [0, 2, 3, 5, 7, 8, 12, 15, 16, 17, 23], "understood": [10, 15], "undesir": 10, "undetermin": [7, 10], "undo": 6, "unexpect": 8, "unexpected": 20, "unfair": 8, "unfortun": [3, 10, 11, 12], "unicode_liter": [10, 11], "uniform": [2, 3, 7, 8, 13, 15, 20, 23], "uniformli": [15, 20], "unifrompdf": 20, "unimport": 15, "union": [7, 8], "uniqu": [2, 4, 8, 15, 16, 18, 23], "unique_cluster_label": 16, "unit": [2, 3, 5, 6, 7, 12, 14, 20, 23], "unitari": [7, 8, 18], "unitarili": [18, 23], "uniti": 20, "univari": 20, "univers": [2, 3, 4, 15, 17, 19, 21, 23], "unix": 3, "unknow": [2, 18, 23], "unknown": [2, 3, 5, 6, 7, 8, 10, 12, 15, 18, 23], "unknowwn": 14, "unlabel": 3, "unless": [2, 5, 8, 13, 15, 23], "unlik": [3, 5, 10, 15], "unnecessarili": 11, "unord": 5, "unravel": 3, "unrol": [5, 13], "unseen": [0, 2, 9, 11], "unstabl": 3, "unsupervis": [2, 3, 6, 14, 17, 23], "unsymmetr": [18, 23], "until": [3, 4, 6, 11, 14, 15, 16], "untouch": 2, "unusu": 14, "up": [1, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21], "updat": [0, 3, 4, 12, 14, 15, 16], "uploa": 23, "upload": [0, 17, 22], "upon": [2, 3, 8, 9, 13, 18], "upper": [1, 2, 10, 11, 18], "uppercas": [18, 23], "upsampl": 6, "upscal": 6, "url": 23, "us": [0, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 22], "usag": [2, 10, 17, 23], "usd": 2, "usd10000": 2, "use_bia": 6, "usecol": [2, 23], "useless": 3, "user": [2, 3, 4, 6, 8, 9, 17, 18, 23], "usernam": 0, "usetex": 20, "usg": 8, "usr": 20, "usual": [2, 5, 6, 9, 14, 15, 16, 23], "ut": 7, "util": [3, 5, 6, 8, 9, 12, 16, 23], "ux": 18, "v": [0, 2, 4, 6, 7, 8, 13, 15, 17], "v0": 20, "v1": 20, "v2": 20, "v_0": 13, "va": 3, "vahid": 23, "val": 15, "val_accuraci": 5, "val_loss": 6, "vale": 4, "valid": [2, 3, 6, 9, 11, 12, 15, 17, 20, 23], "validation_data": 5, "validation_split": 6, "valu": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23], "valuat": 11, "valy": 6, "van": [2, 23], "vandenbergh": [10, 15], "vandermond": [2, 23], "vanilla": [2, 8, 13, 16], "vanish": [3, 6, 15, 20], "var": [7, 8, 12, 13, 20], "var_x": 20, "varabl": 10, "varepsilon": [7, 8], "varepsilon_": [7, 8], "varepsilon_i": [7, 8], "vari": [2, 3, 5, 7, 8, 12, 23], "variabl": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 23], "varianc": [2, 3, 7, 9, 11, 12, 13, 15, 16, 17, 18, 20, 23], "variance_i": [7, 13], "variance_x": [7, 13], "variant": [2, 3, 8, 10, 14, 15, 23], "variat": [5, 6, 13, 23], "varieti": [2, 5, 14, 17, 23], "variou": [1, 3, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "varydimens": 6, "vastli": 5, "vaue": 3, "vault": 2, "vdot": [4, 15], "vec": 8, "vector": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17], "vector_mean": 16, "ventur": [2, 10, 17, 23], "venv": 0, "verbos": [3, 5, 6], "veri": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 22, 23], "verifi": [5, 13, 18, 23], "versatil": [10, 23], "versicolor": [10, 11], "version": [0, 2, 5, 12, 15, 16, 17, 18, 20, 23], "versu": 3, "vert": [1, 2, 3, 7, 8, 9, 10, 11, 13, 15, 23], "vert_1": [7, 8], "vert_2": [7, 8, 13], "via": [2, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 23], "vidal": 13, "video": [2, 3, 14, 17, 19, 21, 23], "view": [3, 5, 7, 8, 14, 15, 20, 22, 23], "violat": 10, "virginica": 11, "viridi": [2, 3, 4, 5, 23], "virtual": 3, "viscos": 15, "viscou": 15, "visibl": 0, "vision": [2, 5], "visual": [2, 5, 13, 14, 17, 23], "visualis": 3, "visualstudio": [0, 1], "viz": [8, 10, 20], "vmap": 15, "vmax": [3, 8], "vmin": [3, 8], "voic": 5, "volum": [2, 5, 23], "vote": [12, 23], "voting_clf": 12, "votingclassifi": 12, "votingsimpl": 12, "vstack": [7, 13, 18, 20, 23], "vt": 7, "w": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "w1": 10, "w2": [10, 13], "w3": 10, "w_": [3, 14], "w_1": [10, 18], "w_1x_": 10, "w_1x_1": 10, "w_2": [10, 18], "w_2x_": 10, "w_2x_2": 10, "w_3": 18, "w_4": 18, "w_hidden": 4, "w_i": [3, 4, 12], "w_ix_i": 14, "w_j": 18, "w_m": 18, "w_output": 4, "w_px_": 10, "w_px_p": 10, "wa": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 16, 18, 23], "wai": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "walk": 11, "walker": 20, "wang": [2, 23], "want": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "warn": 6, "warrant": 8, "wast": 5, "watch": 17, "wave": 5, "wavelet": 10, "we": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "weak": [11, 12, 16], "weather": [3, 14], "web": [17, 19, 21, 23], "webpag": 23, "websit": [8, 18, 19, 23], "wedg": [10, 20], "wednesdai": [21, 23], "wee": 13, "week": [2, 7, 8, 9, 19, 21], "weekli": [0, 1, 17, 19, 21, 22, 23], "weight": [2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 20], "weigth": 4, "welcom": [0, 10, 17], "well": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 22, 23], "went": 10, "were": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 20, 23], "wessel": [2, 23], "what": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "whatev": 5, "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "whenev": [0, 15, 20], "where": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "wherea": [8, 20], "wherein": [3, 14], "whether": [2, 5, 7, 9, 11, 20, 23], "which": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "whichev": [3, 5], "while": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20, 23], "white": 11, "who": [0, 2], "whole": [3, 5, 6, 7, 11, 13, 15], "whose": [2, 8, 12, 20], "whow": 13, "why": [0, 1, 2, 3, 5, 8, 15], "wide": [2, 3, 5, 8, 9, 14, 17, 18, 23], "widehat": 8, "width": [2, 5, 10, 11, 23], "wieringen": [2, 23], "win": 12, "wind": 11, "wing": [21, 23], "winther": 4, "wiothout": 8, "wiscons": 9, "wisconsin": 12, "wisdom": 8, "wise": [2, 3, 7, 14, 15], "wish": [2, 4, 7, 9, 10, 13, 15, 16, 18, 23], "with_std": 2, "wither": 8, "within": [2, 4, 5, 6, 9, 11, 14, 15, 16, 20, 22, 23], "withinclust": 16, "without": [0, 2, 3, 7, 8, 10, 11, 13, 14, 15, 23], "won": [0, 2, 23], "wonder": 10, "word": [2, 3, 5, 6, 7, 8, 9, 16, 20, 23], "work": [0, 1, 2, 3, 6, 8, 9, 10, 11, 15, 17, 19, 20, 21, 23], "workshop": 23, "world": [1, 2, 10], "worldwid": [2, 23], "worri": 0, "wors": [2, 3, 5, 6, 8, 23], "worth": 11, "would": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "wrap": [8, 18, 23], "write": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 18, 23], "written": [1, 2, 4, 5, 7, 13, 14, 15, 17, 18, 20, 23], "wrong": [0, 3, 10], "wrongli": 12, "wrote": [7, 13], "wrt": [12, 15], "wth": [12, 15], "www": [17, 18, 22, 23], "wx_1": 10, "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x0": 10, "x1": [6, 10, 11, 12, 15], "x1_exampl": 10, "x1d": 10, "x2": [10, 11, 12, 15], "x2d": [10, 13], "x2d_train": 13, "x2dsl": 13, "x3": 10, "x_": [2, 4, 5, 7, 8, 10, 12, 13, 15, 16, 18, 20, 23], "x_0": [2, 7, 13, 18, 23], "x_1": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_2": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_3": [10, 18, 20], "x_4": 18, "x_center": 13, "x_data": 3, "x_data_ful": 3, "x_hidden": 4, "x_i": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x_input": 4, "x_ix_": [2, 23], "x_iy_i": 10, "x_j": [1, 2, 4, 10, 11, 14, 20], "x_jy_j": 10, "x_k": [14, 16, 18, 20], "x_l": 20, "x_m": [8, 14, 18, 20], "x_n": [2, 4, 5, 8, 10, 13, 14, 15, 18, 20, 23], "x_new": [11, 12], "x_offset": 8, "x_output": 4, "x_p": [5, 9, 11], "x_poli": 11, "x_poly10": 11, "x_pred": 6, "x_prev": 4, "x_reduc": 13, "x_scale": 10, "x_small": 15, "x_test": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13], "x_test_own": 8, "x_test_scal": [2, 8, 9, 11, 12, 13], "x_tot": 6, "x_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "x_train_mean": 8, "x_train_own": 8, "x_train_scal": [2, 8, 9, 11, 12, 13], "x_val": 3, "xarrai": [17, 23], "xavier": 3, "xbnew": 15, "xcode": [2, 17, 23], "xdclassiffierconfus": 12, "xdclassiffierroc": 12, "xg_clf": 12, "xgb": 12, "xgbclassifi": 12, "xgboost": 11, "xgboot": 12, "xgbregressor": 12, "xgparam": 12, "xgtree": 12, "xi": [10, 15], "xi_": 10, "xi_1": 10, "xi_i": 10, "xk": 10, "xla": [15, 17, 23], "xlabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "xlim": [8, 12], "xm": 11, "xmesh": 15, "xnew": [2, 15, 23], "xp": 20, "xpanda": 2, "xpd": [7, 13], "xplot": 2, "xscale": 2, "xsr": 11, "xt_x": 15, "xtest": 8, "xtick": [5, 8, 10, 11], "xtrain": 8, "xu": [2, 23], "xx": [2, 18, 23], "xy": [2, 8, 10, 18, 23], "xytext": 10, "xz": [18, 23], "y": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "y1": 6, "y2": 6, "y3": 6, "y_": [2, 3, 7, 8, 12, 13, 18, 23], "y_0": [2, 7, 13, 18, 23], "y_1": [2, 7, 10, 11, 13, 15, 18, 23], "y_1y_1": 10, "y_1y_1k": 10, "y_1y_2": 10, "y_1y_2k": 10, "y_1y_n": 10, "y_1y_nk": 10, "y_2": [2, 7, 10, 11, 13, 18, 23], "y_2y_1": 10, "y_2y_1k": 10, "y_2y_2": 10, "y_2y_2k": 10, "y_3": [2, 11, 18], "y_4": 18, "y_data": [2, 3, 7, 8, 23], "y_data_ful": 3, "y_decis": 10, "y_fit": 2, "y_i": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "y_if_": 12, "y_ix_": [2, 23], "y_ix_i": [9, 10, 15], "y_iy_jk": 10, "y_j": [8, 10, 14], "y_k": 14, "y_m": 18, "y_model": [2, 6, 7, 8, 23], "y_n": [10, 15], "y_ny_1": 10, "y_ny_1k": 10, "y_ny_2": 10, "y_ny_2k": 10, "y_ny_n": 10, "y_ny_nk": 10, "y_offset": 8, "y_plot": 11, "y_pred": [2, 3, 6, 8, 9, 10, 11, 12], "y_pred1": 11, "y_pred2": 11, "y_pred_rf": 12, "y_pred_tre": 12, "y_proba": [9, 12], "y_scaler": 8, "y_test": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13], "y_test_onehot": 3, "y_test_predict": 2, "y_tot": 6, "y_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_train_mean": 8, "y_train_onehot": 3, "y_train_predict": 2, "y_train_scal": 8, "y_val": 3, "ye": [5, 8, 9], "year": [2, 17, 23], "yet": [2, 3, 8, 10, 13, 15, 23], "yi": 15, "yield": [2, 4, 7, 8, 10, 12, 14, 15, 16, 18, 20, 23], "yk": 10, "ylabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "ylim": [5, 8], "ym": 11, "ymesh": 15, "yn": 2, "yo": [10, 11, 12], "yoshua": [3, 22], "you": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 21, 22, 23], "young": 2, "your": [0, 3, 4, 6, 7, 8, 10, 13, 15, 17, 18, 23], "your_model_object": 1, "yourself": [13, 15, 23], "youtub": 17, "ypred": 8, "ypredict": [2, 15, 23], "ypredict2": 15, "ypredictlasso": 7, "ypredictol": [2, 7], "ypredictown": 8, "ypredictownridg": 8, "ypredictridg": [2, 7, 8], "ypredictskl": 8, "ytest": 8, "ytick": [5, 8, 10, 11], "ytild": [2, 8, 23], "ytildelasso": 7, "ytildenp": [2, 23], "ytildeol": [2, 7], "ytildeownridg": 8, "ytilderidg": [7, 8], "ytrain": 8, "yuxi": 23, "yx": [18, 23], "yy": [18, 23], "yz": [18, 23], "z": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "z_": [3, 4, 14, 18, 23], "z_0": [18, 23], "z_1": [18, 23], "z_2": [18, 23], "z_c": 3, "z_h": 3, "z_hidden": 4, "z_i": [3, 14], "z_j": [3, 14], "z_k": 14, "z_m": 3, "z_mod": 11, "z_o": 3, "z_output": 4, "zaman": 20, "zaxi": 8, "zero": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "zeros_lik": 6, "zfill": 6, "zip": [6, 8], "zm_h": [2, 23], "zn": 2, "zone": 2, "zoom": 23, "zx": [18, 23], "zy": [18, 23], "zz": [18, 23], "\u00f8yvind": 8}, "titles": ["Exercises week 34", "Exercises week 35", "<span class=\"section-number\">3. </span>Linear Regression", "<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network", "<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning", "<span class=\"section-number\">16. </span>Convolutional Neural Networks", "<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view", "<span class=\"section-number\">4. </span>Ridge and Lasso Regression", "<span class=\"section-number\">5. </span>Resampling Methods", "<span class=\"section-number\">6. </span>Logistic Regression", "<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims", "<span class=\"section-number\">9. </span>Decision trees, overarching aims", "<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)", "<span class=\"section-number\">13. </span>Neural networks", "<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm", "<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning", "Applied Data Analysis and Machine Learning", "<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features", "Course setting", "<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities"], "titleterms": {"": [10, 12], "1": [0, 1, 2], "2": [0, 1, 2, 23], "2023": 21, "3": [0, 1, 2], "34": [0, 23], "35": 1, "4": [0, 1, 2], "5": [1, 2], "A": [2, 3, 6, 10, 11, 23], "And": 23, "In": 21, "Ising": 8, "The": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 17, 23], "To": 23, "With": 6, "about": 23, "activ": [3, 14], "ad": [2, 8, 23], "adaboost": 12, "adagrad": 15, "adam": 15, "adapt": 12, "adjust": 3, "adversari": 6, "again": [5, 11], "ai": 23, "aim": [10, 11, 23], "aka": 23, "algebra": [18, 23], "algorithm": [11, 12, 13, 14, 23], "algortithm": 15, "all": 10, "an": [0, 2, 6, 12, 23], "analys": 7, "analysi": [2, 7, 8, 13, 17, 20, 23], "analyt": [1, 2], "ani": 15, "anoth": 11, "appli": 17, "approach": [2, 10, 16, 23], "approxim": 14, "architectur": 3, "arrai": [18, 23], "assist": 21, "autocorrel": 20, "autograd": [4, 15], "automat": 15, "back": [3, 13, 14], "background": 17, "bag": 12, "base": 15, "basic": [2, 7, 9, 11, 12, 13, 18], "batch": 3, "bay": 7, "befor": 13, "better": 10, "bia": 8, "binari": 3, "bind": 23, "bird": 12, "boost": 12, "bootstrap": [8, 12], "boston": 2, "breast": 3, "brief": 23, "bring": 14, "build": [3, 5, 11], "c": 23, "can": 23, "cancer": [3, 9, 11, 13], "cart": 11, "case": [10, 12, 20], "central": [15, 17, 20], "chain": 14, "chang": 12, "channel": 23, "chi": [2, 23], "choos": 3, "cifar01": 5, "classic": 13, "classif": [3, 11, 12], "classifi": 10, "clip": 3, "cluster": 16, "cnn": 5, "code": [0, 1, 2, 3, 4, 7, 11, 13, 14, 15, 16, 23], "collect": [3, 5], "commun": 23, "compar": [1, 4, 12], "complex": [2, 8], "complic": 8, "compon": 13, "comput": 11, "computerlab": 23, "con": 11, "concept": 20, "conjug": 15, "contn": 23, "convex": [10, 15], "convolut": [5, 14], "correl": 13, "cost": [3, 12], "cours": [17, 19, 22, 23], "covari": [7, 13, 20], "cover": 23, "creat": 1, "cross": 8, "cython": 23, "data": [0, 2, 3, 5, 8, 9, 11, 13, 17, 20, 23], "dataset": [3, 5], "david": 23, "deadlin": 23, "deadllin": 21, "decai": 4, "decis": [11, 12], "decomposit": [7, 13, 18], "deeep": [], "deep": [3, 4, 23], "defin": [3, 23], "degre": 2, "deliver": [0, 1], "dens": 2, "deriv": [1, 7, 14], "descent": [4, 12, 15], "detail": [5, 23], "develop": 3, "diagon": 13, "differ": 10, "differenti": [4, 15], "diffus": 4, "dimension": [4, 5, 10], "disadvantag": 11, "discret": 20, "discrimin": 23, "distribut": [7, 20], "do": 3, "domain": 20, "down": 3, "dropout": 3, "element": [2, 20, 23], "elimin": 18, "energi": 23, "ensembl": 12, "entropi": 11, "environ": [0, 2], "equat": [2, 4, 14], "error": [2, 12, 23], "essenti": 23, "etc": 23, "euler": 4, "evalu": 3, "exampl": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "exercis": [0, 1, 2, 8], "expect": 20, "experi": 20, "explor": 2, "exponenti": 4, "express": 1, "extrapol": 6, "extrem": [12, 23], "ey": 12, "fall": 21, "famili": [3, 23], "famou": 18, "featur": [1, 11, 18], "feed": [3, 14], "final": 14, "find": 1, "fine": 3, "first": [6, 14, 23], "fit": [0, 1, 2, 12, 23], "forc": 5, "forest": 12, "format": 23, "forward": [3, 4, 14], "foster": 23, "fourier": 5, "frank": 8, "freedom": 2, "frequentist": [2, 23], "from": [7, 12, 14, 23], "full": 4, "function": [2, 3, 8, 9, 10, 12, 13, 14, 15, 20, 23], "further": [5, 7], "gan": 6, "gaussian": 18, "gd": 15, "gener": [6, 11, 23], "geometr": 13, "gini": 11, "github": 0, "goal": [0, 1], "good": [2, 23], "grade": [21, 23], "gradient": [3, 4, 12, 15], "growth": 4, "ha": 17, "handl": [18, 23], "hidden": 4, "hous": 2, "how": 1, "hyperparamet": 3, "hyperplan": 10, "i": [2, 3, 23], "id3": 11, "idea": 13, "ii": 23, "implement": [1, 3], "implic": 7, "import": [7, 18, 23], "improv": 3, "includ": 15, "increment": 13, "index": 11, "inform": 21, "input": 4, "instal": [17, 23], "instructor": 21, "interpret": [7, 13, 23], "introduc": [13, 15], "introduct": [2, 8, 17, 18, 23], "invers": [7, 18], "iter": 12, "jax": 15, "julia": 23, "jungl": 12, "kera": [3, 5], "kernel": [10, 13], "lagrangian": 10, "lasso": [7, 8], "later": 7, "layer": [3, 4, 5, 14], "learn": [0, 1, 2, 3, 4, 13, 15, 16, 17, 23], "least": [1, 7, 8, 23], "lectur": 23, "level": 12, "librari": [17, 23], "likelihood": 9, "limit": [3, 15, 20], "linear": [0, 2, 10, 15, 18, 23], "link": [7, 13, 22], "logist": [9, 23], "lu": 18, "machin": [2, 10, 15, 17, 23], "main": [20, 23], "make": [2, 11, 12], "mani": [12, 14], "mass": 23, "materi": 23, "math": 7, "mathemat": [5, 7, 10], "matric": [7, 18, 23], "matrix": [1, 3, 7, 13, 14, 18, 23], "matter": 2, "mean": 2, "meet": [7, 12, 20, 23], "mercer": 10, "method": [8, 11, 12, 15, 23], "minim": 23, "ml": 23, "mlp": 14, "mnist": [5, 6], "model": [0, 2, 3, 6, 8, 14, 23], "momentum": 15, "moon": [10, 11], "more": [5, 8, 18, 23], "multilay": 14, "multipl": [3, 5], "multipli": 10, "need": 23, "network": [3, 4, 5, 6, 9, 14, 23], "neural": [3, 4, 5, 6, 9, 14, 23], "new": 6, "non": 10, "normal": [2, 3], "notat": 14, "now": [3, 11, 15], "nuclear": [2, 23], "numba": 23, "number": [2, 4, 20], "numer": [4, 20], "numpi": [18, 23], "object": 5, "obtain": 13, "od": 4, "off": 8, "ol": [0, 1, 7, 8], "one": [4, 14], "oper": 18, "optim": [3, 10, 15, 17, 23], "order": 15, "ordinari": [1, 7, 8, 23], "organ": [2, 23], "oslo": 22, "other": [6, 11, 13, 14, 18, 23], "our": [2, 6, 7, 13, 15, 23], "outcom": [17, 23], "output": 4, "overarch": [2, 6, 10, 11, 23], "overview": [12, 23], "own": [2, 12, 13, 23], "packag": [18, 23], "panda": 23, "paramet": 23, "part": [15, 17], "partial": 4, "pass": 3, "pca": 13, "pdf": 20, "perceptron": 14, "perform": [3, 11], "period": 5, "perspect": 3, "plethora": 23, "point": 6, "poisson": 4, "polynomi": [1, 5], "popul": 4, "popular": 23, "practic": [15, 21, 23], "pre": [3, 5], "predict": 6, "prerequisit": [5, 17, 23], "princip": 13, "principl": 5, "pro": 11, "probabl": [7, 20], "problem": [3, 4, 15, 23], "procedur": [11, 23], "process": [3, 5], "program": [4, 15], "project": [8, 21, 23], "prop": 15, "propag": [3, 14], "properti": [7, 20], "python": [0, 2, 11, 17, 18, 23], "quick": 10, "r": 23, "random": [12, 13, 20], "read": [11, 23], "real": [8, 23], "recommend": 23, "recurr": [6, 14], "reduc": 2, "reduct": 5, "reformul": 4, "regress": [0, 2, 7, 8, 9, 11, 12, 15, 23], "regular": 3, "relev": 22, "relu": 3, "remark": 5, "remind": [8, 10, 23], "replac": 15, "repositori": 0, "requir": [4, 17], "resampl": 8, "rescal": 8, "resourc": 4, "revisit": 15, "rewrit": 23, "ridg": [2, 7, 8], "rm": 15, "rule": 14, "same": 15, "sampl": 13, "schedul": 23, "schemat": 11, "scheme": 4, "scienc": 23, "scikit": [2, 3, 13, 23], "second": 15, "semest": 21, "set": [0, 2, 4, 5, 11, 14, 19, 23], "setup": 0, "sgd": 15, "should": 3, "similar": 15, "simpl": [2, 6, 11, 15, 23], "singl": 12, "singular": [7, 13], "sklearn": 1, "soft": 10, "softmax": 3, "softwar": 23, "solv": 4, "solver": 15, "some": [15, 18], "specifi": 4, "split": [0, 2], "squar": [1, 2, 7, 8, 12, 23], "standard": 15, "state": 2, "statist": [7, 8, 17, 20, 23], "steepest": [12, 15], "stochast": [15, 20], "strongli": 23, "suggest": 23, "summari": [21, 23], "superposit": 5, "supervis": 3, "support": 10, "svd": 7, "systemat": 5, "take": 1, "taken": 23, "teach": 21, "teacher": [21, 23], "techniqu": [8, 13], "technologi": 17, "tensorflow": [3, 5], "tent": [21, 23], "test": [0, 2, 3], "text": 23, "textbook": [22, 23], "theorem": [7, 10, 13, 14, 20], "theori": 20, "thi": 23, "tip": 15, "togeth": 14, "tool": 23, "top": 3, "topic": 23, "toward": 13, "trade": 8, "tradeoff": 8, "train": [0, 2, 3, 6, 23], "transform": 5, "tree": [11, 12], "tune": 3, "two": [5, 10, 17], "type": [4, 6, 14, 23], "uio": 23, "univers": [14, 22], "unsupervis": 16, "up": [0, 2, 4, 11, 14, 23], "us": [1, 2, 3, 4, 5, 9, 15, 17, 23], "v": 5, "valid": 8, "valu": [7, 13, 20], "variabl": 20, "varianc": 8, "variou": 2, "vector": [1, 10, 14, 18, 23], "versu": 23, "view": [2, 6, 12], "virtual": 0, "visual": [3, 11], "wai": 11, "wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12]}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index 1364bb6bb..eb399b7e1 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -32,7 +32,7 @@
     <link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=eba8b062" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css?v=6644e6bb" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
   
@@ -402,7 +402,7 @@ document.write(`
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-is-generative-modeling">What Is Generative Modeling?</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster">Example of generative modeling, taken from Generative Deep Learning by David Foster</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generative-versus-discriminative-modeling">Generative Versus Discriminative Modeling</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster">Example of discriminative modeling, taken from Generative Deeep Learning by David Foster</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster">Example of discriminative modeling, taken from Generative Deep Learning by David Foster</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#discriminative-modeling">Discriminative Modeling</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#a-frequentist-approach-to-data-analysis">A Frequentist approach to data analysis</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-is-a-good-model">What is a good model?</a></li>
@@ -771,8 +771,8 @@ counterpart, discriminative modeling. If you have studied machine
 learning, most problems you will have faced will have most likely been
 discriminative in nature.</p>
 </section>
-<section id="example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster">
-<h2>Example of discriminative modeling, <a class="reference external" href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html">taken from Generative Deeep Learning by David Foster</a><a class="headerlink" href="#example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster" title="Link to this heading">#</a></h2>
+<section id="example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster">
+<h2>Example of discriminative modeling, <a class="reference external" href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html">taken from Generative Deep Learning by David Foster</a><a class="headerlink" href="#example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster" title="Link to this heading">#</a></h2>
 <!-- dom:FIGURE: [figures/standarddeeplearning.png, width=900 frac=1.0] -->
 <!-- begin figure -->
 <p><img src="figures/standarddeeplearning.png" width="900"><p style="font-size: 0.9em"><i>Figure 1: </i></p></p>
@@ -979,7 +979,7 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 <p><a class="reference external" href="http://www.numpy.org/">Numpy</a> provides an easy way to handle arrays in Python. The standard way to import this library is as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
 </pre></div>
 </div>
 </div>
@@ -987,9 +987,9 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 <p>Here follows a simple example where we set up an array of ten elements, all determined by random numbers drawn according to the normal distribution,</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>n = 10
+x = np.random.normal(size=n)
+print(x)
 </pre></div>
 </div>
 </div>
@@ -998,9 +998,9 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 Another alternative is to declare a vector as follows</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+x = np.array([1, 2, 3])
+print(x)
 </pre></div>
 </div>
 </div>
@@ -1009,9 +1009,9 @@ Another alternative is to declare a vector as follows</p>
 start numbering array elements from <span class="math notranslate nohighlight">\(0\)</span> and on. This means that a vector with <span class="math notranslate nohighlight">\(n\)</span> elements has a sequence of entities <span class="math notranslate nohighlight">\(x_0, x_1, x_2, \dots, x_{n-1}\)</span>. We could also let (recommended) Numpy to compute the logarithms of a specific array as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+x = np.log(np.array([4, 7, 8]))
+print(x)
 </pre></div>
 </div>
 </div>
@@ -1025,12 +1025,12 @@ from Python’s <strong>math</strong> module. The looping is done explicitely by
 logarithms of a vector would be to write</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">log</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)):</span>
-    <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+from math import log
+x = np.array([4, 7, 8])
+for i in range(0, len(x)):
+    x[i] = log(x[i])
+print(x)
 </pre></div>
 </div>
 </div>
@@ -1039,9 +1039,9 @@ logarithms of a vector would be to write</p>
 The attentive reader will also notice that the output is <span class="math notranslate nohighlight">\([1, 1, 2]\)</span>. Python interprets automagically our numbers as integers (like the <strong>automatic</strong> keyword in C++). To change this we could define our array elements to be double precision numbers as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+x = np.log(np.array([4, 7, 8], dtype = np.float64))
+print(x)
 </pre></div>
 </div>
 </div>
@@ -1049,9 +1049,9 @@ The attentive reader will also notice that the output is <span class="math notra
 <p>or simply write them as double precision numbers (Python uses 64 bits as default for floating point type variables), that is</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">]))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+x = np.log(np.array([4.0, 7.0, 8.0]))
+print(x)
 </pre></div>
 </div>
 </div>
@@ -1059,9 +1059,9 @@ The attentive reader will also notice that the output is <span class="math notra
 <p>To check the number of bytes (remember that one byte contains eight bits for double precision variables), you can use simple use the <strong>itemsize</strong> functionality (the array <span class="math notranslate nohighlight">\(x\)</span> is actually an object which inherits the functionalities defined in Numpy) as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">]))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">itemsize</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+x = np.log(np.array([4.0, 7.0, 8.0]))
+print(x.itemsize)
 </pre></div>
 </div>
 </div>
@@ -1074,9 +1074,9 @@ define a <span class="math notranslate nohighlight">\(3 \times 3 \)</span> real
 lowercase letters for vectors and uppercase letters for matrices)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="mf">11.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]</span> <span class="p">]))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
+print(A)
 </pre></div>
 </div>
 </div>
@@ -1084,10 +1084,10 @@ lowercase letters for vectors and uppercase letters for matrices)</p>
 <p>If we use the <strong>shape</strong> function we would get <span class="math notranslate nohighlight">\((3, 3)\)</span> as output, that is verifying that our matrix is a <span class="math notranslate nohighlight">\(3\times 3\)</span> matrix. We can slice the matrix and print for example the first column (Python organized matrix elements in a row-major order, see below) as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="mf">11.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]</span> <span class="p">]))</span>
-<span class="c1"># print the first column, row-major order and elements start with 0</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">A</span><span class="p">[:,</span><span class="mi">0</span><span class="p">])</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
+# print the first column, row-major order and elements start with 0
+print(A[:,0])
 </pre></div>
 </div>
 </div>
@@ -1095,10 +1095,10 @@ lowercase letters for vectors and uppercase letters for matrices)</p>
 <p>We can continue this was by printing out other columns or rows. The example here prints out the second column</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="mf">11.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]</span> <span class="p">]))</span>
-<span class="c1"># print the first column, row-major order and elements start with 0</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">,:])</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
+# print the first column, row-major order and elements start with 0
+print(A[1,:])
 </pre></div>
 </div>
 </div>
@@ -1106,11 +1106,11 @@ lowercase letters for vectors and uppercase letters for matrices)</p>
 <p>Numpy contains many other functionalities that allow us to slice, subdivide etc etc arrays. We strongly recommend that you look up the <a class="reference external" href="http://www.numpy.org/">Numpy website for more details</a>. Useful functions when defining a matrix are the <strong>np.zeros</strong> function which declares a matrix of a given dimension and sets all elements to zero</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">n</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="c1"># define a matrix of dimension 10 x 10 and set all elements to zero</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+n = 10
+# define a matrix of dimension 10 x 10 and set all elements to zero
+A = np.zeros( (n, n) )
+print(A)
 </pre></div>
 </div>
 </div>
@@ -1118,11 +1118,11 @@ lowercase letters for vectors and uppercase letters for matrices)</p>
 <p>or initializing all elements to</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">n</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="c1"># define a matrix of dimension 10 x 10 and set all elements to one</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+n = 10
+# define a matrix of dimension 10 x 10 and set all elements to one
+A = np.ones( (n, n) )
+print(A)
 </pre></div>
 </div>
 </div>
@@ -1130,11 +1130,11 @@ lowercase letters for vectors and uppercase letters for matrices)</p>
 <p>or as unitarily distributed random numbers (see the material on random number generators in the statistics part)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">n</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="c1"># define a matrix of dimension 10 x 10 and set all elements to random numbers with x \in [0, 1]</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+n = 10
+# define a matrix of dimension 10 x 10 and set all elements to random numbers with x \in [0, 1]
+A = np.random.rand(n, n)
+print(A)
 </pre></div>
 </div>
 </div>
@@ -1170,40 +1170,40 @@ function <strong>np.mean(x)</strong>. We can also extract the eigenvalues of the
 covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Importing various packages</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Importing various packages
+import numpy as np
 
-<span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">4</span><span class="o">+</span><span class="mi">3</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">**</span><span class="mi">3</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">z</span><span class="p">))</span>
-<span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
-<span class="n">Sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cov</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">Sigma</span><span class="p">)</span>
-<span class="n">Eigvals</span><span class="p">,</span> <span class="n">Eigvecs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">Sigma</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">Eigvals</span><span class="p">)</span>
+n = 100
+x = np.random.normal(size=n)
+print(np.mean(x))
+y = 4+3*x+np.random.normal(size=n)
+print(np.mean(y))
+z = x**3+np.random.normal(size=n)
+print(np.mean(z))
+W = np.vstack((x, y, z))
+Sigma = np.cov(W)
+print(Sigma)
+Eigvals, Eigvecs = np.linalg.eig(Sigma)
+print(Eigvals)
 </pre></div>
 </div>
 </div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
 
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
-<span class="n">eye</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">eye</span><span class="p">)</span>
-<span class="n">sparse_mtx</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span><span class="n">eye</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">sparse_mtx</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import sparse
+eye = np.eye(4)
+print(eye)
+sparse_mtx = sparse.csr_matrix(eye)
+print(sparse_mtx)
+x = np.linspace(-10,10,100)
+y = np.sin(x)
+plt.plot(x,y,marker=&#39;x&#39;)
+plt.show()
 </pre></div>
 </div>
 </div>
@@ -1224,15 +1224,15 @@ analysis tools for Python. <strong>pandas</strong> stands for panel data, a term
 <p>The following simple example shows how we can, in an easy way make tables of our data. Here we define a data set which includes names, place of birth and date of birth, and displays the data in an easy to read way. We will see repeated use of <strong>pandas</strong>, in particular in connection with classification of data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
-<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;First Name&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Frodo&quot;</span><span class="p">,</span> <span class="s2">&quot;Bilbo&quot;</span><span class="p">,</span> <span class="s2">&quot;Aragorn II&quot;</span><span class="p">,</span> <span class="s2">&quot;Samwise&quot;</span><span class="p">],</span>
-        <span class="s1">&#39;Last Name&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Baggins&quot;</span><span class="p">,</span> <span class="s2">&quot;Baggins&quot;</span><span class="p">,</span><span class="s2">&quot;Elessar&quot;</span><span class="p">,</span><span class="s2">&quot;Gamgee&quot;</span><span class="p">],</span>
-        <span class="s1">&#39;Place of birth&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Shire&quot;</span><span class="p">,</span> <span class="s2">&quot;Shire&quot;</span><span class="p">,</span> <span class="s2">&quot;Eriador&quot;</span><span class="p">,</span> <span class="s2">&quot;Shire&quot;</span><span class="p">],</span>
-        <span class="s1">&#39;Date of Birth T.A.&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2968</span><span class="p">,</span> <span class="mi">2890</span><span class="p">,</span> <span class="mi">2931</span><span class="p">,</span> <span class="mi">2980</span><span class="p">]</span>
-        <span class="p">}</span>
-<span class="n">data_pandas</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="n">display</span><span class="p">(</span><span class="n">data_pandas</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+from IPython.display import display
+data = {&#39;First Name&#39;: [&quot;Frodo&quot;, &quot;Bilbo&quot;, &quot;Aragorn II&quot;, &quot;Samwise&quot;],
+        &#39;Last Name&#39;: [&quot;Baggins&quot;, &quot;Baggins&quot;,&quot;Elessar&quot;,&quot;Gamgee&quot;],
+        &#39;Place of birth&#39;: [&quot;Shire&quot;, &quot;Shire&quot;, &quot;Eriador&quot;, &quot;Shire&quot;],
+        &#39;Date of Birth T.A.&#39;: [2968, 2890, 2931, 2980]
+        }
+data_pandas = pd.DataFrame(data)
+display(data_pandas)
 </pre></div>
 </div>
 </div>
@@ -1243,8 +1243,8 @@ Displaying these results, we see that the indices are given by the default numbe
 <strong>pandas</strong> is extremely flexible and we can easily change the above indices by defining a new type of indexing as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_pandas</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Frodo&#39;</span><span class="p">,</span><span class="s1">&#39;Bilbo&#39;</span><span class="p">,</span><span class="s1">&#39;Aragorn&#39;</span><span class="p">,</span><span class="s1">&#39;Sam&#39;</span><span class="p">])</span>
-<span class="n">display</span><span class="p">(</span><span class="n">data_pandas</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>data_pandas = pd.DataFrame(data,index=[&#39;Frodo&#39;,&#39;Bilbo&#39;,&#39;Aragorn&#39;,&#39;Sam&#39;])
+display(data_pandas)
 </pre></div>
 </div>
 </div>
@@ -1252,7 +1252,7 @@ Displaying these results, we see that the indices are given by the default numbe
 <p>Thereafter we display the content of the row which begins with the index <strong>Aragorn</strong></p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">data_pandas</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;Aragorn&#39;</span><span class="p">])</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>display(data_pandas.loc[&#39;Aragorn&#39;])
 </pre></div>
 </div>
 </div>
@@ -1260,13 +1260,13 @@ Displaying these results, we see that the indices are given by the default numbe
 <p>We can easily append data to this, for example</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">new_hobbit</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;First Name&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Peregrin&quot;</span><span class="p">],</span>
-              <span class="s1">&#39;Last Name&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Took&quot;</span><span class="p">],</span>
-              <span class="s1">&#39;Place of birth&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Shire&quot;</span><span class="p">],</span>
-              <span class="s1">&#39;Date of Birth T.A.&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2990</span><span class="p">]</span>
-              <span class="p">}</span>
-<span class="n">data_pandas</span><span class="o">=</span><span class="n">data_pandas</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">new_hobbit</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Pippin&#39;</span><span class="p">]))</span>
-<span class="n">display</span><span class="p">(</span><span class="n">data_pandas</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>new_hobbit = {&#39;First Name&#39;: [&quot;Peregrin&quot;],
+              &#39;Last Name&#39;: [&quot;Took&quot;],
+              &#39;Place of birth&#39;: [&quot;Shire&quot;],
+              &#39;Date of Birth T.A.&#39;: [2990]
+              }
+data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=[&#39;Pippin&#39;]))
+display(data_pandas)
 </pre></div>
 </div>
 </div>
@@ -1275,19 +1275,19 @@ Displaying these results, we see that the indices are given by the default numbe
 of dimensionality <span class="math notranslate nohighlight">\(10\times 5\)</span> and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-<span class="c1"># setting up a 10 x 5 matrix</span>
-<span class="n">rows</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">cols</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span><span class="n">cols</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">std</span><span class="p">())</span>
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+import pandas as pd
+from IPython.display import display
+np.random.seed(100)
+# setting up a 10 x 5 matrix
+rows = 10
+cols = 5
+a = np.random.randn(rows,cols)
+df = pd.DataFrame(a)
+display(df)
+print(df.mean())
+print(df.std())
+display(df**2)
 </pre></div>
 </div>
 </div>
@@ -1295,24 +1295,24 @@ of dimensionality <span class="math notranslate nohighlight">\(10\times 5\)</spa
 <p>Thereafter we can select specific columns only and plot final results</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;First&#39;</span><span class="p">,</span> <span class="s1">&#39;Second&#39;</span><span class="p">,</span> <span class="s1">&#39;Third&#39;</span><span class="p">,</span> <span class="s1">&#39;Fourth&#39;</span><span class="p">,</span> <span class="s1">&#39;Fifth&#39;</span><span class="p">]</span>
-<span class="n">df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>df.columns = [&#39;First&#39;, &#39;Second&#39;, &#39;Third&#39;, &#39;Fourth&#39;, &#39;Fifth&#39;]
+df.index = np.arange(10)
 
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Second&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+display(df)
+print(df[&#39;Second&#39;].mean() )
+print(df.info())
+print(df.describe())
 
-<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="n">plt</span><span class="p">,</span> <span class="n">mpl</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
-<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.family&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;serif&#39;</span>
+from pylab import plt, mpl
+plt.style.use(&#39;seaborn&#39;)
+mpl.rcParams[&#39;font.family&#39;] = &#39;serif&#39;
 
-<span class="n">df</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lw</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+df.cumsum().plot(lw=2.0, figsize=(10,6))
+plt.show()
 
 
-<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">),</span> <span class="n">rot</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+df.plot.bar(figsize=(10,6), rot=15)
+plt.show()
 </pre></div>
 </div>
 </div>
@@ -1320,10 +1320,10 @@ of dimensionality <span class="math notranslate nohighlight">\(10\times 5\)</spa
 <p>We can produce a <span class="math notranslate nohighlight">\(4\times 4\)</span> matrix</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
-<span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>b = np.arange(16).reshape((4,4))
+print(b)
+df1 = pd.DataFrame(b)
+print(df1)
 </pre></div>
 </div>
 </div>
@@ -1381,25 +1381,25 @@ data with a straight line.</p>
 <p>The Python code follows here.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Importing various packages</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Importing various packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
 
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">linreg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">linreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
-<span class="n">xnew</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">]])</span>
-<span class="n">ypredict</span> <span class="o">=</span> <span class="n">linreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">xnew</span><span class="p">)</span>
+x = np.random.rand(100,1)
+y = 2*x+np.random.randn(100,1)
+linreg = LinearRegression()
+linreg.fit(x,y)
+xnew = np.array([[0],[1]])
+ypredict = linreg.predict(xnew)
 
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xnew</span><span class="p">,</span> <span class="n">ypredict</span><span class="p">,</span> <span class="s2">&quot;r-&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">,</span><span class="s1">&#39;ro&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$x$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$y$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Simple Linear Regression&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+plt.plot(xnew, ypredict, &quot;r-&quot;)
+plt.plot(x, y ,&#39;ro&#39;)
+plt.axis([0,1.0,0, 5.0])
+plt.xlabel(r&#39;$x$&#39;)
+plt.ylabel(r&#39;$y$&#39;)
+plt.title(r&#39;Simple Linear Regression&#39;)
+plt.show()
 </pre></div>
 </div>
 </div>
@@ -1465,22 +1465,22 @@ to be dominated by outliers.</p>
 <p>We can modify easily the above Python code and plot the relative error instead</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
 
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">5</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="mf">0.01</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">linreg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">linreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
-<span class="n">ypredict</span> <span class="o">=</span> <span class="n">linreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+x = np.random.rand(100,1)
+y = 5*x+0.01*np.random.randn(100,1)
+linreg = LinearRegression()
+linreg.fit(x,y)
+ypredict = linreg.predict(x)
 
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">ypredict</span><span class="o">-</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">abs</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="s2">&quot;ro&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$x$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\epsilon_{\mathrm</span><span class="si">{relative}</span><span class="s1">}$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Relative error&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+plt.plot(x, np.abs(ypredict-y)/abs(y), &quot;ro&quot;)
+plt.axis([0,1.0,0.0, 0.5])
+plt.xlabel(r&#39;$x$&#39;)
+plt.ylabel(r&#39;$\epsilon_{\mathrm{relative}}$&#39;)
+plt.title(r&#39;Relative error&#39;)
+plt.show()
 </pre></div>
 </div>
 </div>
@@ -1497,33 +1497,33 @@ other properties from the statistical data analysis.</p>
 example of the functionality of <strong>Scikit-Learn</strong>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> 
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> 
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span> 
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span><span class="p">,</span> <span class="n">mean_squared_log_error</span><span class="p">,</span> <span class="n">mean_absolute_error</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np 
+import matplotlib.pyplot as plt 
+from sklearn.linear_model import LinearRegression 
+from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error
 
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">+</span> <span class="mi">5</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">linreg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">linreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
-<span class="n">ypredict</span> <span class="o">=</span> <span class="n">linreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The intercept alpha: </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">linreg</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Coefficient beta : </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">linreg</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="c1"># The mean squared error                               </span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean squared error: </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ypredict</span><span class="p">))</span>
-<span class="c1"># Explained variance score: 1 is perfect prediction                                 </span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Variance score: </span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ypredict</span><span class="p">))</span>
-<span class="c1"># Mean squared log error                                                        </span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mean squared log error: </span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">mean_squared_log_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ypredict</span><span class="p">)</span> <span class="p">)</span>
-<span class="c1"># Mean absolute error                                                           </span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mean absolute error: </span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ypredict</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ypredict</span><span class="p">,</span> <span class="s2">&quot;r-&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">,</span><span class="s1">&#39;ro&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$x$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$y$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Linear Regression fit &#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+x = np.random.rand(100,1)
+y = 2.0+ 5*x+0.5*np.random.randn(100,1)
+linreg = LinearRegression()
+linreg.fit(x,y)
+ypredict = linreg.predict(x)
+print(&#39;The intercept alpha: \n&#39;, linreg.intercept_)
+print(&#39;Coefficient beta : \n&#39;, linreg.coef_)
+# The mean squared error                               
+print(&quot;Mean squared error: %.2f&quot; % mean_squared_error(y, ypredict))
+# Explained variance score: 1 is perfect prediction                                 
+print(&#39;Variance score: %.2f&#39; % r2_score(y, ypredict))
+# Mean squared log error                                                        
+print(&#39;Mean squared log error: %.2f&#39; % mean_squared_log_error(y, ypredict) )
+# Mean absolute error                                                           
+print(&#39;Mean absolute error: %.2f&#39; % mean_absolute_error(y, ypredict))
+plt.plot(x, ypredict, &quot;r-&quot;)
+plt.plot(x, y ,&#39;ro&#39;)
+plt.axis([0.0,1.0,1.5, 7.0])
+plt.xlabel(r&#39;$x$&#39;)
+plt.ylabel(r&#39;$y$&#39;)
+plt.title(r&#39;Linear Regression fit &#39;)
+plt.show()
 </pre></div>
 </div>
 </div>
@@ -1656,39 +1656,39 @@ After having downloaded this file to our own computer, we are now ready to read
 <p>We start with preparing folders for storing our calculations and the data file over masses and binding energies. We import also various modules that we will find useful in order to present various Machine Learning methods. Here we focus mainly on the functionality of <strong>scikit-learn</strong>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Common imports</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">sklearn.linear_model</span> <span class="k">as</span> <span class="nn">skl</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span><span class="p">,</span> <span class="n">mean_absolute_error</span>
-<span class="kn">import</span> <span class="nn">os</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Common imports
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import sklearn.linear_model as skl
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
+import os
 
-<span class="c1"># Where to save the figures and data files</span>
-<span class="n">PROJECT_ROOT_DIR</span> <span class="o">=</span> <span class="s2">&quot;Results&quot;</span>
-<span class="n">FIGURE_ID</span> <span class="o">=</span> <span class="s2">&quot;Results/FigureFiles&quot;</span>
-<span class="n">DATA_ID</span> <span class="o">=</span> <span class="s2">&quot;DataFiles/&quot;</span>
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = &quot;Results&quot;
+FIGURE_ID = &quot;Results/FigureFiles&quot;
+DATA_ID = &quot;DataFiles/&quot;
 
-<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">PROJECT_ROOT_DIR</span><span class="p">):</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">PROJECT_ROOT_DIR</span><span class="p">)</span>
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
 
-<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">FIGURE_ID</span><span class="p">):</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">FIGURE_ID</span><span class="p">)</span>
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
 
-<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">DATA_ID</span><span class="p">):</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">DATA_ID</span><span class="p">)</span>
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
 
-<span class="k">def</span> <span class="nf">image_path</span><span class="p">(</span><span class="n">fig_id</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">FIGURE_ID</span><span class="p">,</span> <span class="n">fig_id</span><span class="p">)</span>
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
 
-<span class="k">def</span> <span class="nf">data_path</span><span class="p">(</span><span class="n">dat_id</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_ID</span><span class="p">,</span> <span class="n">dat_id</span><span class="p">)</span>
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
 
-<span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig_id</span><span class="p">):</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">image_path</span><span class="p">(</span><span class="n">fig_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.png&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;png&#39;</span><span class="p">)</span>
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + &quot;.png&quot;, format=&#39;png&#39;)
 
-<span class="n">infile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">data_path</span><span class="p">(</span><span class="s2">&quot;MassEval2016.dat&quot;</span><span class="p">),</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+infile = open(data_path(&quot;MassEval2016.dat&quot;),&#39;r&#39;)
 </pre></div>
 </div>
 </div>
@@ -1701,16 +1701,16 @@ data) to actually open the file and simply take a look at it!</p>
 <p>In particular, the program that outputs the final nuclear masses is written in Fortran with a specific format. It means that we need to figure out the format and which columns contain the data we are interested in. Pandas comes with a function that reads formatted output. After having admired the file, we are now ready to start massaging it with <strong>pandas</strong>. The file begins with some basic format information.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;                                                                                                                         </span>
-<span class="sd">This is taken from the data file of the mass 2016 evaluation.                                                               </span>
-<span class="sd">All files are 3436 lines long with 124 character per line.                                                                  </span>
-<span class="sd">       Headers are 39 lines long.                                                                                           </span>
-<span class="sd">   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     </span>
-<span class="sd">   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     </span>
-<span class="sd">   These formats are reflected in the pandas widths variable below, see the statement                                       </span>
-<span class="sd">   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            </span>
-<span class="sd">   Pandas has also a variable header, with length 39 in this case.                                                          </span>
-<span class="sd">&quot;&quot;&quot;</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&quot;&quot;&quot;                                                                                                                         
+This is taken from the data file of the mass 2016 evaluation.                                                               
+All files are 3436 lines long with 124 character per line.                                                                  
+       Headers are 39 lines long.                                                                                           
+   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     
+   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     
+   These formats are reflected in the pandas widths variable below, see the statement                                       
+   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            
+   Pandas has also a variable header, with length 39 in this case.                                                          
+&quot;&quot;&quot;
 </pre></div>
 </div>
 </div>
@@ -1721,24 +1721,24 @@ respectively. We add also for the sake of completeness the element name. The dat
 covert them into the <strong>pandas</strong> DataFrame structure.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Read the experimental data with Pandas</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">infile</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">11</span><span class="p">),</span>
-              <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;Z&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;Element&#39;</span><span class="p">,</span> <span class="s1">&#39;Ebinding&#39;</span><span class="p">),</span>
-              <span class="n">widths</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span>
-              <span class="n">header</span><span class="o">=</span><span class="mi">39</span><span class="p">,</span>
-              <span class="n">index_col</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Read the experimental data with Pandas
+Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
+              names=(&#39;N&#39;, &#39;Z&#39;, &#39;A&#39;, &#39;Element&#39;, &#39;Ebinding&#39;),
+              widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
+              header=39,
+              index_col=False)
 
-<span class="c1"># Extrapolated values are indicated by &#39;#&#39; in place of the decimal place, so</span>
-<span class="c1"># the Ebinding column won&#39;t be numeric. Coerce to float and drop these entries.</span>
-<span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">],</span> <span class="n">errors</span><span class="o">=</span><span class="s1">&#39;coerce&#39;</span><span class="p">)</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">Masses</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
-<span class="c1"># Convert from keV to MeV.</span>
-<span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">]</span> <span class="o">/=</span> <span class="mi">1000</span>
+# Extrapolated values are indicated by &#39;#&#39; in place of the decimal place, so
+# the Ebinding column won&#39;t be numeric. Coerce to float and drop these entries.
+Masses[&#39;Ebinding&#39;] = pd.to_numeric(Masses[&#39;Ebinding&#39;], errors=&#39;coerce&#39;)
+Masses = Masses.dropna()
+# Convert from keV to MeV.
+Masses[&#39;Ebinding&#39;] /= 1000
 
-<span class="c1"># Group the DataFrame by nucleon number, A.</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">Masses</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
-<span class="c1"># Find the rows of the grouped DataFrame with the maximum binding energy.</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">Masses</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">t</span><span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">Ebinding</span><span class="o">==</span><span class="n">t</span><span class="o">.</span><span class="n">Ebinding</span><span class="o">.</span><span class="n">max</span><span class="p">()])</span>
+# Group the DataFrame by nucleon number, A.
+Masses = Masses.groupby(&#39;A&#39;)
+# Find the rows of the grouped DataFrame with the maximum binding energy.
+Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])
 </pre></div>
 </div>
 </div>
@@ -1754,31 +1754,12 @@ to make some simple fits using both the functionalities in <strong>numpy</strong
 the number of nucleons <span class="math notranslate nohighlight">\(A\)</span>, the number of protons <span class="math notranslate nohighlight">\(Z\)</span> and the number of neutrons <span class="math notranslate nohighlight">\(N\)</span>, the element name and finally the energies themselves.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span>
-<span class="n">Z</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Z&#39;</span><span class="p">]</span>
-<span class="n">N</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">]</span>
-<span class="n">Element</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Element&#39;</span><span class="p">]</span>
-<span class="n">Energies</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">Masses</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>            N    Z    A Element  Ebinding
-A                                        
-4   0       1    3    4      Li  1.153760
-5   2       3    2    5      He  5.512132
-6   7       3    3    6      Li  5.332331
-7   12      4    3    7      Li  5.606439
-8   17      4    4    8      Be  7.062435
-...       ...  ...  ...     ...       ...
-264 3297  156  108  264      Hs  7.298375
-265 3303  157  108  265      Hs  7.296247
-266 3310  158  108  266      Hs  7.298273
-269 3331  159  110  269      Ds  7.250154
-270 3337  160  110  270      Ds  7.253775
-
-[264 rows x 5 columns]
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>A = Masses[&#39;A&#39;]
+Z = Masses[&#39;Z&#39;]
+N = Masses[&#39;N&#39;]
+Element = Masses[&#39;Element&#39;]
+Energies = Masses[&#39;Ebinding&#39;]
+print(Masses)
 </pre></div>
 </div>
 </div>
@@ -1787,13 +1768,13 @@ A
 It has dimensionality <span class="math notranslate nohighlight">\(p\times n\)</span>, where <span class="math notranslate nohighlight">\(n\)</span> is the number of data points and <span class="math notranslate nohighlight">\(p\)</span> are the so-called predictors. In our case here they are given by the number of polynomials in <span class="math notranslate nohighlight">\(A\)</span> we wish to include in the fit.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now we set up the design matrix X</span>
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">A</span><span class="p">),</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="o">**</span><span class="p">(</span><span class="mf">2.0</span><span class="o">/</span><span class="mf">3.0</span><span class="p">)</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="o">/</span><span class="mf">3.0</span><span class="p">)</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Now we set up the design matrix X
+X = np.zeros((len(A),5))
+X[:,0] = 1
+X[:,1] = A
+X[:,2] = A**(2.0/3.0)
+X[:,3] = A**(-1.0/3.0)
+X[:,4] = A**(-1.0)
 </pre></div>
 </div>
 </div>
@@ -1801,8 +1782,8 @@ It has dimensionality <span class="math notranslate nohighlight">\(p\times n\)</
 <p>With <strong>scikitlearn</strong> we are now ready to use linear regression and fit our data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">skl</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Energies</span><span class="p">)</span>
-<span class="n">fity</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>clf = skl.LinearRegression().fit(X, Energies)
+fity = clf.predict(X)
 </pre></div>
 </div>
 </div>
@@ -1811,39 +1792,29 @@ It has dimensionality <span class="math notranslate nohighlight">\(p\times n\)</
 Now we can print measures of how our fit is doing, the coefficients from the fits and plot the final fit together with our data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The mean squared error                               </span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean squared error: </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Energies</span><span class="p">,</span> <span class="n">fity</span><span class="p">))</span>
-<span class="c1"># Explained variance score: 1 is perfect prediction                                 </span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Variance score: </span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">Energies</span><span class="p">,</span> <span class="n">fity</span><span class="p">))</span>
-<span class="c1"># Mean absolute error                                                           </span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mean absolute error: </span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">Energies</span><span class="p">,</span> <span class="n">fity</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># The mean squared error                               
+print(&quot;Mean squared error: %.2f&quot; % mean_squared_error(Energies, fity))
+# Explained variance score: 1 is perfect prediction                                 
+print(&#39;Variance score: %.2f&#39; % r2_score(Energies, fity))
+# Mean absolute error                                                           
+print(&#39;Mean absolute error: %.2f&#39; % mean_absolute_error(Energies, fity))
+print(clf.coef_, clf.intercept_)
 
-<span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Eapprox&#39;</span><span class="p">]</span>  <span class="o">=</span> <span class="n">fity</span>
-<span class="c1"># Generate a plot comparing the experimental with the fitted values values.</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$A = N + Z$&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$E_\mathrm</span><span class="si">{bind}</span><span class="s1">\,/\mathrm</span><span class="si">{MeV}</span><span class="s1">$&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">],</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ame2016&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">],</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Eapprox&#39;</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;m&#39;</span><span class="p">,</span>
-            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Fit&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">save_fig</span><span class="p">(</span><span class="s2">&quot;Masses2016&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+Masses[&#39;Eapprox&#39;]  = fity
+# Generate a plot comparing the experimental with the fitted values values.
+fig, ax = plt.subplots()
+ax.set_xlabel(r&#39;$A = N + Z$&#39;)
+ax.set_ylabel(r&#39;$E_\mathrm{bind}\,/\mathrm{MeV}$&#39;)
+ax.plot(Masses[&#39;A&#39;], Masses[&#39;Ebinding&#39;], alpha=0.7, lw=2,
+            label=&#39;Ame2016&#39;)
+ax.plot(Masses[&#39;A&#39;], Masses[&#39;Eapprox&#39;], alpha=0.7, lw=2, c=&#39;m&#39;,
+            label=&#39;Fit&#39;)
+ax.legend()
+save_fig(&quot;Masses2016&quot;)
+plt.show()
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean squared error: 0.02
-Variance score: 0.95
-Mean absolute error: 0.05
-[ 0.00000000e+00 -2.96611194e-02  2.01719003e-01  1.08078025e+01
- -4.03097597e+01] 5.294399745619595
-</pre></div>
-</div>
-<img alt="_images/df7b764b543c6e41eeaa5ee2d1d6e85e9f6c059c7cab9b3fc7a2f38d7dfb90ec.png" src="_images/df7b764b543c6e41eeaa5ee2d1d6e85e9f6c059c7cab9b3fc7a2f38d7dfb90ec.png" />
-</div>
 </div>
 </section>
 <section id="and-what-about-using-neural-networks">
@@ -1852,91 +1823,40 @@ Mean absolute error: 0.05
 functionality.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <span class="n">MLPRegressor</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>from sklearn.neural_network import MLPRegressor
+from sklearn.metrics import accuracy_score
+import seaborn as sns
 
 
-<span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span>
-<span class="n">Y_train</span> <span class="o">=</span> <span class="n">Energies</span>
-<span class="n">n_hidden_neurons</span> <span class="o">=</span> <span class="mi">50</span>
-<span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="c1"># store models for later use</span>
-<span class="n">eta_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="n">lmbd_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="c1"># store the models for later use</span>
-<span class="n">DNN_scikit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
-<span class="n">train_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)))</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">eta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
-        <span class="n">dnn</span> <span class="o">=</span> <span class="n">MLPRegressor</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="n">n_hidden_neurons</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
-                            <span class="n">alpha</span><span class="o">=</span><span class="n">lmbd</span><span class="p">,</span> <span class="n">learning_rate_init</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
-        <span class="n">dnn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
-        <span class="n">DNN_scikit</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dnn</span>
-        <span class="n">train_accuracy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dnn</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
-        <span class="n">fity</span> <span class="o">=</span> <span class="n">dnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
-        <span class="n">MSE</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">fity</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean squared error: </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">fity</span><span class="p">))</span>
-        <span class="n">train_accuracy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">train_accuracy</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Training Accuracy&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;$\eta$&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;$\lambda$&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">train_accuracy</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean squared error: 1.73
-Mean squared error: 19.48
-Mean squared error: 12.11
-Mean squared error: 23.42
-Mean squared error: 0.23
-Mean squared error: 0.18
-Mean squared error: 0.29
-Mean squared error: 0.20
-Mean squared error: 0.26
-Mean squared error: 4.07
-Mean squared error: 2.12
-Mean squared error: 7.54
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean squared error: 15.78
-Mean squared error: 122.02
-Mean squared error: 51.22
-Mean squared error: 152.55
-</pre></div>
-</div>
-<img alt="_images/1b1c59fe24a1c61677966d3734d014c13848c51a6e194c77783efd4db527d26b.png" src="_images/1b1c59fe24a1c61677966d3734d014c13848c51a6e194c77783efd4db527d26b.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[  1.7304881   19.47958494  12.11340253  23.41589548]
- [  0.22948497   0.18392847   0.29364655   0.20072279]
- [  0.26303845   4.06730814   2.11590451   7.5411205 ]
- [ 15.77893972 122.02334824  51.2167072  152.54894451]]
+X_train = X
+Y_train = Energies
+n_hidden_neurons = 50
+epochs = 100
+# store models for later use
+eta_vals = np.logspace(-3, 0, 4)
+lmbd_vals = np.logspace(-3, 0, 4)
+# store the models for later use
+DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+sns.set()
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation=&#39;relu&#39;, solver=&#39;adam&#39;,
+                            alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
+        dnn.fit(X_train, Y_train)
+        DNN_scikit[i][j] = dnn
+        train_accuracy[i][j] = dnn.score(X_train, Y_train)
+        fity = dnn.predict(X_train)
+        MSE = mean_squared_error(Y_train, fity)
+        print(&quot;Mean squared error: %.2f&quot; % mean_squared_error(Y_train, fity))
+        train_accuracy[i][j] = MSE
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
+ax.set_title(&quot;Training Accuracy&quot;)
+ax.set_ylabel(&quot;$\eta$&quot;)
+ax.set_xlabel(&quot;$\lambda$&quot;)
+plt.show()
+print(train_accuracy)
 </pre></div>
 </div>
 </div>
@@ -2145,75 +2065,75 @@ our matrix as <span class="math notranslate nohighlight">\(\boldsymbol{X}\in {\m
 <p>We restate the parts of the code we are most interested in.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Common imports</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
-<span class="kn">import</span> <span class="nn">os</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Common imports
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from IPython.display import display
+import os
 
-<span class="c1"># Where to save the figures and data files</span>
-<span class="n">PROJECT_ROOT_DIR</span> <span class="o">=</span> <span class="s2">&quot;Results&quot;</span>
-<span class="n">FIGURE_ID</span> <span class="o">=</span> <span class="s2">&quot;Results/FigureFiles&quot;</span>
-<span class="n">DATA_ID</span> <span class="o">=</span> <span class="s2">&quot;DataFiles/&quot;</span>
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = &quot;Results&quot;
+FIGURE_ID = &quot;Results/FigureFiles&quot;
+DATA_ID = &quot;DataFiles/&quot;
 
-<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">PROJECT_ROOT_DIR</span><span class="p">):</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">PROJECT_ROOT_DIR</span><span class="p">)</span>
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
 
-<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">FIGURE_ID</span><span class="p">):</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">FIGURE_ID</span><span class="p">)</span>
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
 
-<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">DATA_ID</span><span class="p">):</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">DATA_ID</span><span class="p">)</span>
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
 
-<span class="k">def</span> <span class="nf">image_path</span><span class="p">(</span><span class="n">fig_id</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">FIGURE_ID</span><span class="p">,</span> <span class="n">fig_id</span><span class="p">)</span>
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
 
-<span class="k">def</span> <span class="nf">data_path</span><span class="p">(</span><span class="n">dat_id</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_ID</span><span class="p">,</span> <span class="n">dat_id</span><span class="p">)</span>
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
 
-<span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig_id</span><span class="p">):</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">image_path</span><span class="p">(</span><span class="n">fig_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.png&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;png&#39;</span><span class="p">)</span>
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + &quot;.png&quot;, format=&#39;png&#39;)
 
-<span class="n">infile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">data_path</span><span class="p">(</span><span class="s2">&quot;MassEval2016.dat&quot;</span><span class="p">),</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+infile = open(data_path(&quot;MassEval2016.dat&quot;),&#39;r&#39;)
 
 
-<span class="c1"># Read the experimental data with Pandas</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">infile</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">11</span><span class="p">),</span>
-              <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;Z&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;Element&#39;</span><span class="p">,</span> <span class="s1">&#39;Ebinding&#39;</span><span class="p">),</span>
-              <span class="n">widths</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span>
-              <span class="n">header</span><span class="o">=</span><span class="mi">39</span><span class="p">,</span>
-              <span class="n">index_col</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+# Read the experimental data with Pandas
+Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
+              names=(&#39;N&#39;, &#39;Z&#39;, &#39;A&#39;, &#39;Element&#39;, &#39;Ebinding&#39;),
+              widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
+              header=39,
+              index_col=False)
 
-<span class="c1"># Extrapolated values are indicated by &#39;#&#39; in place of the decimal place, so</span>
-<span class="c1"># the Ebinding column won&#39;t be numeric. Coerce to float and drop these entries.</span>
-<span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">],</span> <span class="n">errors</span><span class="o">=</span><span class="s1">&#39;coerce&#39;</span><span class="p">)</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">Masses</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
-<span class="c1"># Convert from keV to MeV.</span>
-<span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">]</span> <span class="o">/=</span> <span class="mi">1000</span>
+# Extrapolated values are indicated by &#39;#&#39; in place of the decimal place, so
+# the Ebinding column won&#39;t be numeric. Coerce to float and drop these entries.
+Masses[&#39;Ebinding&#39;] = pd.to_numeric(Masses[&#39;Ebinding&#39;], errors=&#39;coerce&#39;)
+Masses = Masses.dropna()
+# Convert from keV to MeV.
+Masses[&#39;Ebinding&#39;] /= 1000
 
-<span class="c1"># Group the DataFrame by nucleon number, A.</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">Masses</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
-<span class="c1"># Find the rows of the grouped DataFrame with the maximum binding energy.</span>
-<span class="n">Masses</span> <span class="o">=</span> <span class="n">Masses</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">t</span><span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">Ebinding</span><span class="o">==</span><span class="n">t</span><span class="o">.</span><span class="n">Ebinding</span><span class="o">.</span><span class="n">max</span><span class="p">()])</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span>
-<span class="n">Z</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Z&#39;</span><span class="p">]</span>
-<span class="n">N</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">]</span>
-<span class="n">Element</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Element&#39;</span><span class="p">]</span>
-<span class="n">Energies</span> <span class="o">=</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">]</span>
+# Group the DataFrame by nucleon number, A.
+Masses = Masses.groupby(&#39;A&#39;)
+# Find the rows of the grouped DataFrame with the maximum binding energy.
+Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])
+A = Masses[&#39;A&#39;]
+Z = Masses[&#39;Z&#39;]
+N = Masses[&#39;N&#39;]
+Element = Masses[&#39;Element&#39;]
+Energies = Masses[&#39;Ebinding&#39;]
 
-<span class="c1"># Now we set up the design matrix X</span>
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">A</span><span class="p">),</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="o">**</span><span class="p">(</span><span class="mf">2.0</span><span class="o">/</span><span class="mf">3.0</span><span class="p">)</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="o">/</span><span class="mf">3.0</span><span class="p">)</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">)</span>
-<span class="c1"># Then nice printout using pandas</span>
-<span class="n">DesignMatrix</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="n">DesignMatrix</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">A</span>
-<span class="n">DesignMatrix</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;A^(2/3)&#39;</span><span class="p">,</span> <span class="s1">&#39;A^(-1/3)&#39;</span><span class="p">,</span> <span class="s1">&#39;1/A&#39;</span><span class="p">]</span>
-<span class="n">display</span><span class="p">(</span><span class="n">DesignMatrix</span><span class="p">)</span>
+# Now we set up the design matrix X
+X = np.zeros((len(A),5))
+X[:,0] = 1
+X[:,1] = A
+X[:,2] = A**(2.0/3.0)
+X[:,3] = A**(-1.0/3.0)
+X[:,4] = A**(-1.0)
+# Then nice printout using pandas
+DesignMatrix = pd.DataFrame(X)
+DesignMatrix.index = A
+DesignMatrix.columns = [&#39;1&#39;, &#39;A&#39;, &#39;A^(2/3)&#39;, &#39;A^(-1/3)&#39;, &#39;1/A&#39;]
+display(DesignMatrix)
 </pre></div>
 </div>
 </div>
@@ -2349,10 +2269,10 @@ allow for the usage of direct linear algebra methods such as <strong>LU</strong>
 write</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># matrix inversion to find beta</span>
-<span class="n">beta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Energies</span><span class="p">)</span>
-<span class="c1"># and then make the prediction</span>
-<span class="n">ytilde</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># matrix inversion to find beta
+beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
+# and then make the prediction
+ytilde = X @ beta
 </pre></div>
 </div>
 </div>
@@ -2360,8 +2280,8 @@ write</p>
 <p>Alternatively, you can use the least squares functionality in <strong>Numpy</strong> as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Energies</span><span class="p">,</span> <span class="n">rcond</span> <span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">ytildenp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">fit</span><span class="p">,</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
+ytildenp = np.dot(fit,X.T)
 </pre></div>
 </div>
 </div>
@@ -2369,18 +2289,18 @@ write</p>
 <p>And finally we plot our fit with and compare with data</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Eapprox&#39;</span><span class="p">]</span>  <span class="o">=</span> <span class="n">ytilde</span>
-<span class="c1"># Generate a plot comparing the experimental with the fitted values values.</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$A = N + Z$&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$E_\mathrm</span><span class="si">{bind}</span><span class="s1">\,/\mathrm</span><span class="si">{MeV}</span><span class="s1">$&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">],</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Ebinding&#39;</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ame2016&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">],</span> <span class="n">Masses</span><span class="p">[</span><span class="s1">&#39;Eapprox&#39;</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;m&#39;</span><span class="p">,</span>
-            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Fit&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">save_fig</span><span class="p">(</span><span class="s2">&quot;Masses2016OLS&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Masses[&#39;Eapprox&#39;]  = ytilde
+# Generate a plot comparing the experimental with the fitted values values.
+fig, ax = plt.subplots()
+ax.set_xlabel(r&#39;$A = N + Z$&#39;)
+ax.set_ylabel(r&#39;$E_\mathrm{bind}\,/\mathrm{MeV}$&#39;)
+ax.plot(Masses[&#39;A&#39;], Masses[&#39;Ebinding&#39;], alpha=0.7, lw=2,
+            label=&#39;Ame2016&#39;)
+ax.plot(Masses[&#39;A&#39;], Masses[&#39;Eapprox&#39;], alpha=0.7, lw=2, c=&#39;m&#39;,
+            label=&#39;Fit&#39;)
+ax.legend()
+save_fig(&quot;Masses2016OLS&quot;)
+plt.show()
 </pre></div>
 </div>
 </div>
@@ -2392,8 +2312,8 @@ write</p>
 Since we are not using <strong>Scikit-Learn</strong> here we can define our own <span class="math notranslate nohighlight">\(R2\)</span> function as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">R2</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span> <span class="n">y_model</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span> <span class="o">-</span> <span class="n">y_model</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_data</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def R2(y_data, y_model):
+    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
 </pre></div>
 </div>
 </div>
@@ -2401,7 +2321,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 <p>and we would be using it as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">R2</span><span class="p">(</span><span class="n">Energies</span><span class="p">,</span><span class="n">ytilde</span><span class="p">))</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>print(R2(Energies,ytilde))
 </pre></div>
 </div>
 </div>
@@ -2409,11 +2329,11 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 <p>We can easily add our <strong>MSE</strong> score as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">y_model</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def MSE(y_data,y_model):
+    n = np.size(y_model)
+    return np.sum((y_data-y_model)**2)/n
 
-<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">Energies</span><span class="p">,</span><span class="n">ytilde</span><span class="p">))</span>
+print(MSE(Energies,ytilde))
 </pre></div>
 </div>
 </div>
@@ -2421,9 +2341,9 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 <p>and finally the relative error as</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">RelativeError</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">abs</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">/</span><span class="n">y_data</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">RelativeError</span><span class="p">(</span><span class="n">Energies</span><span class="p">,</span> <span class="n">ytilde</span><span class="p">))</span>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def RelativeError(y_data,y_model):
+    return abs((y_data-y_model)/y_data)
+print(RelativeError(Energies, ytilde))
 </pre></div>
 </div>
 </div>
@@ -2657,7 +2577,7 @@ Singular Value Decomposition (SVD) method discussed next week.</p>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-is-generative-modeling">What Is Generative Modeling?</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster">Example of generative modeling, taken from Generative Deep Learning by David Foster</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generative-versus-discriminative-modeling">Generative Versus Discriminative Modeling</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster">Example of discriminative modeling, taken from Generative Deeep Learning by David Foster</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster">Example of discriminative modeling, taken from Generative Deep Learning by David Foster</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#discriminative-modeling">Discriminative Modeling</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#a-frequentist-approach-to-data-analysis">A Frequentist approach to data analysis</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-is-a-good-model">What is a good model?</a></li>
diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
index a594ee228..f7aa1b4cc 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3b47d5e6",
+   "id": "0b9e53e2",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f010738",
+   "id": "d69d6c55",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9821466c",
+   "id": "ed7a2caa",
    "metadata": {
     "editable": true
    },
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d635526f",
+   "id": "1a7fa793",
    "metadata": {
     "editable": true
    },
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46d1ebd2",
+   "id": "42eed58e",
    "metadata": {
     "editable": true
    },
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "722a1812",
+   "id": "19b78895",
    "metadata": {
     "editable": true
    },
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8724a41b",
+   "id": "920c8b78",
    "metadata": {
     "editable": true
    },
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a74065a9",
+   "id": "66791ac8",
    "metadata": {
     "editable": true
    },
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb3519d1",
+   "id": "d006c6f2",
    "metadata": {
     "editable": true
    },
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f46e4ab5",
+   "id": "d2b59d04",
    "metadata": {
     "editable": true
    },
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18134f7b",
+   "id": "2b1dfff8",
    "metadata": {
     "editable": true
    },
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e4b2cb4",
+   "id": "39ce46e9",
    "metadata": {
     "editable": true
    },
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dc04ba4",
+   "id": "cc1139fe",
    "metadata": {
     "editable": true
    },
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9d7446a",
+   "id": "f56c0bb5",
    "metadata": {
     "editable": true
    },
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ac913df",
+   "id": "4b601e8d",
    "metadata": {
     "editable": true
    },
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe4f142d",
+   "id": "2518a99e",
    "metadata": {
     "editable": true
    },
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82a26310",
+   "id": "a0e1e9ae",
    "metadata": {
     "editable": true
    },
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d97a91f",
+   "id": "8af343cb",
    "metadata": {
     "editable": true
    },
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0af85fa1",
+   "id": "c0cfedb4",
    "metadata": {
     "editable": true
    },
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca1c0d4b",
+   "id": "4561f563",
    "metadata": {
     "editable": true
    },
@@ -398,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e3a4194",
+   "id": "b94446df",
    "metadata": {
     "editable": true
    },
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44fe97f3",
+   "id": "2da0b611",
    "metadata": {
     "editable": true
    },
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fd61872",
+   "id": "cb6368d0",
    "metadata": {
     "editable": true
    },
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50148149",
+   "id": "cd5643ee",
    "metadata": {
     "editable": true
    },
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12bc5144",
+   "id": "dad2568b",
    "metadata": {
     "editable": true
    },
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25428d4e",
+   "id": "ccf34c1f",
    "metadata": {
     "editable": true
    },
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2efac50e",
+   "id": "d9a80c4f",
    "metadata": {
     "editable": true
    },
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfd0c6a0",
+   "id": "bf1454b7",
    "metadata": {
     "editable": true
    },
@@ -572,7 +572,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f92f8d35",
+   "id": "4a01a6f0",
    "metadata": {
     "editable": true
    },
@@ -588,7 +588,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "174d6e96",
+   "id": "2bf27180",
    "metadata": {
     "editable": true
    },
@@ -604,12 +604,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fcac42b",
+   "id": "15ddfa02",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Example of discriminative modeling, [taken from Generative Deeep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n",
+    "## Example of discriminative modeling, [taken from Generative Deep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n",
     "\n",
     "<!-- dom:FIGURE: [figures/standarddeeplearning.png, width=900 frac=1.0] -->\n",
     "<!-- begin figure -->\n",
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8942d9a",
+   "id": "371a54c3",
    "metadata": {
     "editable": true
    },
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a28ea0e4",
+   "id": "f9ec1f0a",
    "metadata": {
     "editable": true
    },
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3260f00",
+   "id": "19f2aef4",
    "metadata": {
     "editable": true
    },
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ac16500",
+   "id": "e08bd4e6",
    "metadata": {
     "editable": true
    },
@@ -736,7 +736,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "825ca09e",
+   "id": "729c3f46",
    "metadata": {
     "editable": true
    },
@@ -775,7 +775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40bd0bf2",
+   "id": "6825a222",
    "metadata": {
     "editable": true
    },
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a356fdf0",
+   "id": "bad98b2e",
    "metadata": {
     "editable": true
    },
@@ -845,7 +845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9730b8a",
+   "id": "1daa92e1",
    "metadata": {
     "editable": true
    },
@@ -869,7 +869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f967a6a",
+   "id": "1c840067",
    "metadata": {
     "editable": true
    },
@@ -896,7 +896,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76e167a3",
+   "id": "f33d6379",
    "metadata": {
     "editable": true
    },
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94a3c420",
+   "id": "e3a03646",
    "metadata": {
     "editable": true
    },
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6ff6b6b",
+   "id": "fb6d2495",
    "metadata": {
     "editable": true
    },
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55dbb002",
+   "id": "3101a680",
    "metadata": {
     "editable": true
    },
@@ -951,13 +951,10 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "655878bd",
+   "id": "66625f81",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -966,7 +963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "914fa12c",
+   "id": "d8edb94a",
    "metadata": {
     "editable": true
    },
@@ -977,13 +974,10 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a0cfca17",
+   "id": "7d44772f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -994,7 +988,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "342c0109",
+   "id": "7c3a5d5e",
    "metadata": {
     "editable": true
    },
@@ -1006,13 +1000,10 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d09ad51a",
+   "id": "bd150398",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1023,7 +1014,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be9009bc",
+   "id": "62bb511e",
    "metadata": {
     "editable": true
    },
@@ -1035,13 +1026,10 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "19dfe7ab",
+   "id": "de9d1b88",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1052,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c78e90e",
+   "id": "575f4f67",
    "metadata": {
     "editable": true
    },
@@ -1069,13 +1057,10 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "48603a4c",
+   "id": "eadd4613",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1089,7 +1074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5cfa34",
+   "id": "43c2b238",
    "metadata": {
     "editable": true
    },
@@ -1101,13 +1086,10 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "1c252e6c",
+   "id": "f1153763",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1118,7 +1100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "375afb81",
+   "id": "b897cb85",
    "metadata": {
     "editable": true
    },
@@ -1129,13 +1111,10 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4c87de99",
+   "id": "3417f7b5",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1146,7 +1125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afff17c8",
+   "id": "421de263",
    "metadata": {
     "editable": true
    },
@@ -1157,13 +1136,10 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "d5827065",
+   "id": "e9055b4f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1174,7 +1150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83e054e8",
+   "id": "52301fe3",
    "metadata": {
     "editable": true
    },
@@ -1189,13 +1165,10 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "0cd4400a",
+   "id": "7f400b7f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1206,7 +1179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2868228",
+   "id": "35677f44",
    "metadata": {
     "editable": true
    },
@@ -1217,13 +1190,10 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6e7ef557",
+   "id": "16b1c27a",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1235,7 +1205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47391e12",
+   "id": "7566ab17",
    "metadata": {
     "editable": true
    },
@@ -1246,13 +1216,10 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a9457054",
+   "id": "b10affa1",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1264,7 +1231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a04c51eb",
+   "id": "c50f969e",
    "metadata": {
     "editable": true
    },
@@ -1275,13 +1242,10 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "43e1f145",
+   "id": "35f1236f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1294,7 +1258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e11ea608",
+   "id": "02cec13a",
    "metadata": {
     "editable": true
    },
@@ -1305,13 +1269,10 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "ed021bcf",
+   "id": "821bc62d",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1324,7 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cf97a5d",
+   "id": "5df31e24",
    "metadata": {
     "editable": true
    },
@@ -1335,13 +1296,10 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "0dd77598",
+   "id": "d821d3ff",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1354,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a27628a3",
+   "id": "a99c04f6",
    "metadata": {
     "editable": true
    },
@@ -1366,7 +1324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80585c1d",
+   "id": "fdb302f5",
    "metadata": {
     "editable": true
    },
@@ -1381,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74725f56",
+   "id": "4c91d693",
    "metadata": {
     "editable": true
    },
@@ -1391,7 +1349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd223a73",
+   "id": "316ccd01",
    "metadata": {
     "editable": true
    },
@@ -1403,7 +1361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40dfa247",
+   "id": "73cf195b",
    "metadata": {
     "editable": true
    },
@@ -1414,7 +1372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56e47db3",
+   "id": "c9d6f1ba",
    "metadata": {
     "editable": true
    },
@@ -1429,7 +1387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26251aec",
+   "id": "a5bbc329",
    "metadata": {
     "editable": true
    },
@@ -1444,13 +1402,10 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "dab44ca5",
+   "id": "74dd353c",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1474,13 +1429,10 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "58c875cb",
+   "id": "f07645f8",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1501,7 +1453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d735077",
+   "id": "245ee493",
    "metadata": {
     "editable": true
    },
@@ -1527,13 +1479,10 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "d60ac131",
+   "id": "e428bcdc",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1550,7 +1499,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06e4b620",
+   "id": "861269ad",
    "metadata": {
     "editable": true
    },
@@ -1564,13 +1513,10 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "2174392e",
+   "id": "c34c9894",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1580,7 +1526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72c17dd0",
+   "id": "0ec5724d",
    "metadata": {
     "editable": true
    },
@@ -1591,13 +1537,10 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "7612f9de",
+   "id": "83302e08",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1606,7 +1549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "242d4c4b",
+   "id": "886277ee",
    "metadata": {
     "editable": true
    },
@@ -1617,13 +1560,10 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "0171d1ad",
+   "id": "c1010af7",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1638,7 +1578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5232bfb",
+   "id": "f3c82384",
    "metadata": {
     "editable": true
    },
@@ -1650,13 +1590,10 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "cb2ab622",
+   "id": "70542372",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1677,7 +1614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1998bc9c",
+   "id": "a83eb591",
    "metadata": {
     "editable": true
    },
@@ -1688,13 +1625,10 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "e0ec9915",
+   "id": "2f42295f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1720,7 +1654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebe44ad1",
+   "id": "fb1b0336",
    "metadata": {
     "editable": true
    },
@@ -1731,13 +1665,10 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "6f6d21e6",
+   "id": "0438c751",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1749,7 +1680,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f97c531",
+   "id": "f6d3d4c8",
    "metadata": {
     "editable": true
    },
@@ -1766,7 +1697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36a57ec4",
+   "id": "d552a0d0",
    "metadata": {
     "editable": true
    },
@@ -1778,7 +1709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca69e9e9",
+   "id": "b96bd9e5",
    "metadata": {
     "editable": true
    },
@@ -1809,7 +1740,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4bc6d8a",
+   "id": "41318c5c",
    "metadata": {
     "editable": true
    },
@@ -1821,7 +1752,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd9f61d0",
+   "id": "789c6fe4",
    "metadata": {
     "editable": true
    },
@@ -1849,13 +1780,10 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "81d8ecc3",
+   "id": "8c7472f2",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1882,7 +1810,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9f8b0e5",
+   "id": "25cef2d3",
    "metadata": {
     "editable": true
    },
@@ -1899,7 +1827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37a6973d",
+   "id": "58a04438",
    "metadata": {
     "editable": true
    },
@@ -1911,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b1616dd",
+   "id": "55191297",
    "metadata": {
     "editable": true
    },
@@ -1932,7 +1860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02f59dce",
+   "id": "2951bf64",
    "metadata": {
     "editable": true
    },
@@ -1945,7 +1873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37a3928f",
+   "id": "1109ceeb",
    "metadata": {
     "editable": true
    },
@@ -1976,7 +1904,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ced1c75",
+   "id": "7eeca264",
    "metadata": {
     "editable": true
    },
@@ -1988,7 +1916,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b2c7527",
+   "id": "fd8b7402",
    "metadata": {
     "editable": true
    },
@@ -2006,13 +1934,10 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "dcc4e935",
+   "id": "0de5719f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2036,7 +1961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef0a62d7",
+   "id": "ce194339",
    "metadata": {
     "editable": true
    },
@@ -2058,13 +1983,10 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "e2a9bb21",
+   "id": "cde68f17",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2099,7 +2021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6eca083",
+   "id": "f48a129c",
    "metadata": {
     "editable": true
    },
@@ -2110,7 +2032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c940fd70",
+   "id": "c337129d",
    "metadata": {
     "editable": true
    },
@@ -2123,7 +2045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b1317ef",
+   "id": "d6462e3c",
    "metadata": {
     "editable": true
    },
@@ -2144,7 +2066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3895d32d",
+   "id": "c2a5a5fd",
    "metadata": {
     "editable": true
    },
@@ -2156,7 +2078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88937a4",
+   "id": "d19d16f8",
    "metadata": {
     "editable": true
    },
@@ -2166,7 +2088,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d62abca",
+   "id": "7739ec09",
    "metadata": {
     "editable": true
    },
@@ -2178,7 +2100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "903ee760",
+   "id": "7f433018",
    "metadata": {
     "editable": true
    },
@@ -2190,7 +2112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef89ae2c",
+   "id": "20361838",
    "metadata": {
     "editable": true
    },
@@ -2202,7 +2124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "924ac782",
+   "id": "02d95914",
    "metadata": {
     "editable": true
    },
@@ -2213,7 +2135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50053e83",
+   "id": "524bb980",
    "metadata": {
     "editable": true
    },
@@ -2225,7 +2147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc7df77b",
+   "id": "6cb0b956",
    "metadata": {
     "editable": true
    },
@@ -2247,7 +2169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fe669ae",
+   "id": "142be577",
    "metadata": {
     "editable": true
    },
@@ -2259,7 +2181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28ceb188",
+   "id": "4e765569",
    "metadata": {
     "editable": true
    },
@@ -2272,7 +2194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d89d075b",
+   "id": "f79ebfdf",
    "metadata": {
     "editable": true
    },
@@ -2289,7 +2211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3816234",
+   "id": "eb09c88a",
    "metadata": {
     "editable": true
    },
@@ -2301,7 +2223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ddf946bd",
+   "id": "bf69319c",
    "metadata": {
     "editable": true
    },
@@ -2311,7 +2233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec48e2fd",
+   "id": "eec8df19",
    "metadata": {
     "editable": true
    },
@@ -2323,7 +2245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "462f1772",
+   "id": "313f9634",
    "metadata": {
     "editable": true
    },
@@ -2333,7 +2255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dabcb01d",
+   "id": "83b87f63",
    "metadata": {
     "editable": true
    },
@@ -2345,7 +2267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be2a6bd1",
+   "id": "be859efd",
    "metadata": {
     "editable": true
    },
@@ -2355,7 +2277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb37d8f8",
+   "id": "a976e227",
    "metadata": {
     "editable": true
    },
@@ -2367,7 +2289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de325e18",
+   "id": "9f1dc803",
    "metadata": {
     "editable": true
    },
@@ -2383,7 +2305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c68a0c1",
+   "id": "4fe2c4e9",
    "metadata": {
     "editable": true
    },
@@ -2395,7 +2317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3492a4b",
+   "id": "bd8e264d",
    "metadata": {
     "editable": true
    },
@@ -2406,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "207b9ead",
+   "id": "9a0e9ac7",
    "metadata": {
     "editable": true
    },
@@ -2418,7 +2340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d74c7ddc",
+   "id": "9b20bc14",
    "metadata": {
     "editable": true
    },
@@ -2432,7 +2354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a02ea08c",
+   "id": "5fae8c2f",
    "metadata": {
     "editable": true
    },
@@ -2444,7 +2366,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9863119",
+   "id": "c955d871",
    "metadata": {
     "editable": true
    },
@@ -2469,7 +2391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92ca8aa6",
+   "id": "10feca33",
    "metadata": {
     "editable": true
    },
@@ -2485,14 +2407,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "32b99126",
+   "execution_count": 27,
+   "id": "cd57c0cd",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2533,7 +2452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "185af862",
+   "id": "ed4e2094",
    "metadata": {
     "editable": true
    },
@@ -2550,13 +2469,10 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "2ae9db3d",
+   "id": "309536df",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2574,7 +2490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ade787b",
+   "id": "27597f0a",
    "metadata": {
     "editable": true
    },
@@ -2587,14 +2503,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "e0d18716",
+   "execution_count": 29,
+   "id": "1c60fe5f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2620,7 +2533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd819c63",
+   "id": "36972bcb",
    "metadata": {
     "editable": true
    },
@@ -2639,38 +2552,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "fc0346bc",
+   "execution_count": 30,
+   "id": "85ffeaf7",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            N    Z    A Element  Ebinding\n",
-      "A                                        \n",
-      "4   0       1    3    4      Li  1.153760\n",
-      "5   2       3    2    5      He  5.512132\n",
-      "6   7       3    3    6      Li  5.332331\n",
-      "7   12      4    3    7      Li  5.606439\n",
-      "8   17      4    4    8      Be  7.062435\n",
-      "...       ...  ...  ...     ...       ...\n",
-      "264 3297  156  108  264      Hs  7.298375\n",
-      "265 3303  157  108  265      Hs  7.296247\n",
-      "266 3310  158  108  266      Hs  7.298273\n",
-      "269 3331  159  110  269      Ds  7.250154\n",
-      "270 3337  160  110  270      Ds  7.253775\n",
-      "\n",
-      "[264 rows x 5 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "A = Masses['A']\n",
     "Z = Masses['Z']\n",
@@ -2682,7 +2570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f70ca63",
+   "id": "799627e9",
    "metadata": {
     "editable": true
    },
@@ -2693,14 +2581,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "d56956d6",
+   "execution_count": 31,
+   "id": "365fbba9",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2715,7 +2600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b3df36c",
+   "id": "0d3c5663",
    "metadata": {
     "editable": true
    },
@@ -2725,14 +2610,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "d10ecf29",
+   "execution_count": 32,
+   "id": "60ba302b",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2742,7 +2624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2289c78e",
+   "id": "af13881a",
    "metadata": {
     "editable": true
    },
@@ -2753,38 +2635,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "286afac2",
+   "execution_count": 33,
+   "id": "a5cba204",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 0.02\n",
-      "Variance score: 0.95\n",
-      "Mean absolute error: 0.05\n",
-      "[ 0.00000000e+00 -2.96611194e-02  2.01719003e-01  1.08078025e+01\n",
-      " -4.03097597e+01] 5.294399745619595\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The mean squared error                               \n",
     "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n",
@@ -2810,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fa56ca8",
+   "id": "db2d6c19",
    "metadata": {
     "editable": true
    },
@@ -2823,95 +2680,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "76f0ddd2",
+   "execution_count": 34,
+   "id": "66c27da6",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 1.73\n",
-      "Mean squared error: 19.48\n",
-      "Mean squared error: 12.11\n",
-      "Mean squared error: 23.42\n",
-      "Mean squared error: 0.23\n",
-      "Mean squared error: 0.18\n",
-      "Mean squared error: 0.29\n",
-      "Mean squared error: 0.20\n",
-      "Mean squared error: 0.26\n",
-      "Mean squared error: 4.07\n",
-      "Mean squared error: 2.12\n",
-      "Mean squared error: 7.54\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 15.78\n",
-      "Mean squared error: 122.02\n",
-      "Mean squared error: 51.22\n",
-      "Mean squared error: 152.55\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyQAAANbCAYAAACzflsQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABl90lEQVR4nO3dd3hU5brG4WdCEkIIoZPQCZ3QFRSVJsWGW0FA8QjSqwIiCgiIVBXpKFgowgYUhCAiitQtiNJBEEIvoScQQkKA1JnzRyQ6JuACMnwpv/u6ch2z1pqZd+bMnvDM+65v2RwOh0MAAAAAYICb6QIAAAAAZF0EEgAAAADGEEgAAAAAGEMgAQAAAGAMgQQAAACAMQQSAAAAAMYQSAAAAAAYQyABAAAAYAyBBAAAAIAxBBIAGdKgQYNUoUKF2/40atTonh5j6dKlqlChgs6cOePS29yr06dPq2LFinrooYcUExNz3x4XAIC0YHM4HA7TRQDAnTp16pQuX76c/Pv06dMVHBysTz75JHmbp6enAgMD7/oxLl++rFOnTikwMFCenp4uu829mjRpklatWqUzZ85o1KhRatGixX15XAAA0gKBBECmMGjQIG3btk3r1683Xcp9Zbfb1ahRIzVv3lzBwcGKjIzUokWLTJcFAIBljGwByNS2bt2qChUqaOHChXr88cf16KOPatOmTZKkxYsX64UXXlCNGjVUrVo1Pf/88/rxxx+Tb/vP8atBgwapQ4cOCgoK0pNPPqkqVaroueee04YNG+7pNpK0e/duvfLKK6pRo4YaNmyouXPnqkOHDho0aNBtn9+mTZt0/vx5Pf7443ruuef0+++/6+DBgymOCw8P1+DBg/Xoo4+qZs2aeuWVV7Rz587k/fHx8Zo2bZqaNGmiatWqqVmzZgoKCkre365dO7Vr1y7V13br1q3Jzz0wMFCLFy9W3bp1Vb9+fR05ckSJiYn64osv9Oyzz6patWqqUaOG2rRpo82bNzvd3759+9SlSxc9+OCDqlOnjvr166fz588rISFBdevWVf/+/VM8r6efflrvvPPObV8jAED6RiABkCVMmjRJAwcO1MCBA1WjRg0tWLBAw4YNU+PGjfX5559r3Lhx8vDw0Ntvv61z587d8n727dunWbNmqU+fPpo2bZrc3d3Vp08fRUZG3vVtjh07pg4dOkiSJk6cqN69e+uLL75wCgy3EhQUpICAAFWvXl1NmzaVr6+vvv76a6djrl+/rjZt2ui3335T//799cknnyhnzpzq0qWLjh07JkkaOHCgvvjiC7Vq1Uqff/65GjRooMGDB2vZsmX/WsPfJSYm6rPPPtPo0aP1xhtvqGzZsho/frymTZuml156STNnztTIkSMVERGhvn376vr165KkgwcP6uWXX9aNGzf04YcfauTIkQoODlanTp3kcDjUvHlzrV27VtHR0cmPtWfPHh0/flwvvPDCHdUIAEhf3E0XAAD3Q5s2bfTUU08l/3769Gl16tRJr732WvK2YsWK6YUXXtCuXbtUpEiRVO/n6tWrWrp0qUqUKCFJ8vb2Vtu2bbVlyxY9+eSTd3Wbzz//XD4+Ppo5c6Zy5MghSSpdurTatGlz2+d05coVrV+/Xr1795YkZc+eXc2aNdPy5cs1YMAA5cyZU5L07bff6vTp01q2bJkqVqwoSapVq5aaN2+u7du3y26364cfftCQIUP06quvSpIeeeQRnTt3Tlu3blXz5s1vW8c/9ejRQw0bNkz+PSwsTP369XPqsHh5eal37946dOiQatasqenTpyt37tyaPXu2smfPLkny9/fXG2+8oUOHDqlly5aaMWOGVq1apZYtWyY/rxIlSqhWrVp3VB8AIH0hkADIEipUqOD0+81RqKtXr+rkyZM6efJk8ghRfHz8Le8nX758ycFCSvpHsyTduHHjrm+zZcsWNWjQIDmMSFLNmjVVtGjR2z6n5cuXKyEhQY0aNVJUVJQk6cknn9TXX3+t77//PjnQ7NixQ8WKFUsOI1JSeFm5cqUkJXdUmjZt6nT/kydPvu3j30r58uWdfp8wYYKkpBP+Q0JCdOLEieRzfW6+1jt37lSDBg2Sw4gkVatWzemcoAcffFDfffedWrZsqbi4OP34449q3769bDbbXdUJAEgfCCQAsoT8+fM7/X7q1CkNGzZMW7Zskbu7u0qXLp0cWm631sffQ4Ok5H8M2+32u77N5cuXU9QnSQULFrzlfUpJ52zY7XY1a9Ysxb6FCxcmB5IrV66kev83XblyRVLK1+hu/fN+/vjjD40YMUJ//PGHvLy8VLZs2eSwdfO1/rcaJalVq1YaPHiwzp07pz179igqKooVxQAgEyCQAMhy7Ha7unXrJg8PD33zzTcKDAyUu7u7jh49quXLl9/3evz9/RUeHp5ie3h4uAICAlK9TXBwsA4cOKDXX39dDz30kNO+9evXa86cOdqzZ4+qV6+uXLlypXpdlN27d8vHx0e+vr6SkoLRze6NJB0/flyXL19OHolKTEx0uv3N8z9uJzo6Wl26dFGFChW0YsUKlSlTRm5ubtqwYYNWrVqVfFyuXLmclnG+acOGDapYsaL8/Pz01FNPafTo0Vq1apV2796tRx555JajdQCAjIOT2gFkORERETpx4oRatWqlatWqyd096buZjRs3Srp9t8MVateurY0bNyo2NjZ524EDB257ccUlS5bI09NTHTp00MMPP+z007lzZ2XLlk0LFy6UlHS+yOnTp3Xo0KHk28fFxal379765ptv9OCDD0qS1q5d6/QYkyZN0qhRoyRJPj4+unDhgtP+Xbt2/etzO378uK5cuaJXX31V5cqVk5tb0p+df77WtWrV0i+//KK4uLjk2x46dEjdunXTH3/8ISnp3JtnnnlGK1as0C+//EJ3BAAyCTokALKc/Pnzq2jRolqwYIH8/f3l6+urTZs2ae7cuZJufz6IK/To0UM//vijunTpok6dOikqKkpTpkyRzWZL9fyIuLg4/fDDD2rQoIFy5cqVYn+hQoX02GOP6ccff9Q777yjF154QfPmzVPPnj3Vt29f5cuXTwsWLFBMTIzatWunEiVK6KmnntL48eMVExOjypUra9OmTVqzZk3yeSSPP/641q9frzFjxqhJkybauXOnpRW4AgIC5OPjo88++0zu7u5yd3fXqlWrtGTJEkl/vda9evXSSy+9pK5du6p9+/aKi4vTlClTVLlyZdWvXz/5/lq1aqWXXnpJPj4+euKJJ+7i1QYApDd0SABkSdOnT5efn58GDRqkN954Q7///rs+/fRTlS5dWjt27LivtZQsWVKzZs1SbGys+vTpo0mTJqlr164qWLBg8kpZf7d27VpduXJFzz777C3vs0WLFoqJidG3334rHx8fzZ8/XzVr1tSYMWPUt29fxcbGat68eckn248bN06vvvqq5s2bp+7du2vTpk2aPHly8spkLVu2VNeuXfXjjz+qa9eu2rVrl6ZMmfKvzy1XrlyaPn26HA6H+vbtqwEDBujcuXOaP3++cubMmfxaBwYGat68ebLb7erXr59GjhypGjVqaMaMGU5XvK9Ro4by5s2rZs2aycvL645eZwBA+sSV2gHAsM2bN8vDw8Np+drIyEg99thjGjBgQPJSvJD27t2r1q1bKygoSFWqVDFdDgAgDTCyBQCG7d+/X1OnTtWbb76pypUrKyIiQrNnz1auXLlu2wXJSrZu3aqtW7dq2bJlqlOnDmEEADIRAgkAGNapUyfFxcXp66+/1vnz5+Xt7a2HHnpIY8eOVb58+UyXly5EREToyy+/VNmyZfXBBx+YLgcAkIYY2QIAAABgDCe1AwAAADCGQAIAAADAGAIJAAAAAGMIJAAAAACMyVKrbD3h0cZ0Ccgi3CqVNV0CsgjblWjTJSCLSPRnxTfcH6u3DTNdwi3ZL5Q3XUKq3PwPmy7hntAhAQAAAGAMgQQAAACAMVlqZAsAAAC4W3bZTZeQqozeYcjo9QMAAADIwAgkAAAAAIxhZAsAAACwINGRPke2Mvo/6OmQAAAAADCGQAIAAADAmIze4QEAAADuC7scpkvIlOiQAAAAADCGQAIAAADAGEa2AAAAAAvS64URMzo6JAAAAACMIZAAAAAAMIaRLQAAAMCCRAerbLkCHRIAAAAAxhBIAAAAABjDyBYAAABgARdGdA06JAAAAACMIZAAAAAAMIaRLQAAAMCCREa2XIIOCQAAAABjCCQAAAAAjGFkCwAAALCAVbZcgw4JAAAAAGMIJAAAAACMYWQLAAAAsCDRwciWK9AhAQAAAGAMgQQAAACAMYxsAQAAABbYTReQSdEhAQAAAGAMgQQAAACAMYxsAQAAABYkcmFEl6BDAgAAAMAYAgkAAAAAYxjZAgAAACxIZGLLJeiQAAAAADCGQAIAAADAGEa2AAAAAAu4MKJr0CEBAAAAYAyBBAAAAIAxjGwBAAAAFiTKZrqETIkOCQAAAABjCCQAAAAAjGFkCwAAALDAzoURXYIOCQAAAABjCCQAAAAAjGFkCwAAALCAVbZcgw4JAAAAAGMIJAAAAACMYWQLAAAAsICRLdegQwIAAADAGAIJAAAAAGMY2QIAAAAssDsY2XIFOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAAA0ffp0tWvX7pb7hw4dqkaNGjlts9vtmjp1qurVq6fq1aurU6dOCgkJuaPHJZAAAAAAWdycOXM0derUW+5fu3atFi9enGL79OnTtXDhQo0ePVqLFi2SzWZT165dFRcXZ/mxCSQAAACABYlyS5c/9yI0NFRdunTRlClTFBAQkOoxYWFhevfdd/XQQw85bY+Li9Ps2bPVu3dvNWjQQBUrVtSkSZMUGhqqNWvWWK6BQAIAAABkUfv371fu3Lm1fPlyVa9ePcV+h8OhQYMG6fnnn08RSA4ePKhr166pTp06ydt8fX0VGBio7du3W66BQAIAAABkUY0aNdKECRNUvHjxVPfPmTNHFy9e1Jtvvpli34ULFyRJhQsXdtpeqFAhnT9/3nINrLIFAAAAWJBeL4zYuHHj2+5ft27dXd3vwYMH9cknn2jBggXy9PRMsf/GjRuSlGJf9uzZFRkZaflx6JAAAAAAcBIbG6u33npLPXv2VMWKFVM9xsvLS5JSnMAeGxurHDlyWH4sOiQAAABABna3HZDb2bNnj44cOaJPPvlE06ZNkyTFx8crISFBNWvW1IgRI1SqVClJSSe9lyhRIvm2YWFhtwwxqSGQAAAAABZkpQsjVqtWTatXr3baNm/ePK1evVrz5s1T/vz55enpKR8fH23dujU5kERFRSk4OFht27a1/FgEEgAAAABOvLy8VLJkSadtuXPnlru7u9P2tm3bavz48cqXL5+KFi2qcePGyd/fX02bNrX8WAQSAAAAAHelT58+SkhI0NChQxUTE6PatWtr1qxZqZ4Efys2h8PhcGGN6coTHm1Ml4Aswq1SWdMlIIuwXYk2XQKyiET/fKZLQBaxetsw0yXc0i8n0+ff93qljpou4Z6wyhYAAAAAYwgkAAAAAIzhHBIAAADAAjvf5bsEryoAAAAAYwgkAAAAAIxhZAsAAACwICtdGPF+okMCAAAAwBgCCQAAAABjGNkCAAAALEh08F2+K/CqAgAAADCGQAIAAADAGEa2AAAAAAvsrLLlEnRIAAAAABhDIAEAAABgDCNbAAAAgAWJfJfvEryqAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgavKoAAAAAjKFDAhUsll+f7/5Iw1tO0N6Nwake0/TVBnp7Vs9b3se4TtO1Zt5GV5WITKiAX259FvS6Rr7xlfbuOJG8/aH6FfRK98cVUN5PUVduaNPafZr78VrduB5nsFpkZAUK59GnqwdqZNdZ+mPL0eTtVeuUVbs3n1KpSkUUH5ugAztPaNb73+t8yCWD1SIjeqb5A3qudW0VLppXVyKuafPGQ/rvFz/r+rWkz61HG1TQK53qqXipAoq8cl2rV+zR11/+ooQEu+HKgfSBQJLFFSpRQB/88I588uS87XHbftylPnWHOm2z2Wzq92k3efvm0LaVu11ZJjKZQoXzaMxn7eXjm8Np+6ONKmnoxJe1d8dJvf/2ImVzd9PLXRtq7MxOeqPdF7In8scbd6ZQ0bwaPa+HfHI7v9cqPlBK7y/oqS1r9mlcn3nKnsNTbXo/ofFBfdSz6VhFRVwzVDEymtbtHlWnno20eP5v2r39hIoUz6f23RuqVJlCGvT6fNV+tKyGjX1Rq1f8rpmfrFPxUgXUqVcj5S/go8kf/GC6fNwhO8NFLkEgyaJsNpuavlpf3ca2tXR85KWrirx01Wlb895Pq3iloupXf1iKfUBqbDabmjxXU137P5Xq/rY9G+nU8Ysa2mOuEhISJUn7doVozo9v6onmD+inoB33s1xkYDabTU1a1VaXoc+nuv+l15ro9NFQvd9zjhwOhyRp/44TmrflPTVt/ZCCvvjf/SwXGZTNJrVp/5h++HanZk9fL0navf2EoiKv690PWqtcpcJq06GuDu0/q4mjv0/enzuPt17uWFefTVqtmJh4k08BSBeIeVlUQLUS6vNJZ62Zt1FjO0y749vn9cutDiNe1IrP1+jgtqP/fgNAUkB5P/Ue+h+tXb5b4wYvSbG/eOmC2vnrkeQwIkmRl6/p9PGLerh+hftZKjK4gEpF9PqY1lq7ZLvGvzE/xf5Dv4do2awNyWFEkiLConQ9OkaFSxa4n6UiA/POmV3rfvpD61ftc9p+9tRlSVKRonk1fsR3GjfiO6f98fGJcnNzUzZ3/hkGSHRIsqyLpy6pQ8U3dOnsZVWrH3jHt28//EXZE+2aM2yRC6pDZhV2PlKdnp2kS6FRqlYrIMX+yMvX5Vc0r9O2bO5uKlg4jzw8+biCdWFnI9S5/mhduhCpqnXKpti/8OM1KbZVe6SscuXJqZOHzt+PEpEJXIuO1fTxP6XY/ljDipKkk8cv6vzZiOTt3jmz64GHAtTqlUe0/qc/dC069r7VirSR6LCZLiFTMv4XPiEhQatXr9aOHTt07tw5xcXFKUeOHPL391etWrXUtGlTubsbLzPTuRpxTVfvckY6T0FfNWlbT0smrtC1yOtpXBkys+ioG4qOunHL/Wu+26WXuzVU6471tHrZTnlm91D73k3knTM7J7XjjkRHXld0pPXjc+fLqb5jX9LFcxFau2Sb6wpDphdYtZheevUx/frzQYUcv5i8PX/BXPr6h36SpPNnIzRvxgZTJQLpjtFe4alTp9SsWTMNHjxYBw8elJeXlwoWLCgPDw8dOHBA77zzjp577jmdO3fOZJn4h6c7N5LNzU3ffrzSdCnIZOZ9ul6LZm3Uq6831qINgzX7h366cS1Wm/93QLE3CCRwjXx+vvpg4WvKkz+XRnebrRjCL+5SlRolNHry/+nc2cuaOHq5076YG3Ea0Ou/GjFgkaIir+uTuV1UIoDxQEAy3CEZMWKEihUrpiVLlihXrlwp9kdFRalfv34aOXKkPvvsMwMVIjX1Wj6snWv2ciI70pw90a4vp6zW/E/Xq3CxvAq/eFXXrsboo9mddTXy1p0V4G6VqlBYI+Z0U46cnhr66mc6vPe06ZKQQTVsWllvDXtep09d0uA+C3Q1KsZp/7XoWP2+46Qkac/OEP13WR+98HIdTX5/hYFqgfTFaIdk586dGjBgQKphRJJ8fX319ttva/v27fe5MtxKgaL5VLZGgDYu2Wy6FGRCVR8spQcfLav4uASdOn5R167GyC2bmwLK++voATqlSFvVHy2nCUv7ymaT3m71sQ7sPGm6JGRQrds+okGjXtCBfWfUv9tcRYQnjUS7ZbOpQZNAlSnv73R89NUYnT9zWQX9fE2Ui3uQKLd0+ZPRGX0Gvr6+CgsLu+0x586dk5eX132qCP+mQu0ykqT9vx02XAkyo3pPVFHf95o7rTzzZPMHlMs3h35dn/pFO4G7UaZyUQ2f3UVh5yLU7/nJCjl8wXRJyKCatXhAXfs01cZ1wXqn93xdv/bXier2RIe69G6iLq83drpNQT9fFQ8oqONHQu93uUC6ZHRkq1WrVnrnnXfUp08fPfzwwypcuLA8PT0VFxen0NBQbdu2TePHj1erVq1MlpkleefKoRKBRXX+WKjTaFZAlRKKi4nT+eN8iCLt/bB4m55qWUtvjW6pVd/uVEA5f3Xq94R+XrlX+3eFmC4PmcgbH7VRNvdsWjDpJxUskkcFi+RJ3hd5OVrnQ8LNFYcMI2/+nOre70ldOHdF332zTWUrFnbaf/5M0snrbw17Xm8MflYb1uxX/oK59Ern+roaeV1BC5g2ACTDgaR3795yc3PT2LFjdf16ytWacubMqVdeeUV9+/Y1UF3WVrZmgMavG6ZxnT/Vmv/+tRJIXr/cir7CylpwjZCjYXrv9Xnq2PcJDZ/aVhHh0Vo4Y4MWzmQ1GqQd/xL5VbZqcUnSkM86pti/ZvE2Tez/1f0uCxnQQ4+Wk5eXh/yL5NGkGSnfS+NGfKfVK/boxo04vfTqY2r0ZBXFxMRr++ajmj1tva5E8Pc0o7E7Mv54VHpkc/z9qlCGxMfH68CBAwoNDdWNGzfk5eUlf39/VaxYUZ6enmn2OE94tEmz+wJux61SyuseAK5guxJtugRkEYn++UyXgCxi9bZhpku4pUVHa5suIVUvlc3Y51uniwt8eHh4qFq1aqbLAAAAAHCfpYtAAgAAAKR3mWFFq/SIVxUAAACAMQQSAAAAAMYwsgUAAABYkOiwmS4hU6JDAgAAAMAYAgkAAAAAYxjZAgAAACyw812+S/CqAgAAADCGQAIAAADAGEa2AAAAAAsSHXyX7wq8qgAAAACMIZAAAAAAMIaRLQAAAMACu7gwoivQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa/CqAgAAADCGQAIAAADAGEa2AAAAAAsS+S7fJXhVAQAAABhDIAEAAABgDCNbAAAAgAV2BxdGdAU6JAAAAACMIZAAAAAAMIaRLQAAAMACVtlyDV5VAAAAAMYQSAAAAAAYw8gWAAAAYIHdwXf5rsCrCgAAAMAYAgkAAAAAYxjZAgAAACxIFBdGdAU6JAAAAACMIZAAAAAAMIaRLQAAAMACVtlyDV5VAAAAAMYQSAAAAAAYw8gWAAAAYAGrbLkGHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQavKgAAAABjCCQAAAAAjGFkCwAAALAgkZEtl+BVBQAAAGAMgQQAAACAMYxsAQAAABbYuTCiS9AhAQAAAGAMgQQAAACAMYxsAQAAABawypZr8KoCAAAAMIZAAgAAAMAYRrYAAAAAC+wOVtlyBTokAAAAAIwhkAAAAAAwhpEtAAAAwIJEvst3CV5VAAAAAMYQSAAAAABo+vTpateundO29evXq2XLlqpZs6YaNWqksWPHKiYmJnm/3W7X1KlTVa9ePVWvXl2dOnVSSEjIHT0ugQQAAACwwO6wpcuftDBnzhxNnTrVaduOHTv0+uuv68knn9SyZcs0fPhwrVy5UiNGjEg+Zvr06Vq4cKFGjx6tRYsWyWazqWvXroqLi7P82AQSAAAAIIsKDQ1Vly5dNGXKFAUEBDjtW7hwoerUqaNu3bqpZMmSql+/vvr166fly5crLi5OcXFxmj17tnr37q0GDRqoYsWKmjRpkkJDQ7VmzRrLNRBIAAAAgCxq//79yp07t5YvX67q1as77evUqZMGDBiQ4jYJCQmKjo7WwYMHde3aNdWpUyd5n6+vrwIDA7V9+3bLNbDKFgAAAGCBPRN+l9+oUSM1atQo1X2BgYFOv8fFxenLL79U5cqVlS9fPu3YsUOSVLhwYafjChUqpPPnz1uugUACAAAAZGCNGze+7f5169bd82MkJCRowIABOnr0qBYsWCBJunHjhiTJ09PT6djs2bMrMjLS8n1nvpgHAAAAIM1ER0erR48eWrdunaZOnZo82uXl5SVJKU5gj42NVY4cOSzfPx0SAAAAwILENFrRKq2lRQfkVsLCwtS1a1edOXNGM2bMcDpf5OaoVlhYmEqUKOF0m4oVK1p+DDokAAAAAFKIjIxU+/btdfnyZX311VdOYUSSKlasKB8fH23dujV5W1RUlIKDg1WrVi3Lj0OHBAAAAEAKH3zwgU6fPq2ZM2cqX758unjxYvK+fPnyydPTU23bttX48eOVL18+FS1aVOPGjZO/v7+aNm1q+XEIJAAAAIAFaXURwozAbrfrxx9/VHx8vNq3b59i/7p161SsWDH16dNHCQkJGjp0qGJiYlS7dm3NmjUrxYnut2NzOByOtCw+PXvCo43pEpBFuFUqa7oEZBG2K9GmS0AWkeifz3QJyCJWbxtmuoRb6rv7ZdMlpGpKza9Nl3BPOIcEAAAAgDGMbAEAAAAW2B18l+8KvKoAAAAAjCGQAAAAADCGkS0AAADAgkRlnVW27ic6JAAAAACMIZAAAAAAMIaRLQAAAMCCrHRhxPuJDgkAAAAAYwgkAAAAAIxhZAsAAACwgAsjugavKgAAAABjCCQAAAAAjGFkCwAAALDAzoURXYIOCQAAAABjCCQAAAAAjGFkCwAAALAgkQsjugQdEgAAAADGEEgAAAAAGMPIFgAAAGABF0Z0DV5VAAAAAMYQSAAAAAAYk6VGthyJiaZLQBaRuO+Q6RIAIG2dOWu6AsA4O6tsuQQdEgAAAADGEEgAAAAAGJOlRrYAAACAu2UXI1uuQIcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYYHfwXb4r8KoCAAAAMIZAAgAAAMAYRrYAAAAAC1hlyzXokAAAAAAwhkACAAAAwBhGtgAAAAAL7GJkyxXokAAAAAAwhkACAAAAwBhGtgAAAAALWGXLNeiQAAAAADCGQAIAAADAGEa2AAAAAAsY2XINOiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALCAkS3XoEMCAAAAwBgCCQAAAABjGNkCAAAALLCLkS1XoEMCAAAAwBgCCQAAAABjGNkCAAAALGCVLdegQwIAAADAGAIJAAAAAGMY2QIAAAAsYGTLNeiQAAAAADCGQAIAAADAGEa2AAAAAAsY2XINOiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALDAwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABbYxciWK9AhAQAAAGAMgQQAAACAMYxsAQAAABZwYUTXoEMCAAAAwBgCCQAAAABjGNkCAAAALODCiK5BhwQAAACAMQQSAAAAAMYwsgUAAABYwCpbrkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjAKluuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABY4HCYriBzokMCAAAAQNOnT1e7du2cth04cEBt27ZVjRo11LBhQ82aNctpv91u19SpU1WvXj1Vr15dnTp1UkhIyB09LoEEAAAAyOLmzJmjqVOnOm2LiIhQx44dVapUKQUFBal3796aMmWKgoKCko+ZPn26Fi5cqNGjR2vRokWy2Wzq2rWr4uLiLD82I1sAAACABXZlvlW2QkNDNWTIEO3cuVMBAQFO+7755ht5enpq+PDhcnd3V5kyZRQSEqIZM2aoZcuWiouL0+zZs/X222+rQYMGkqRJkyapXr16WrNmjZo1a2apBjokAAAAQBa1f/9+5c6dW8uXL1f16tWd9u3YsUO1a9eWu/tfPYw6deroxIkTCg8P18GDB3Xt2jXVqVMneb+vr68CAwO1fft2yzXQIQEAAACyqEaNGqlRo0ap7rtw4YLKly/vtK1QoUKSpHPnzunChQuSpMKFC6c45vz585ZrIJAAAAAAFjjS6YURGzdufNv969atu6v7jYmJkaenp9O27NmzS5JiY2N148YNSUr1mMjISMuPw8gWAAAAgBS8vLxSnJweGxsrSfL29paXl5ckpXpMjhw5LD8OHRIAAAAgA7vbDsi/8ff3V1hYmNO2m7/7+fkpISEheVuJEiWcjqlYsaLlx6FDAgAAAFhgd9jS5Y+r1K5dWzt37lRiYmLyts2bNysgIED58+dXxYoV5ePjo61btybvj4qKUnBwsGrVqmX5cQgkAAAAAFJo2bKloqOjNWTIEB09elRLly7V3Llz1b17d0lJ5460bdtW48eP17p163Tw4EH169dP/v7+atq0qeXHYWQLAAAAQAr58+fXzJkzNWbMGLVo0UIFCxbUgAED1KJFi+Rj+vTpo4SEBA0dOlQxMTGqXbu2Zs2aleJE99uxORwOhyueQHrU1K216RIAAABwG2vsi02XcEvVV7xruoRU7Xl2lOkS7gkjWwAAAACMIZAAAAAAMIZzSAAAAAAL0uuFETM6OiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALDAzsiWS9AhAQAAAGAMHZIspNaTNdRxVBuVCCymyItRWvH5ai38cNktj3f3cFer/s+q6asNVbB4fl06c1nrv/pFCz9cpoT4hOTjnu7SWC/0babCpf0UduqSvv90lb6d+uN9eEZIr+70vfZ35R4orambx6hD+T4KDbnotO/R52vrlaGtVLxCEV2+cEVr52/Qwg+c34/IWlz1ufZE+4Zq1f8/KlrWX+Hnr2jtvA1aMDpIiQmJ9+FZISNw1XsPyIoIJFlE4CPlNfK7gdqw6Dd9+e5CValbUR1Hvyw3Nzd99f7SVG/Tc3IHNX21gRaMDtLh7UdV9oHSavdeaxUqWVATu3wqSXq2xxPqO72rFo5dpl1r9qjiw+XUffyr8sqZXV9/8O39fIpIJ+7mvXZT6WolNXrFO3L3SPnR9ECTanov6C1tWPSbZr2zQAFVS6jTmJeVp2BufdJ7lqueDtIxV32utejzjHpN7qiNizdrxoB5yl3AV68Of1EBVUtqRMtx9/MpIp1y1XsP6Z/DYbqCzIlAkkW0G9Zax34/qbHtP5Yk7Vj1u9w9sumlgc21ZOIKxcXEOR2fK6+Pnu3eVDMHLdDi8cslSbvX75MkdfuonWYNWqDIS1FqM7C5fv7zH4g3jylWroief/1pAkkWdafvNSnpm8PmvZ9S+5FtFHcj5X5JerLD4wo7dUkftvtYdrtdu9buVZ5CufXCG830ab85fHOdBbnic+3q5Wi1HdZaO1fv0aiXJibf9vDO45q1f5IeaFJNu9buvU/PEOmVq/6mAlkV55BkAR6e7qrWsLI2fbvVafvGJVvknSuHqtarmOI2OXN7a8Xna7R5+Q6n7WcOn5MkFS5dSJI0+OkxmjFwntMx8fEJ8sjukZZPARnE3bzXJOmhZ2qq7bDW+vr9pZo5aH7q953dXTHXYmW325O3RV6Kkmd2D3nnypF2TwIZgqs+1/L65ZZvPh9tXuF8zKkDZ3TlYpTqPPtgGj8TZDSu/JsKZFUEkiygcGk/eWb30Nk/P/huOnf0giSpWPkiKW5z4WSYPn5tZvKH5U11Wzys+LgEnTl8XpJ06uBZhZ26JCnpG6CnOzdS03b1tXz6T654Kkjn7ua9JkmHth9Tu4DX9NX7S5WYYE/1mO+m/aSi5Qqr9VvPKWdub1V6uJxe6NtMW3/YpasR0Wn7RJDuuepzLfrKNSXEJ8i/lPM/EH3y5FSuvDlTbEfW48q/qUj/HA5buvzJ6BjZygJy5skpSboWdcNp+/WrSb97+1r7drnuCw+rSbv6+nbqj4q+cs1pX+VHK2jyptGSpMM7jum7j1fea9nIgO72vRZ+7vK/3veen/frm3HfqdtH7dTto3aSpCO7juv9V6bcS8nIoFz5ufbzot/03GtP6eT+0/r1223KUyi3ek3uqIT4RHnlzJ6GzwIZ0f34mwpkNXRIsgA3tz+T8y3OxLLb//0MrXot6+idBX21d0OwZg1akGL/hRNh6t/wPb3/yhTlzO2tadvHKk+h3PdUNzKetHiv3Urfz7rpxbef1/zRS9T/8fc0vtM05S7gqw9WDlH2HJ53fb/ImFz5uTal5wytW/CL3pzRQ99enqPpO8cqeMshHdp+VDHXYtOkfmRc9+NvKpDVGO+QtGvXTjabtVbTf//7XxdXkznd/ObF29fbafvNuftrkddve/uW/Z5V14/aae/P+zWs+UeKj0u5PGH4+QiFn4+QJB3cekRzDk/VM10a/+uqSshc7vW9div5i+TTM10a6+sPvtXcYYskSXs3BOvQ9mOa8cdEPdWpkb6bxphgVuLKz7WYazGa2OVTTe/7pfxKFlDoyYuKuR6rpzo20p7j+9P4mSCjuR9/U5F+ZYbxqPTIeCB55JFH9PHHH6t06dKqVq2a6XIypXPHQpWYkKgiZf2dtt/8/VTwmVve9rWpndT89af186Lf9FH7j50+OHP4eOmR52rp4NajOnfsQvL288dDFR1xTQWL50/jZ4L07l7ea7dTqEQBubm5af+vB522n9x/WpGXolSycvG7KxgZlqs+1yTp4WYPKDrimvb/dkghf95PnoK+Klg8v47sPp7GzwQZjSvfe0BWZTyQ9OrVS97e3po6dao+//xzFStWzHRJmU58bLz2bjygui0eTl5uUJLqt6qjqxHROrjtaKq36/T+/6n5609ryaQV+rz/3BT77Yl29Z/ZU2vmbdTk7p8nby9fq4x88+fSsT0haf9kkK7d7Xvt35w7ekGJCYmqWq+Stv/0e/L2YuWLKHcBX104EXqvpSODcdXnmiQ92/0J+ebPpb6PDUne9sIbzWRPtGvLip1p+0SQ4bjyvQdkVcYDiSR16NBBmzZt0uTJkzV+/HjT5WRKX40J0tg17+rdRW/qpy/XK/DRCmr91nOaOWiB4mLi5J0rh0oGFtO5Y6GKvBSlMtVL6aUBz+vQ9qPa+M1vqvRwOaf7Cwk+o+tXb2jRR9/plaEtFRV+VbvX7lXR8kX06ntJ67Ov+vJ/hp4tTLrT95oVkZeitHTKD2r91nOSpJ1r9sqvZEG1HdZaoSEX9eOMda58SkinXPW5tuzjH/XhqnfVc1IHbV6+QzUaVdHL77ygrz/8VhdOhBl6tkhPXPXeQ/rHdRFdw+ZwpI9rToaGhio4OFiPP/64yx6jqVtrl913RvBY84f06vAXVaxCEYWfvazl03/SkokrJEnVGgRqwv9GaFzHaVo992e1H/GS2r7b6pb31f/x97R3Q7BsNpuadW+q53o+qSJl/XX1crQ2fbtVXw5dqOtRd3e+ADK+O3mv/dMT7Rvq7S9fU9uAXgoNuei0r0XfZ/Rs9yfkH1BIl89HaOeavfpyyNdcUCwLc8XnmiQ93uYx/d+QlvIPKKSwkIta/ukqffcJ5ynhL65670FaY19suoRbqrB0pOkSUnXohWGmS7gn6SaQ3A9ZPZAAAACkdwSSO5fRA0m6GNkCAAAA0jtW2XINrkMCAAAAwBgCCQAAAABjGNkCAAAArMgyZ17fX3RIAAAAABhDIAEAAABgDCNbAAAAgAWssuUadEgAAAAAGEMgAQAAAGAMI1sAAACABQ5W2XIJOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIALI7oGHRIAAAAAxhBIAAAAABjDyBYAAABgBSNbLkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjg4MKILkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjBKlsuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYwSpbLkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjCKluuQIcEAAAAgDEEEgAAAADGMLIFAAAAWMEqWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFY4uDCiK9AhAQAAAGAMgQQAAACAMYxsAQAAABY4WGXLJeiQAAAAADCGQAIAAADAGEa2AAAAACsY2XIJOiQAAAAAjCGQAAAAADCGkS0AAADACi6M6BJ0SAAAAAAYQyABAAAAYAwjWwAAAIAFNlbZcgk6JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIILI7oEHRIAAAAAxhBIAAAAABhDIAEAAACscKTTn3sQHx+vSZMmqWHDhqpZs6b+7//+T7t27Uref+DAAbVt21Y1atRQw4YNNWvWrHt7wFQQSAAAAIAs6tNPP1VQUJBGjx6tZcuWqXTp0uratatCQ0MVERGhjh07qlSpUgoKClLv3r01ZcoUBQUFpWkNnNQOAAAAZFHr1q3Ts88+q7p160qSBg0apMWLF+v333/XyZMn5enpqeHDh8vd3V1lypRRSEiIZsyYoZYtW6ZZDXRIAAAAACtMj2a5YGQrT548+t///qczZ84oMTFRixYtkqenpypVqqQdO3aodu3acnf/q4dRp04dnThxQuHh4ff2wH9DhwQAAADIooYMGaJ+/fqpcePGypYtm9zc3DRlyhSVKFFCFy5cUPny5Z2OL1SokCTp3Llzyp8/f5rUQCABAAAAMrDGjRvfdv+6detuue/YsWPy9fXVtGnT5Ofnp8WLF2vgwIGaP3++YmJi5Onp6XR89uzZJUmxsbH3XvifCCQAAACAFfc4HpXenD17Vm+//bbmzJmjWrVqSZKqVq2qo0eP6uOPP5aXl5fi4uKcbnMziHh7e6dZHQQSAAAAIAO7XQfkdvbu3av4+HhVrVrVaXv16tW1ceNGFSlSRGFhYU77bv7u5+d3d8WmgpPaAQAAgCyocOHCkqRDhw45bT98+LBKliyp2rVra+fOnUpMTEzet3nzZgUEBKTZ+SMSgQQAAACwxmFLnz93qVq1aqpVq5YGDhyoLVu26OTJk5o8ebI2b96sbt26qWXLloqOjtaQIUN09OhRLV26VHPnzlX37t3T8EVlZAsAAADIktzc3DR9+nRNnjxZ77zzjiIjI1W+fHnNmTNHNWrUkCTNnDlTY8aMUYsWLVSwYEENGDBALVq0SNM6bA6HI5OdnnNrTd1amy4BAAAAt7HGvth0CbdUatoE0yWk6uRr/U2XcE/okAAAAAAW2LLM1/j3F+eQAAAAADCGQAIAAADAGEa2AAAAACsY2XIJOiQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGPu6KT2ixcvatq0aTp9+rQKFCigSpUqqUqVKqpcubJy5MjhqhoBAAAAZFJ3FEgGDx6sTZs2qVy5cjpz5oy+//57ORwOubm5qXTp0qpSpYqqVq2qqlWrqmLFivLw8HBV3QAAAMB9xYURXeOOAsnu3bv19ttvq1OnTpKk69eva//+/frjjz/0xx9/aPv27fr2228lSZ6entq7d2/aVwwAAAAg07ijQJI9e3YFBgYm/+7t7a3atWurdu3ayduuXLmivXv3at++fWlXJQAAAIBM6Y4CSZMmTRQcHKw6derc8pg8efKofv36ql+//j0XB2RUbpxThfvEEZ9gugRkEW6+PqZLAMxz2ExXkCnd0SpbLVu21MqVK3X06FFX1QMAAAAgC7mjDsmLL74om82m1q1b66mnnlK9evVUuXJllSxZ0lX1AQAAAMjE7iiQjB49WgcOHND+/fu1cuVKffvtt7LZbMqZM6cCAwNVpUoVDRgwwFW1AgAAAOawypZL3FEgadWqVfJ/2+12HTt2TPv379e+ffsUHByshQsXEkgAAAAAWHZHgeTv3NzcVK5cOZUrV07NmzeXJDkcxEYAAAAA1t11IEmNzcbKAwAAAMik+O7dJe5olS0AAAAASEsEEgAAAADGpOnIFgAAAJBZ2RjZcgk6JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIKRLZegQwIAAADAGAIJAAAAAGMY2QIAAAAs4MKIrkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjhsJmuIFOiQwIAAADAGAIJAAAAAGMY2QIAAACsYJUtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACzgwoiuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMHIlkvQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGABF0Z0DTokAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjGGVLQAAAMAKVtlyCTokAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgadEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMCFEV2DDgkAAAAAYwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACxglS3XoEMCAAAAwBgCCQAAAABjGNkCAAAArGBkyyXokAAAAAAwhkACAAAAwBhGtgAAAAArGNlyCTokAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgadEgAAAAAGEMgAQAAAGAMgQQAAACwwpFOf+7RsmXL9Mwzz6hq1apq1qyZVq5cmbzvwIEDatu2rWrUqKGGDRtq1qxZ9/6A/0AgAQAAALKo7777ToMHD9ZLL72kFStW6JlnntGbb76p3bt3KyIiQh07dlSpUqUUFBSk3r17a8qUKQoKCkrTGjipHQAAAMiCHA6HpkyZovbt26t9+/aSpNdee027du3Stm3btG3bNnl6emr48OFyd3dXmTJlFBISohkzZqhly5ZpVgcdEgAAAMAK06NZaTyydfz4cZ09e1b/+c9/nLbPmjVL3bt3144dO1S7dm25u//Vw6hTp45OnDih8PDwu3/gf6BDAgAAAGRgjRs3vu3+devWpbr95MmTkqTr16+rc+fOCg4OVrFixdSzZ081atRIFy5cUPny5Z1uU6hQIUnSuXPnlD9//nsvXnRIAAAAgCwpOjpakjRw4EA9++yzmj17th577DH16tVLmzdvVkxMjDw9PZ1ukz17dklSbGxsmtVBhwQAAACwIL1eGPFWHZB/4+HhIUnq3LmzWrRoIUmqVKmSgoOD9eWXX8rLy0txcXFOt7kZRLy9ve+hYmd0SAAAAIAsyN/fX5JSjGWVLVtWZ86ckb+/v8LCwpz23fzdz88vzeogkAAAAABZUGBgoHLmzKk9e/Y4bT98+LBKlCih2rVra+fOnUpMTEzet3nzZgUEBKTZ+SMSgQQAAACwxvRqWmm8ypaXl5e6dOmiadOmacWKFTp16pQ+/fRT/frrr+rYsaNatmyp6OhoDRkyREePHtXSpUs1d+5cde/e/e4fNBWcQwIAAABkUb169VKOHDk0adIkhYaGqkyZMvr444/18MMPS5JmzpypMWPGqEWLFipYsKAGDBiQfL5JWrE5HI50enpO2mvq1tp0CUbVerKGOo5qoxKBxRR5MUorPl+thR8uu+Xx7h7uatX/WTV9taEKFs+vS2cua/1Xv2jhh8uUEJ+QfFzxCkXUdWw7VWsYqMT4RO3deECfvzVXF06E3fK+Mzu3HDlMl5BuvftVH5WtUVLtA/tbOr5sjVKa8vMwdao2QKGnLrm4uozH8bf/LUJ6pnMjPdfrCRUOKKQrYVHavGKn/jtiia5fvfGvty1XM0BTNo1Qx8A3FRrCe+2f3Hx9TJeQLlR7rLw++u6tW+6fN3a5FoxbkWJ7sbL+mrllZIrtp49cUNdHhqVpjRndT5e+MF3CLVV5e5LpElK1b1w/0yXcEzokWUTgI+U18ruB2rDoN3357kJVqVtRHUe/LDc3N331/tJUb9Nzcgc1fbWBFowO0uHtR1X2gdJq915rFSpZUBO7fCpJKlgsvyZvGq3Th87pg1emKHsOT3UY9bI+XPWuulXrr7iYuFTvG1lTozaPqu7ztXQh5KKl40tXLa5RS9+UuwcfVfh3rfs/q06jXtLiiSu0e/1+FSnjp/bDW6tU5eIa9PT7t71t6WolNOq7t3mv4V8d3XNKbzz5QYrt7Qc3V/mapfTz0m2p3q5M1eKSpAHPj1dcTHzy9ti//TfSv/S6ylZGxydvFtFuWGsd+/2kxrb/WJK0Y9XvcvfIppcGNteSiStSBIdceX30bPemmjlogRaPXy5J2r1+nySp20ftNGvQAkVeitKrI17U9as3NLDpSMXeSLqPCyfCNPK7gSpfq7T2bTp4H58l0rN8/nnUa1xbXTzz71d2dffIpud7NtWr77ZU3A3+WOPf2Ww2tRnwvH6YsU6zhy6SlPSZFXU5Wu9+3VflHgjQkV0nUtzO3SObnn/tSbUf3lpxN/gCBf/uenSMDu50fi/Vebq6ajaopNEdP9PZY6lPB5SuUkwXQi5p76+H70eZQIbCSe1ZgIenu6o1rKxN32512r5xyRZ558qhqvUqprhNztzeWvH5Gm1evsNp+5nD5yRJhUsnXaWzbouH9dPs9clhRJIO7zyuNsW6E0bgpN/0ztq5bp92/xz8r8fWfrK6XnmnhRZ+tFyz3l10H6pDRuftm0Prvtqk9Qt/c9p+9sh5SVKRMqkvT/nQ0zXVdugL+vrDZZo5+GuX14nMx9PLQ70+eFlbV+/Vpu933fK4MlWK69i+0/exMiDjIJBkAYVL+8kzu4fO/hkmbjp39IIkqVj5Iiluc+FkmD5+bWZyALmpbouHFR+XoDOHz8u/VCH55Mmp0JMX1fuTzgq6OFs/XF+gkd8NVMHiBVz3hJDhPNW+gcrVLKVp/f9r6fjDO0+ofeCb+nrc905LDQK3ci3yuqb3m6vgzc7fPj/WvLYk6eT+1P8heGjHMb1arq++/vA7JSbYXV4nMp8WPZoov39ufTbk9l+elK5SXDl9c2jSyoFafmaavto/Th3fbaFs7tnuU6VIE6ZX00rjVbbSC+OB5MSJE/r44481evRobdiwIcX+6OhovfPOOwYqyzxy5skpSboW5XxS582TPL19rZ2AXfeFh9WkXX0tn/6Toq9cU+6CvpKkLh++ovxF8un9/5usiV0/U5kapTR+/Xvy8s6ehs8CGVWh4vnV7cP/0ydvzFVUeLSl24Sfj9DViGsurgyZXWCdcnrprf/o1++2KyT4bKrHhJ/jvYa75+6RTc93baQN327X+RO3PjcuT8FcyueXW8XL+WvF7A0a8uIUrZz3i1r0aKL+n3S4fwUD6ZTRc0h27typzp07y8/PTw6HQwsWLFCTJk00YcIEeXp6SpJiYmK0bNkyffBByhPIYI2bmy3pP26xoJrd/u/Rul7LOho0v4/2bgjWrEELJEnunklvn4jQSI1oOV43F2w7d/SCpm5+X43b1tMPX6xNg2eAjOzNz7po+6o92vTdjn8/GEgjVR6roJHfvqVzx8M0sVv6XbEHGVu95x9UPr/cWvzJ6tsed/1qjN55YZJOH72gS+ciJEl//HZY8bEJ6jCkub6e8INOH7lwP0oG0iWjHZIJEyaoVatWWrVqlVavXq2JEyfq119/VY8ePRQfz4msaSX6StK3f96+3k7bvXMldUauRV6/7e1b9ntWQxb20/5NB/Xuc2MVH5e0zOiNPzss23/arb+vHn1g6xFdjYhWmRoBafYckDE9172JAioX12cDF8gtm5vcsrnJZksKyH//byAtNXzxEX248h2FnrqkgU+OoQMCl6n7nwd18sBZndh/5rbHxcXEa/fGA8lh5KZta/ZKShrnQgZhejSLka20d+jQIbVt2zb596efflozZszQ7t27NWDAAIOVZS7njoUqMSFRRcr6O22/+fup4Ft/kL42tZN6TGivX5Zs0ZBm7yvmWozz/Sba5ZHdI8Xt3D3cnU50R9ZUt3lt5Snoq4XHP9bKqDlaGTVHTV+pK/+SBbUyao5eeae56RKRybR+81kN+u9rOrD1qPo3GqmI0EjTJSGTyuaeTQ82DNRGC93fYmX99EyH+vL28XLant0raRrE6jgrkFkZHdny8fFRRESESpUqlbztwQcf1Lhx49SnTx998MEH6tq1q7kCM4n42Hjt3XhAdVs8nLyEryTVb1VHVyOidXDb0VRv1+n9/1Pz15/Wkkkr9Hn/uSn2x1yL0b5fDuixFg9r9uCvkjsnNRtVUQ4fL+375YBrnhAyjKl9vlQOH+dzlNoObq5yNUvpvdaTFX4+4ha3BO5csy6N1PXD/9PPizfrow7TlRDPgghwnYDAovLKmV3BW4/967H5C+dRn/FtlRCXqNVf/Zq8vX7zWrp29YaO7AlxZalAumc0kDRo0EAjR47U8OHDFRgYKA+PpG/amzRposGDB2v06NE6f/68yRIzja/GBGnsmnf17qI39dOX6xX4aAW1fus5zRy0QHExcfLOlUMlA4vp3LFQRV6KUpnqpfTSgOd1aPtRbfzmN1V6uJzT/YUEn9H1qzc0a/BXGv+/4Rrzw2AtnrBcef3yqMuHr+jAlsMplgxG1nMmlZnoqMvRio9L0JHdSev4e+fyUomKRXX+RJgiL1293yUik8jrl1vdx7fThZMX9d201Spb03lk9PzxUMXHJqhEpaI6fzyU9xruWalKRSVJpw6n/HeKt4+XSlQorPMnLyoyPFp//HpYezYdUrdRreXl7anTRy7ooaZV9Xy3Rpo5PEjR/zI6DWR2RgNJ//791a9fP7Vp00aff/656tevn7yvbdu2cnNz0/vv3/7qurDm9//t08hWE/Tq8Bc1/NsBCj97WTMGzNOSiSskSWUfCNCE/43QuI7TtHruz6r7wsNyc3NThdplNXVzyv8f9H/8Pe3dEKwDWw7r7UbD1XH0yxq25C3FXo/Vb99t1+dv/Vd2O0to4t+VrVFK434arPHdv9Ca+ZtMl4MM6qGnasjLO7v8SxXUpJ/fS7F/XOfPFBpyUePXvqtxnT/TmnkbDVSJzCRPoaSVJq9eSXmOUtnqJfTRd29pwutfas3CzbLbHRrRbrraDvyPXujZVPn8cuv8yYua2n++fprH515GwpmPrmFzOG6x9NJ9dOrUKeXNm1e5cuVKse/EiRNavXq1unfvfs+P09St9T3fB2CFWw5rSykD98oRn2C6BGQRbr4+pktAFvHTpfS7Ml61NyeZLiFVeyf2M13CPTHaIbmpRIkSt9wXEBCQJmEEAAAAQPqTLgIJAAAAkO4ZnyvKnIxfqR0AAABA1kUgAQAAAGAMI1sAAACABTZGtlyCDgkAAAAAYwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAAKxgZMsl6JAAAAAAMIZAAgAAAMAYRrYAAAAAC7gwomvQIQEAAABgDIEEAAAAgDGMbAEAAABWMLLlEnRIAAAAABhDIAEAAABgDCNbAAAAgAWssuUadEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGABq2y5Bh0SAAAAAMYQSAAAAAAYw8gWAAAAYAUjWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABZwYUTXoEMCAAAAwBgCCQAAAABjGNkCAAAArGBkyyXokAAAAAAwhkACAAAAwBhGtgAAAAALbA5mtlyBDgkAAAAAYwgkAAAAAIxhZAsAAACwgoktl6BDAgAAAMAYAgkAAAAAYxjZAgAAACywMbLlEnRIAAAAABhDIAEAAABgDCNbAAAAgBWMbLkEHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQYdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABawypZr0CEBAAAAYAyBBAAAAIAxjGwBAAAAVjiY2XIFOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsMBmN11B5kSHBAAAAMjiTpw4oZo1a2rp0qXJ2w4cOKC2bduqRo0aatiwoWbNmuWSxyaQAAAAAFlYfHy83nrrLV2/fj15W0REhDp27KhSpUopKChIvXv31pQpUxQUFJTmj8/IFgAAAGBFJl1l6+OPP1bOnDmdtn3zzTfy9PTU8OHD5e7urjJlyigkJEQzZsxQy5Yt0/Tx6ZAAAAAAWdT27du1aNEijR071mn7jh07VLt2bbm7/9W/qFOnjk6cOKHw8PA0rYEOCQAAAJCBNW7c+Lb7161bl+r2qKgoDRgwQEOHDlXhwoWd9l24cEHly5d32laoUCFJ0rlz55Q/f/57qNgZgQQAAACwwJbJRraGDx+uGjVq6D//+U+KfTExMfL09HTalj17dklSbGxsmtZBIAEAAAAysFt1QG5n2bJl2rFjh77//vtU93t5eSkuLs5p280g4u3tfedF3gaBBAAAAMhigoKCFB4eroYNGzptf++99zRr1iwVKVJEYWFhTvtu/u7n55emtRBIAAAAACscmWdma/z48YqJiXHa9sQTT6hPnz565pln9MMPP2jhwoVKTExUtmzZJEmbN29WQEBAmp4/IrHKFgAAAJDl+Pn5qWTJkk4/kpQ/f34VLVpULVu2VHR0tIYMGaKjR49q6dKlmjt3rrp3757mtRBIAAAAADjJnz+/Zs6cqRMnTqhFixb65JNPNGDAALVo0SLNH4uRLQAAAMCCzLbK1j8dOnTI6fdq1app0aJFLn9cOiQAAAAAjCGQAAAAADAmS41suZctbboEZBEPLDliugRkESs/rWe6BGQRO9771HQJgHmZfGTLFDokAAAAAIwhkAAAAAAwJkuNbAEAAAB3K7OvsmUKHRIAAAAAxhBIAAAAABjDyBYAAABghYOZLVegQwIAAADAGAIJAAAAAGMY2QIAAAAsYJUt16BDAgAAAMAYAgkAAAAAYxjZAgAAAKxgZMsl6JAAAAAAMIZAAgAAAMAYRrYAAAAAC1hlyzXokAAAAAAwhkACAAAAwBhGtgAAAAAr7MxsuQIdEgAAAADGEEgAAAAAGMPIFgAAAGAFE1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMCFEV2DDgkAAAAAYwgkAAAAAIxhZAsAAACwwsHMlivQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACABTYujOgSdEgAAAAAGEMgAQAAAGAMI1sAAACAFXbTBWROdEgAAAAAGEMgAQAAAGAMI1sAAACABayy5Rp0SAAAAAAYQyABAAAAYAwjWwAAAIAVTGy5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYAWrbLkEHRIAAAAAxhBIAAAAABjDyBYAAABggY2JLZegQwIAAADAGAIJAAAAAGMY2QIAAACsYJUtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACyw2U1XkDnRIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwseUSdEgAAAAAGEMgAQAAAGAMI1sAAACABTZW2XIJOiQAAAAAjCGQAAAAADCGkS0AAADACka2XIIOCQAAAABjCCQAAAAAjGFkCwAAALDCbrqAzIkOCQAAAABjCCQAAAAAjGFkCwAAALCACyO6Bh0SAAAAAMYQSAAAAAAYw8gWAAAAYAUjWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFbYTReQOdEhAQAAAGAMgQQAAACAMQQSAAAAwAKbw5Euf+7FlStXNGzYMNWvX18PPPCAXn75Ze3YsSN5/4EDB9S2bVvVqFFDDRs21KxZs+71ZUyBQAIAAABkUW+++ab27NmjiRMnasmSJapcubI6d+6sY8eOKSIiQh07dlSpUqUUFBSk3r17a8qUKQoKCkrTGjipHU4K+OfWpyv6aWSv/+qPbceTt+f381Xnt5/Rg/XKy90jmw7tPa1ZY3/UsQPnDFaL++X6pXit6X9UjwwooUKVc97yOHu8XYdXhCvk5yu6Hh6vHPk8VKJeblVsXkBuHq75/uPC7qvavzBMUWdild3XXaWfyKsKzQvIZrMlHxO2/5qCF4Up8lSM3NzdlL9CDlVr6yefwtldUhPM8/Jw1y+TX1M2N+f3XWx8gh7p87HTNnc3N81660X9tv+kPv9hy/0sEwadD5Oe7yh9Mlp6qOatjzseIjV71ZZie0AJh36c55raftkqTZklHTsp5c0jtXlO6vqK9LePNW3bLX38pXT4mOTpKdWoLL3VQypZzDU1IXMKCQnRr7/+qq+//loPPPCAJGnIkCHauHGjVqxYIS8vL3l6emr48OFyd3dXmTJlFBISohkzZqhly5ZpVgeBBMkKFcmj0bM7y8c3h9P2HDk9NW5BD8XHJ+rjYd8qLjZeL7/WWGPmdFHPZycp4uJVQxXjfrh2MU6bRoco/vq/Ly3y+5wLCtlwRZVaFlTesjl05XiMgheH6frFeNXqVTTNa7t06Lp+HXtaxR/1VeU2hXTp4HXt+zpMDrtUqWVBSVL4oev6ZdRJFamVSw/1KabEWLsOBF3S/949qScmllF2Xz4GM6NyxQoom5ubBs36QefDo5K32+3Oow3ZPbJpdMenVTWgsH7bf/I+VwlTzl6Qur4tXY22Sbr9uMuBo0n/d+5khzw9/9ru5aLvM3bvk14bLD31uNS3s7TzD2nyTMnukHq0++uYzv2lxx+TPnpXiomRPpsnte0tLf8yKcTARTLZhRHz5s2rL774QlWqVEneZrPZ5HA4FBkZqX379ql27dpyd//rb2WdOnX0+eefKzw8XPnz50+TOvhLDNlsNjVp8aC6DHwm1f0tOtSTb96c6vrU+OTwcWTfGU39to+qPVRaG37Ycz/LxX3isDsUsuGK9v431NLxcVcTdHxNhKq+4qcKzxeQJPlV9ZEk/TE/VFVf8VP23Hf+kbP/mzCF/HxFz0wvn2LfgcVhylPKSw/1SfpK0L9mLtkTHTq07JLKP5tf2bK76eC3l+RbNLvqvFlcNrekrxfzV/TWjz0O6+TPV1ThuQJ3XBPSv/LFCikuPkHrdx1Vgj31MF2zbFENeulxFczjc5+rgyl2u7TsJ+mjT63f5uBRqai/47ZdlDv1yZfStz9J6xal3DdtjlSxrPTR0KTf6z0sJSRIMxZIHV5MCkIzFkilS0qTR0g3m4A1q0qNWifdb6c2aVcrMobGjRvfdv+6detS3e7r66sGDRo4bVu5cqVOnTqlunXratKkSSpf3vnvb6FChSRJ586dS7NAwjkkUEBFf70+ornWLtul8QNSfjo+9mQVbfrpD6dOSMSlaLWr9z5hJBOLDInRrhnnVbJBHtXu/e/djfjrdpVumldFauVy2p6rSNJXitFhccnbzm2P0rqBx7T0/4L1fZdD+n32eSXE3Nni7onxdl3cf11FH3Z+vGJ1cishxq6LB69LkvKVy6GyzfInhxFJypHXQ+45sunahTghc6pQrKCOX7h8yzAiSZN6Pqfzl6/qlQ8W3MfKYNKhY9KISVLzJ6WxQ6zd5uDRpIDwb9Ztklp1k6o3leq1kN6fKl2/cWf1xcVJ236XmtZ33v5kA+n6DZt27k36vWol6dVWf4URSSqUX/Lxlk6dvbPHBP5u586dGjx4sBo3bqxGjRopJiZGnn9vDUrKnj2pPRgbG5tmj2u8QxIbG6sjR46obNmy8vLy0oEDBzR//nyFhoaqXLlyat++vfz9/U2XmamFnbuizk3G6VJopKo+VNppXzZ3N5Uo46f13+1Wu75P6KnWteWbN6cO7A7R9JHf6eThC4aqhqt5F/DQUx+Xk3d+D4Xtv/avx+f089QDXYuk2H52a5Rs2WzK9ef5Gqd+uaJtU8+qRL3cqtymkK5djNe+r5LOAan3bsmkVnGi468hij//PWlP/KtN7pbNpmuh8bInOJSriPPchI//nwHoXKxU3Sd5dOvvwvZdU/y1RPmW4BySzKpC8YJyOBya3ucFVS9dRHEJiVq767AmBW3U9dh4SVKXCd/o6Llww5XifirsJ61aIPkXSjoHw4qDR6UyJaU2PZPGt3x9pOZPSX06Sx5//itqxRrp7dE2/aepQ307J42ETZ4hHTkpzZ6QdO5HYuJf0z43c3JCwl+P4+4unT4nxcfbVLK481hQiT/PCzl5WnqsttTz1ZR1bt0lRV61qVzpzDVSlO6k05GtW3VA7sTatWv11ltvqXr16po4caIkycvLS3Fxzl/e3Qwi3t7e9/yYNxkNJMeOHVOHDh108eJFFSlSRKNHj1avXr1UrFgxlSlTRmvXrtXSpUv11VdfqUyZMiZLzdSiI28oOjL1r3F8fHPI3SObWnSopwtnwjV5aJA8PLOpXZ8nNHZeN/V6brLCQ6NSvS0yNs9c7vL898Nu68yWKIVsjFTZZ/LJ0yebHA6H/lgQKr8aPsljVpLkU9hTv4wM0YVd0Sr8YC5tGHlSl4KvO93X0jbByf/99LRyir+WKElyz+Hc6L35e/yN1L8Zj41M0M7PzilHfneVapDnHp8h0iObTSpbpIAS7XZ9++smzfxxqwJL+qlbszoqXTi/ukz8Rg6HCCNZUB5fSb7Wj790Wbp02SabzaH+3ZMCzZad0syvpQth0rh3k/59OuFzqd5DjuQxKynp5PJOb9q0YYtDDR+ROr4pbf/d+eT4qn+bslm70KGo6KT/9vnHv/Ny/nlqZ/Qtvhu6fEUaNl7yL+hQ8yetPz/gpvnz52vMmDFq2rSpxo8fn9wV8ff3V1hYmNOxN3/38/NLs8c3Gkg++ugj1axZU7169dKsWbPUs2dPPffccxo5cqRsNpsSEhI0YMAAffDBB5o5c6bJUrMsD4+/3iJDO89WzPWklHzkj7OateZt/afto5oz4SdT5SEdO7M5UtumnlXBQG9VfSXpQ+vquTjdCE9QxRa5nDoeBQNzyj2Hm0L3JgWSB7sVUfyfI1wn1kTo/K6renRgieTjc+R1143Lf361mHLxm6TNqWy/cTlev4wOUWxUguoPKyX3HNnS5skiXbHJpj7Tlik86ppOhkZIknYdPavwqOsa0+lpPRJYihPYYYlPTmnWBIcCSkiFk8bm9VCNpFWtpsy0qUc7h2w26cJFm7q1dTh1PGpXl3xyOvTbDqnhI9KI/tK160mfe998L/28WZr+/l/HFywghV5K+u/UPr8k5xGtm8IuSV3eksIjpC8nSjnT7ktrZBFfffWVRo0apXbt2mnw4MFy+9sbrXbt2lq4cKESExOVLVvS38zNmzcrICAgzc4fkQwHkm3btikoKEilS5fWwIED9f333+vll19OXq7T3d1dPXr00EsvvWSyzCzt+rWkttzebceSw4gkXTx/RaeOhal0pZQjOsDh7y9p77xQFaycU48NKK5sfy75G3c1qauxe+Z57Z55PsXtYiKS/prnKvrXKNX5nVfl5m5TvjLOq7955Ey6z4R/rP6V8GdnxMPbOWxEhsRo0wenlBBjV90hJZWvrPP9IfOwOxzaeeRMiu2/7DshSSpftCCBBJZ4ZZcerZVye4M60pSZ0sFjfwWVkZNsGjkp5bEXLyWFkIC/vlPRz5slDw+pSkXnY3P9ub7CPzsh1/4cYvD5x6rrh49JPQYlnasyY1zSuSVwsXQ6snW3Tpw4offff19NmzZV9+7dFR7+V+fYy8tLLVu21MyZMzVkyBB16dJFe/fu1dy5czVixIg0rcNoIPHy8lJMTIwkqUCBAnrxxReTT5S5KSoqSrly5Urt5rgPrkfHKOLSVXl4pnyruLtnU1xMvIGqkF45HA79PvuCjv10WcUe8VXt3kWTw4j0V4io2s5PBVO5nolnTusdCx8/T9ncpOh/nJh+83ffYn99loT9Ea3fxp2Wh7ebGo4spdwlvO7oeSFjKZg7p+pWCdCv+08q7Ep08navPzu+V67d4ZnGyLJOnJK27paebeIcBmL+PJc3b+6/QsTbPR16qEbK+/C9g3/ClCgiZcvmSHFi+qk/83WZUn9t27JLen2IlCun9N+pUnnnU0ABS1atWqX4+HitWbNGa9ascdrXokULffjhh5o5c6bGjBmjFi1aqGDBghowYIBatGiRpnUYDSR169bVqFGjNHr0aJUpU0YjR45M3udwOLRt2zaNGDFCTZo0MVgldmw8pEebVpFvXm9FRSTN9RcNKKBiAQX00+JthqtDerLvqzAd++myyjXLr2rt/ZwuTihJvkWyK3vubLoeFqd8f1tuNyYiXts+PqvSTfMmn5T+b7J5uqlApZw6uzVK5Z/Ln/xYZ7ZEyiOnW3IHJOLEDf069pRyFvJUvSEllSO/Rxo9W6RXnh7uerdtU33xwxZ9tmJz8vYnapVXot2u3UdZhgjWhF6SRky0ycPdoZbN/tq+8n9STm+HKldIOt8jf16Hzpx3Xm73Yrg0YEzSRQ1LWLwMU/bsUq1q0pqNSfd18yN01QbJ18ehan92QIIPS73ekYoWlmaOk/xSrt0BWNKjRw/16NHjtsdUq1ZNixalskZ1GjIaSN555x316NFD06dP14QJE5z2/fjjj+rfv7/q1aunN99801CFkKSvpq3TI00qa8zsLvpq2lplc8+mDm8+pYvnI7WKQJJlxV9PVNSZWPn4eSp7bnddOXFDh767pLxlvFTsUV9dPuL8LbRvsezy8M6mKi/7aefn52Rzs6nwg7kUfz1RB5Zc1PXwBOUtnXKMqvKLhVT5xUKp1lCpZQFtHBWiLRPPqFSjPAo/dF2Hl4er6it+ypY9qRuz89NzsidIga0L6np4vK6H/9XVy+7rbjkAIeM4eylSK7YEq8MTtRSfkKg/TpxXjbJF1enJ2lq8YY9C/jyvBPin6GvS0ZNJASJfnqTzQB6q4dDY6dKNmKSxqw1bpPlB0ts9pdx/dj/6dpGGT5CyuUkNH5WuRkuf/lcKvShVrpDycV7vmPSTmh6vSp3elPq9J73wjLR7vzR7odS/+18XYxz6UdIKXa93SLri/Pm/nXOcL4/1AIS7cGcr1MMio4EkX758+uabb3TlypUU+x555BEtW7ZMFStWTHlD3FcXTl9W/5emq9PbT+utj9rIbrdr969H9MX7K3TjGtdxyKoiTsRo4/CTqtWriEo9nldnt16VHFLEsRj9b8iJFMfXH15KhSrnVEDjvHLP4abD313S8bURcvdyU/4K3nqoTzHl9LuzcFCoqo8e6V9cwd+EafNHp5Ujn7uqtfNT+f8kdV+iQ+N05UTSWOiWiSnPKSjZII9qv85f7sxo9IK1OhV2Rc/WCVSXZx5W2JVofbZii/67Zofp0pCOBR+W2r9h0/uDHGrxtJQtm/TJGOmTOdKcb6SLl6XiRaTh/aUX//PX7Vo/m9QpmbVQ+maF5J1DeqCKNG6oVKzwndVQ5wFpysikiye+PlTyK5AUfjr+eTrt6XPSgSNJrZM33kt5++ZPOfTBO3f3/AFTbA5HJjs75zaeLj/QdAnIIh5YcsR0CcgiVn5az3QJyCJ2vHcHlzcH7oGb/2HTJdzSU5UtXlHzPvtp/xjTJdwT4xdGBAAAADICW9b5Hv++SmVFawAAAAC4PwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAAKywM7LlCnRIAAAAABhDIAEAAABgDCNbAAAAgBWssuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWGFnZMsV6JAAAAAAMIZAAgAAAMAYRrYAAAAAKxx20xVkSnRIAAAAABhDIAEAAABgDCNbAAAAgBVcGNEl6JAAAAAAMIZAAgAAAMAYRrYAAAAAK7gwokvQIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGCF3W66gkyJDgkAAAAAYwgkAAAAAIxhZAsAAACwglW2XIIOCQAAAABjCCQAAAAAjGFkCwAAALCCkS2XoEMCAAAAwBgCCQAAAABjGNkCAAAArLAzsuUKdEgAAAAAGEMgAQAAAGAMI1sAAACABQ6H3XQJmRIdEgAAAADGEEgAAAAAGMPIFgAAAGAFq2y5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYIWDkS1XoEMCAAAAwBgCCQAAAABjGNkCAAAArLBzYURXoEMCAAAAwBgCCQAAAABjGNkCAAAArGCVLZegQwIAAADAGAIJAAAAAGMY2QIAAAAscLDKlkvQIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFbYGdlyBTokAAAAAIwhkAAAAAAwhpEtAAAAwAoHF0Z0BTokAAAAAIwhkAAAAAAwhpEtAAAAwAIHq2y5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYAWrbLkEHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQYdEgAAAADGEEgAAAAAGMPIFgAAAGAFq2y5BB0SAAAAAMYQSAAAAAAYY3M4HCwXAAAAAMAIOiQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGMIJAAAAACMIZAAAAAAMIZAAgAAAMAYAgkAAAAAYwgkAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjCGQIFV2u11Tp05VvXr1VL16dXXq1EkhISGmy0ImN336dLVr1850GciErly5omHDhql+/fp64IEH9PLLL2vHjh2my0ImFR4errffflt16tRRzZo11a1bNx09etR0WUC6RSBBqqZPn66FCxdq9OjRWrRokWw2m7p27aq4uDjTpSGTmjNnjqZOnWq6DGRSb775pvbs2aOJEydqyZIlqly5sjp37qxjx46ZLg2ZUM+ePXX69GnNmDFDS5YskZeXlzp06KAbN26YLg1IlwgkSCEuLk6zZ89W79691aBBA1WsWFGTJk1SaGio1qxZY7o8ZDKhoaHq0qWLpkyZooCAANPlIBMKCQnRr7/+qvfee0+1atVS6dKlNWTIEPn5+WnFihWmy0MmExERoWLFimnUqFGqWrWqypQpo169eunixYs6cuSI6fKAdIlAghQOHjyoa9euqU6dOsnbfH19FRgYqO3btxusDJnR/v37lTt3bi1fvlzVq1c3XQ4yobx58+qLL75QlSpVkrfZbDY5HA5FRkYarAyZUd68eTVx4kSVK1dOknTp0iXNmjVL/v7+Klu2rOHqgPTJ3XQBSH8uXLggSSpcuLDT9kKFCun8+fMmSkIm1qhRIzVq1Mh0GcjEfH191aBBA6dtK1eu1KlTp1S3bl1DVSErePfdd/XNN9/I09NTn376qby9vU2XBKRLdEiQws0ZV09PT6ft2bNnV2xsrImSACDN7Ny5U4MHD1bjxo0Jw3Cp9u3bKygoSM8995xee+017d+/33RJQLpEIEEKXl5ekpTiBPbY2FjlyJHDREkAkCbWrl2rzp07q1q1apo4caLpcpDJlS1bVlWqVNGoUaNUrFgxzZ8/33RJQLpEIEEKN0e1wsLCnLaHhYXJ39/fREkAcM/mz5+v3r17q379+poxY0byly9AWgoPD9eKFSuUmJiYvM3NzU1lypRJ8XcVQBICCVKoWLGifHx8tHXr1uRtUVFRCg4OVq1atQxWBgB356uvvtKoUaP0yiuvaPLkySlGUoG0EhYWpv79+2vbtm3J2+Lj4xUcHKwyZcoYrAxIvzipHSl4enqqbdu2Gj9+vPLly6eiRYtq3Lhx8vf3V9OmTU2XBwB35MSJE3r//ffVtGlTde/eXeHh4cn7vLy8lCtXLoPVIbOpWLGi6tatqxEjRmj06NHy9fXVZ599pqioKHXo0MF0eUC6RCBBqvr06aOEhAQNHTpUMTExql27tmbNmsW3igAynFWrVik+Pl5r1qxJcS2lFi1a6MMPPzRUGTIjm82myZMna8KECXrjjTd09epV1apVSwsWLFCRIkVMlwekSzaHw+EwXQQAAACArIlzSAAAAAAYQyABAAAAYAyBBAAAAIAxBBIAAAAAxhBIAAAAABhDIAEAAABgDIEEAAAAgDEEEgAAAADGEEgAIIOJjY1VYGCgatasqVGjRpkuBwCAe0IgAYAMxmazae7cuapWrZrmz5+vEydOmC4JAIC7RiABgAzG09NTtWvXVpcuXSRJ+/fvN1wRAAB3j0ACABlU6dKlJUkHDhwwXAkAAHePQAIAGdSMGTMkSQcPHjRcCQAAd49AAgAZ0KZNm/T1118rd+7cCg4ONl0OAAB3jUACABlMVFSUBg8erMaNG+vll1/W5cuXFRoaarosAADuCoEEADKYESNGKCEhQaNHj1ZgYKAkxrYAABkXgQQAMpCffvpJK1as0JgxY5QvX77kQMKJ7QCAjIpAAgAZxMWLF/Xee+/ppZde0uOPPy5JKl68uHx9fTmPBACQYRFIACCDePfdd5U7d24NGjTIaXulSpUY2QIAZFgEEgDIABYvXqyNGzfqo48+kre3t9O+wMBAnTp1StHR0YaqAwDg7tkcDofDdBEAAAAAsiY6JAAAAACMIZAAAAAAMIZAAgAAAMAYAgkAAAAAYwgkAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGP+HxPLK3XyXLGWAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  1.7304881   19.47958494  12.11340253  23.41589548]\n",
-      " [  0.22948497   0.18392847   0.29364655   0.20072279]\n",
-      " [  0.26303845   4.06730814   2.11590451   7.5411205 ]\n",
-      " [ 15.77893972 122.02334824  51.2167072  152.54894451]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -2951,7 +2726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19fec57c",
+   "id": "50a1e770",
    "metadata": {
     "editable": true
    },
@@ -2971,7 +2746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bcb8b34",
+   "id": "5af218e3",
    "metadata": {
     "editable": true
    },
@@ -3003,7 +2778,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ebf620c",
+   "id": "55450c7b",
    "metadata": {
     "editable": true
    },
@@ -3027,7 +2802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db27019b",
+   "id": "be42ca6d",
    "metadata": {
     "editable": true
    },
@@ -3055,7 +2830,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ab4acd0",
+   "id": "4b7a4ceb",
    "metadata": {
     "editable": true
    },
@@ -3070,7 +2845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b60035b5",
+   "id": "e00d9920",
    "metadata": {
     "editable": true
    },
@@ -3082,7 +2857,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29af7e67",
+   "id": "6243c073",
    "metadata": {
     "editable": true
    },
@@ -3097,7 +2872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "740b4af2",
+   "id": "009ea7de",
    "metadata": {
     "editable": true
    },
@@ -3110,7 +2885,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ad3976",
+   "id": "712c684d",
    "metadata": {
     "editable": true
    },
@@ -3122,7 +2897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0051e649",
+   "id": "b4bcd2a5",
    "metadata": {
     "editable": true
    },
@@ -3132,7 +2907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a7712be",
+   "id": "143b6359",
    "metadata": {
     "editable": true
    },
@@ -3143,7 +2918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27e08926",
+   "id": "d2e56068",
    "metadata": {
     "editable": true
    },
@@ -3161,7 +2936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ec61954",
+   "id": "2541505d",
    "metadata": {
     "editable": true
    },
@@ -3172,7 +2947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7aab6da",
+   "id": "725efee7",
    "metadata": {
     "editable": true
    },
@@ -3184,7 +2959,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "523fa0e8",
+   "id": "cb15e9f1",
    "metadata": {
     "editable": true
    },
@@ -3194,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4172710",
+   "id": "87026e15",
    "metadata": {
     "editable": true
    },
@@ -3206,7 +2981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a21de62",
+   "id": "cd09c85a",
    "metadata": {
     "editable": true
    },
@@ -3216,7 +2991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a68d2930",
+   "id": "c7845cfd",
    "metadata": {
     "editable": true
    },
@@ -3228,7 +3003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "519a6c63",
+   "id": "20e94324",
    "metadata": {
     "editable": true
    },
@@ -3238,7 +3013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f28767d",
+   "id": "46cc127e",
    "metadata": {
     "editable": true
    },
@@ -3257,7 +3032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60e193f4",
+   "id": "b83e3f3f",
    "metadata": {
     "editable": true
    },
@@ -3267,7 +3042,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b76cff50",
+   "id": "d3c3d5b8",
    "metadata": {
     "editable": true
    },
@@ -3279,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c65fa96",
+   "id": "c6e741c3",
    "metadata": {
     "editable": true
    },
@@ -3289,7 +3064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "554a1508",
+   "id": "510abc9b",
    "metadata": {
     "editable": true
    },
@@ -3305,7 +3080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec603f5c",
+   "id": "1a6464a4",
    "metadata": {
     "editable": true
    },
@@ -3325,7 +3100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98c74b9b",
+   "id": "0f403e2d",
    "metadata": {
     "editable": true
    },
@@ -3335,7 +3110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f4d2c5c",
+   "id": "4a8138dc",
    "metadata": {
     "editable": true
    },
@@ -3346,7 +3121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "654bf9a6",
+   "id": "726cbaa4",
    "metadata": {
     "editable": true
    },
@@ -3365,7 +3140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f80909ca",
+   "id": "6b7adfb6",
    "metadata": {
     "editable": true
    },
@@ -3375,7 +3150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee25c0f7",
+   "id": "84165f3e",
    "metadata": {
     "editable": true
    },
@@ -3387,7 +3162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1947e0c",
+   "id": "ebf366ef",
    "metadata": {
     "editable": true
    },
@@ -3397,7 +3172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05229723",
+   "id": "02ffad05",
    "metadata": {
     "editable": true
    },
@@ -3408,7 +3183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb0abff0",
+   "id": "d54237d5",
    "metadata": {
     "editable": true
    },
@@ -3428,7 +3203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc16c9f8",
+   "id": "8a7e9dbb",
    "metadata": {
     "editable": true
    },
@@ -3440,7 +3215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7f65472",
+   "id": "d4d5ff52",
    "metadata": {
     "editable": true
    },
@@ -3455,13 +3230,10 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "acf08e73",
+   "id": "297415bf",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -3538,7 +3310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "937591a1",
+   "id": "2ce99829",
    "metadata": {
     "editable": true
    },
@@ -3548,7 +3320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "132c386c",
+   "id": "f6a44999",
    "metadata": {
     "editable": true
    },
@@ -3560,7 +3332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "869bdef2",
+   "id": "990c34c3",
    "metadata": {
     "editable": true
    },
@@ -3570,7 +3342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7781a1c4",
+   "id": "a06f9457",
    "metadata": {
     "editable": true
    },
@@ -3581,7 +3353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd8e693c",
+   "id": "19cd9843",
    "metadata": {
     "editable": true
    },
@@ -3593,7 +3365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5c29ebf",
+   "id": "cc8c11ab",
    "metadata": {
     "editable": true
    },
@@ -3603,7 +3375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a798f40c",
+   "id": "d3d4e384",
    "metadata": {
     "editable": true
    },
@@ -3615,7 +3387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bae820af",
+   "id": "6187929e",
    "metadata": {
     "editable": true
    },
@@ -3625,7 +3397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06c75a81",
+   "id": "e0ba2cb1",
    "metadata": {
     "editable": true
    },
@@ -3637,7 +3409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59236913",
+   "id": "468fec32",
    "metadata": {
     "editable": true
    },
@@ -3650,7 +3422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81b5da1b",
+   "id": "ba618e10",
    "metadata": {
     "editable": true
    },
@@ -3662,7 +3434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e74c517f",
+   "id": "a74d3f1c",
    "metadata": {
     "editable": true
    },
@@ -3672,7 +3444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "648beb7f",
+   "id": "fb434398",
    "metadata": {
     "editable": true
    },
@@ -3684,7 +3456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d9326d3",
+   "id": "42558cc8",
    "metadata": {
     "editable": true
    },
@@ -3696,7 +3468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f51ed614",
+   "id": "d851da5b",
    "metadata": {
     "editable": true
    },
@@ -3707,7 +3479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ced37c54",
+   "id": "49533cb3",
    "metadata": {
     "editable": true
    },
@@ -3719,7 +3491,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebbeea54",
+   "id": "a62f6a4c",
    "metadata": {
     "editable": true
    },
@@ -3738,7 +3510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b5d06a0",
+   "id": "381182cd",
    "metadata": {
     "editable": true
    },
@@ -3751,7 +3523,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74890321",
+   "id": "04b172c8",
    "metadata": {
     "editable": true
    },
@@ -3761,7 +3533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad33aa43",
+   "id": "265d8614",
    "metadata": {
     "editable": true
    },
@@ -3773,7 +3545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "626df4e6",
+   "id": "68c1005e",
    "metadata": {
     "editable": true
    },
@@ -3783,7 +3555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6594383b",
+   "id": "61fb0fbc",
    "metadata": {
     "editable": true
    },
@@ -3795,7 +3567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec04a0b3",
+   "id": "acb3fa59",
    "metadata": {
     "editable": true
    },
@@ -3805,7 +3577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a82796e0",
+   "id": "82f920b5",
    "metadata": {
     "editable": true
    },
@@ -3817,7 +3589,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9607cc25",
+   "id": "26de4671",
    "metadata": {
     "editable": true
    },
@@ -3828,7 +3600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f84698d3",
+   "id": "071a86ff",
    "metadata": {
     "editable": true
    },
@@ -3840,7 +3612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1245df8a",
+   "id": "fdb51c1d",
    "metadata": {
     "editable": true
    },
@@ -3850,7 +3622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6df8204",
+   "id": "5532b00a",
    "metadata": {
     "editable": true
    },
@@ -3862,7 +3634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6250497e",
+   "id": "a7c57982",
    "metadata": {
     "editable": true
    },
@@ -3872,7 +3644,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f788dc8f",
+   "id": "022ce912",
    "metadata": {
     "editable": true
    },
@@ -3884,7 +3656,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdb72b3a",
+   "id": "12518c37",
    "metadata": {
     "editable": true
    },
@@ -3905,7 +3677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d498ca67",
+   "id": "89f9bbda",
    "metadata": {
     "editable": true
    },
@@ -3916,7 +3688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "786a3fef",
+   "id": "51458daa",
    "metadata": {
     "editable": true
    },
@@ -3928,7 +3700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0080ea0",
+   "id": "eaf4a93e",
    "metadata": {
     "editable": true
    },
@@ -3938,7 +3710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27b8b857",
+   "id": "2fea3537",
    "metadata": {
     "editable": true
    },
@@ -3950,7 +3722,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bf5241a",
+   "id": "ca4f1123",
    "metadata": {
     "editable": true
    },
@@ -3960,7 +3732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b578bc7",
+   "id": "09de659a",
    "metadata": {
     "editable": true
    },
@@ -3972,7 +3744,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11220b8a",
+   "id": "77f9fd08",
    "metadata": {
     "editable": true
    },
@@ -3984,7 +3756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a920365b",
+   "id": "beab9d54",
    "metadata": {
     "editable": true
    },
@@ -3998,13 +3770,10 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "0b7b8c69",
+   "id": "ce0d14e1",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4016,7 +3785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c209e80c",
+   "id": "20bbbdf2",
    "metadata": {
     "editable": true
    },
@@ -4027,13 +3796,10 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "947eedad",
+   "id": "d1b21aae",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4043,7 +3809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55e5aa0a",
+   "id": "02bb27d6",
    "metadata": {
     "editable": true
    },
@@ -4054,13 +3820,10 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "3fb277ad",
+   "id": "98630c99",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4080,7 +3843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36c1d2bc",
+   "id": "9af04036",
    "metadata": {
     "editable": true
    },
@@ -4094,13 +3857,10 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "6db56b3d",
+   "id": "2d8cd3da",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4110,7 +3870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0ee98a4",
+   "id": "71899eed",
    "metadata": {
     "editable": true
    },
@@ -4121,13 +3881,10 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "8774bce4",
+   "id": "f8cc2dd0",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4136,7 +3893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d29c10a",
+   "id": "44f3dd69",
    "metadata": {
     "editable": true
    },
@@ -4147,13 +3904,10 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "93c8b0c6",
+   "id": "acdf8d3d",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4166,7 +3920,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff36a22e",
+   "id": "26d00774",
    "metadata": {
     "editable": true
    },
@@ -4177,13 +3931,10 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "35feafa3",
+   "id": "fcf52c3c",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4194,7 +3945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c4ac2dc",
+   "id": "82083461",
    "metadata": {
     "editable": true
    },
@@ -4216,7 +3967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc2eacf3",
+   "id": "c247a29f",
    "metadata": {
     "editable": true
    },
@@ -4228,7 +3979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48c5a8b6",
+   "id": "5c6266aa",
    "metadata": {
     "editable": true
    },
@@ -4238,7 +3989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc4699e",
+   "id": "c36f8d5c",
    "metadata": {
     "editable": true
    },
@@ -4250,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4db48e9d",
+   "id": "5b1a5174",
    "metadata": {
     "editable": true
    },
@@ -4262,7 +4013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "350fec9b",
+   "id": "81c82695",
    "metadata": {
     "editable": true
    },
@@ -4272,7 +4023,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b474ddf5",
+   "id": "7dc8a3bc",
    "metadata": {
     "editable": true
    },
@@ -4284,7 +4035,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67fc1d79",
+   "id": "41789a38",
    "metadata": {
     "editable": true
    },
@@ -4294,7 +4045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6aa9f684",
+   "id": "4a2e42bf",
    "metadata": {
     "editable": true
    },
@@ -4306,7 +4057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce023be0",
+   "id": "acd67ee6",
    "metadata": {
     "editable": true
    },
@@ -4316,7 +4067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3754c848",
+   "id": "ee90b0eb",
    "metadata": {
     "editable": true
    },
@@ -4328,7 +4079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d884286",
+   "id": "54eaab7f",
    "metadata": {
     "editable": true
    },
@@ -4340,7 +4091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8446340",
+   "id": "f941f1ac",
    "metadata": {
     "editable": true
    },
@@ -4350,7 +4101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6830538",
+   "id": "89e02a88",
    "metadata": {
     "editable": true
    },
@@ -4362,7 +4113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84f9e6b4",
+   "id": "3d1f3b65",
    "metadata": {
     "editable": true
    },
@@ -4372,7 +4123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "586509f5",
+   "id": "1dda0ac2",
    "metadata": {
     "editable": true
    },
@@ -4384,7 +4135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff04787a",
+   "id": "aafe7aae",
    "metadata": {
     "editable": true
    },
@@ -4396,7 +4147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fab40e86",
+   "id": "37f9c1fa",
    "metadata": {
     "editable": true
    },
@@ -4408,7 +4159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "175c9026",
+   "id": "373adac2",
    "metadata": {
     "editable": true
    },
@@ -4418,7 +4169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b8d1d83",
+   "id": "bdd6d800",
    "metadata": {
     "editable": true
    },
@@ -4430,7 +4181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c64f049",
+   "id": "913df005",
    "metadata": {
     "editable": true
    },
@@ -4440,7 +4191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a591f8f",
+   "id": "76f6737e",
    "metadata": {
     "editable": true
    },
@@ -4452,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "956b5691",
+   "id": "48f6aa66",
    "metadata": {
     "editable": true
    },
@@ -4462,7 +4213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85b1bd72",
+   "id": "513a1986",
    "metadata": {
     "editable": true
    },
@@ -4474,7 +4225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6950fb95",
+   "id": "20fbbe07",
    "metadata": {
     "editable": true
    },
@@ -4485,7 +4236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2d998e7",
+   "id": "4dd97daa",
    "metadata": {
     "editable": true
    },
@@ -4497,7 +4248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79e10d20",
+   "id": "032afd0e",
    "metadata": {
     "editable": true
    },
@@ -4507,7 +4258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6585dbe2",
+   "id": "25eac667",
    "metadata": {
     "editable": true
    },
@@ -4519,7 +4270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4291e4ad",
+   "id": "18c343cd",
    "metadata": {
     "editable": true
    },
@@ -4529,7 +4280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a50444a",
+   "id": "9864986f",
    "metadata": {
     "editable": true
    },
@@ -4541,7 +4292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58d08cf2",
+   "id": "1ecdde02",
    "metadata": {
     "editable": true
    },
@@ -4554,7 +4305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8215c3c6",
+   "id": "9d62dddc",
    "metadata": {
     "editable": true
    },
@@ -4566,7 +4317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09efd53b",
+   "id": "db398f5b",
    "metadata": {
     "editable": true
    },
@@ -4578,7 +4329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c694cf4",
+   "id": "2bbf293c",
    "metadata": {
     "editable": true
    },
@@ -4590,7 +4341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "909419d5",
+   "id": "d82e1506",
    "metadata": {
     "editable": true
    },
@@ -4602,7 +4353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d35d3a4",
+   "id": "1ddd417c",
    "metadata": {
     "editable": true
    },
@@ -4614,7 +4365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "845b476b",
+   "id": "e7904873",
    "metadata": {
     "editable": true
    },
@@ -4624,7 +4375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "644470d4",
+   "id": "19604164",
    "metadata": {
     "editable": true
    },
@@ -4636,7 +4387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c4ad6c8",
+   "id": "0f262e03",
    "metadata": {
     "editable": true
    },
@@ -4648,7 +4399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "839e60fb",
+   "id": "cdb18e0d",
    "metadata": {
     "editable": true
    },
@@ -4660,25 +4411,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
\ No newline at end of file
diff --git a/doc/LectureNotes/week34.ipynb b/doc/LectureNotes/week34.ipynb
index 95856e1b9..899bd8e9f 100644
--- a/doc/LectureNotes/week34.ipynb
+++ b/doc/LectureNotes/week34.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3b47d5e6",
+   "id": "0b9e53e2",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f010738",
+   "id": "d69d6c55",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9821466c",
+   "id": "ed7a2caa",
    "metadata": {
     "editable": true
    },
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d635526f",
+   "id": "1a7fa793",
    "metadata": {
     "editable": true
    },
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46d1ebd2",
+   "id": "42eed58e",
    "metadata": {
     "editable": true
    },
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "722a1812",
+   "id": "19b78895",
    "metadata": {
     "editable": true
    },
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8724a41b",
+   "id": "920c8b78",
    "metadata": {
     "editable": true
    },
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a74065a9",
+   "id": "66791ac8",
    "metadata": {
     "editable": true
    },
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb3519d1",
+   "id": "d006c6f2",
    "metadata": {
     "editable": true
    },
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f46e4ab5",
+   "id": "d2b59d04",
    "metadata": {
     "editable": true
    },
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18134f7b",
+   "id": "2b1dfff8",
    "metadata": {
     "editable": true
    },
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e4b2cb4",
+   "id": "39ce46e9",
    "metadata": {
     "editable": true
    },
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dc04ba4",
+   "id": "cc1139fe",
    "metadata": {
     "editable": true
    },
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9d7446a",
+   "id": "f56c0bb5",
    "metadata": {
     "editable": true
    },
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ac913df",
+   "id": "4b601e8d",
    "metadata": {
     "editable": true
    },
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe4f142d",
+   "id": "2518a99e",
    "metadata": {
     "editable": true
    },
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82a26310",
+   "id": "a0e1e9ae",
    "metadata": {
     "editable": true
    },
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d97a91f",
+   "id": "8af343cb",
    "metadata": {
     "editable": true
    },
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0af85fa1",
+   "id": "c0cfedb4",
    "metadata": {
     "editable": true
    },
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca1c0d4b",
+   "id": "4561f563",
    "metadata": {
     "editable": true
    },
@@ -398,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e3a4194",
+   "id": "b94446df",
    "metadata": {
     "editable": true
    },
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44fe97f3",
+   "id": "2da0b611",
    "metadata": {
     "editable": true
    },
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fd61872",
+   "id": "cb6368d0",
    "metadata": {
     "editable": true
    },
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50148149",
+   "id": "cd5643ee",
    "metadata": {
     "editable": true
    },
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12bc5144",
+   "id": "dad2568b",
    "metadata": {
     "editable": true
    },
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25428d4e",
+   "id": "ccf34c1f",
    "metadata": {
     "editable": true
    },
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2efac50e",
+   "id": "d9a80c4f",
    "metadata": {
     "editable": true
    },
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfd0c6a0",
+   "id": "bf1454b7",
    "metadata": {
     "editable": true
    },
@@ -572,7 +572,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f92f8d35",
+   "id": "4a01a6f0",
    "metadata": {
     "editable": true
    },
@@ -588,7 +588,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "174d6e96",
+   "id": "2bf27180",
    "metadata": {
     "editable": true
    },
@@ -604,12 +604,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fcac42b",
+   "id": "15ddfa02",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Example of discriminative modeling, [taken from Generative Deeep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n",
+    "## Example of discriminative modeling, [taken from Generative Deep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n",
     "\n",
     "<!-- dom:FIGURE: [figures/standarddeeplearning.png, width=900 frac=1.0] -->\n",
     "<!-- begin figure -->\n",
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8942d9a",
+   "id": "371a54c3",
    "metadata": {
     "editable": true
    },
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a28ea0e4",
+   "id": "f9ec1f0a",
    "metadata": {
     "editable": true
    },
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3260f00",
+   "id": "19f2aef4",
    "metadata": {
     "editable": true
    },
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ac16500",
+   "id": "e08bd4e6",
    "metadata": {
     "editable": true
    },
@@ -736,7 +736,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "825ca09e",
+   "id": "729c3f46",
    "metadata": {
     "editable": true
    },
@@ -775,7 +775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40bd0bf2",
+   "id": "6825a222",
    "metadata": {
     "editable": true
    },
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a356fdf0",
+   "id": "bad98b2e",
    "metadata": {
     "editable": true
    },
@@ -845,7 +845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9730b8a",
+   "id": "1daa92e1",
    "metadata": {
     "editable": true
    },
@@ -869,7 +869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f967a6a",
+   "id": "1c840067",
    "metadata": {
     "editable": true
    },
@@ -896,7 +896,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76e167a3",
+   "id": "f33d6379",
    "metadata": {
     "editable": true
    },
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94a3c420",
+   "id": "e3a03646",
    "metadata": {
     "editable": true
    },
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6ff6b6b",
+   "id": "fb6d2495",
    "metadata": {
     "editable": true
    },
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55dbb002",
+   "id": "3101a680",
    "metadata": {
     "editable": true
    },
@@ -951,13 +951,10 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "655878bd",
+   "id": "66625f81",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -966,7 +963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "914fa12c",
+   "id": "d8edb94a",
    "metadata": {
     "editable": true
    },
@@ -977,13 +974,10 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a0cfca17",
+   "id": "7d44772f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -994,7 +988,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "342c0109",
+   "id": "7c3a5d5e",
    "metadata": {
     "editable": true
    },
@@ -1006,13 +1000,10 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d09ad51a",
+   "id": "bd150398",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1023,7 +1014,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be9009bc",
+   "id": "62bb511e",
    "metadata": {
     "editable": true
    },
@@ -1035,13 +1026,10 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "19dfe7ab",
+   "id": "de9d1b88",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1052,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c78e90e",
+   "id": "575f4f67",
    "metadata": {
     "editable": true
    },
@@ -1069,13 +1057,10 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "48603a4c",
+   "id": "eadd4613",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1089,7 +1074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5cfa34",
+   "id": "43c2b238",
    "metadata": {
     "editable": true
    },
@@ -1101,13 +1086,10 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "1c252e6c",
+   "id": "f1153763",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1118,7 +1100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "375afb81",
+   "id": "b897cb85",
    "metadata": {
     "editable": true
    },
@@ -1129,13 +1111,10 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4c87de99",
+   "id": "3417f7b5",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1146,7 +1125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afff17c8",
+   "id": "421de263",
    "metadata": {
     "editable": true
    },
@@ -1157,13 +1136,10 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "d5827065",
+   "id": "e9055b4f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1174,7 +1150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83e054e8",
+   "id": "52301fe3",
    "metadata": {
     "editable": true
    },
@@ -1189,13 +1165,10 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "0cd4400a",
+   "id": "7f400b7f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1206,7 +1179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2868228",
+   "id": "35677f44",
    "metadata": {
     "editable": true
    },
@@ -1217,13 +1190,10 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6e7ef557",
+   "id": "16b1c27a",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1235,7 +1205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47391e12",
+   "id": "7566ab17",
    "metadata": {
     "editable": true
    },
@@ -1246,13 +1216,10 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a9457054",
+   "id": "b10affa1",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1264,7 +1231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a04c51eb",
+   "id": "c50f969e",
    "metadata": {
     "editable": true
    },
@@ -1275,13 +1242,10 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "43e1f145",
+   "id": "35f1236f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1294,7 +1258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e11ea608",
+   "id": "02cec13a",
    "metadata": {
     "editable": true
    },
@@ -1305,13 +1269,10 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "ed021bcf",
+   "id": "821bc62d",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1324,7 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cf97a5d",
+   "id": "5df31e24",
    "metadata": {
     "editable": true
    },
@@ -1335,13 +1296,10 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "0dd77598",
+   "id": "d821d3ff",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1354,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a27628a3",
+   "id": "a99c04f6",
    "metadata": {
     "editable": true
    },
@@ -1366,7 +1324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80585c1d",
+   "id": "fdb302f5",
    "metadata": {
     "editable": true
    },
@@ -1381,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74725f56",
+   "id": "4c91d693",
    "metadata": {
     "editable": true
    },
@@ -1391,7 +1349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd223a73",
+   "id": "316ccd01",
    "metadata": {
     "editable": true
    },
@@ -1403,7 +1361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40dfa247",
+   "id": "73cf195b",
    "metadata": {
     "editable": true
    },
@@ -1414,7 +1372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56e47db3",
+   "id": "c9d6f1ba",
    "metadata": {
     "editable": true
    },
@@ -1429,7 +1387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26251aec",
+   "id": "a5bbc329",
    "metadata": {
     "editable": true
    },
@@ -1444,13 +1402,10 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "dab44ca5",
+   "id": "74dd353c",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1474,13 +1429,10 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "58c875cb",
+   "id": "f07645f8",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1501,7 +1453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d735077",
+   "id": "245ee493",
    "metadata": {
     "editable": true
    },
@@ -1527,13 +1479,10 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "d60ac131",
+   "id": "e428bcdc",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1550,7 +1499,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06e4b620",
+   "id": "861269ad",
    "metadata": {
     "editable": true
    },
@@ -1564,13 +1513,10 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "2174392e",
+   "id": "c34c9894",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1580,7 +1526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72c17dd0",
+   "id": "0ec5724d",
    "metadata": {
     "editable": true
    },
@@ -1591,13 +1537,10 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "7612f9de",
+   "id": "83302e08",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1606,7 +1549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "242d4c4b",
+   "id": "886277ee",
    "metadata": {
     "editable": true
    },
@@ -1617,13 +1560,10 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "0171d1ad",
+   "id": "c1010af7",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1638,7 +1578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5232bfb",
+   "id": "f3c82384",
    "metadata": {
     "editable": true
    },
@@ -1650,13 +1590,10 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "cb2ab622",
+   "id": "70542372",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1677,7 +1614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1998bc9c",
+   "id": "a83eb591",
    "metadata": {
     "editable": true
    },
@@ -1688,13 +1625,10 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "e0ec9915",
+   "id": "2f42295f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1720,7 +1654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebe44ad1",
+   "id": "fb1b0336",
    "metadata": {
     "editable": true
    },
@@ -1731,13 +1665,10 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "6f6d21e6",
+   "id": "0438c751",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1749,7 +1680,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f97c531",
+   "id": "f6d3d4c8",
    "metadata": {
     "editable": true
    },
@@ -1766,7 +1697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36a57ec4",
+   "id": "d552a0d0",
    "metadata": {
     "editable": true
    },
@@ -1778,7 +1709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca69e9e9",
+   "id": "b96bd9e5",
    "metadata": {
     "editable": true
    },
@@ -1809,7 +1740,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4bc6d8a",
+   "id": "41318c5c",
    "metadata": {
     "editable": true
    },
@@ -1821,7 +1752,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd9f61d0",
+   "id": "789c6fe4",
    "metadata": {
     "editable": true
    },
@@ -1849,13 +1780,10 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "81d8ecc3",
+   "id": "8c7472f2",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1882,7 +1810,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9f8b0e5",
+   "id": "25cef2d3",
    "metadata": {
     "editable": true
    },
@@ -1899,7 +1827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37a6973d",
+   "id": "58a04438",
    "metadata": {
     "editable": true
    },
@@ -1911,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b1616dd",
+   "id": "55191297",
    "metadata": {
     "editable": true
    },
@@ -1932,7 +1860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02f59dce",
+   "id": "2951bf64",
    "metadata": {
     "editable": true
    },
@@ -1945,7 +1873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37a3928f",
+   "id": "1109ceeb",
    "metadata": {
     "editable": true
    },
@@ -1976,7 +1904,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ced1c75",
+   "id": "7eeca264",
    "metadata": {
     "editable": true
    },
@@ -1988,7 +1916,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b2c7527",
+   "id": "fd8b7402",
    "metadata": {
     "editable": true
    },
@@ -2006,13 +1934,10 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "dcc4e935",
+   "id": "0de5719f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2036,7 +1961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef0a62d7",
+   "id": "ce194339",
    "metadata": {
     "editable": true
    },
@@ -2058,13 +1983,10 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "e2a9bb21",
+   "id": "cde68f17",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2099,7 +2021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6eca083",
+   "id": "f48a129c",
    "metadata": {
     "editable": true
    },
@@ -2110,7 +2032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c940fd70",
+   "id": "c337129d",
    "metadata": {
     "editable": true
    },
@@ -2123,7 +2045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b1317ef",
+   "id": "d6462e3c",
    "metadata": {
     "editable": true
    },
@@ -2144,7 +2066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3895d32d",
+   "id": "c2a5a5fd",
    "metadata": {
     "editable": true
    },
@@ -2156,7 +2078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88937a4",
+   "id": "d19d16f8",
    "metadata": {
     "editable": true
    },
@@ -2166,7 +2088,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d62abca",
+   "id": "7739ec09",
    "metadata": {
     "editable": true
    },
@@ -2178,7 +2100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "903ee760",
+   "id": "7f433018",
    "metadata": {
     "editable": true
    },
@@ -2190,7 +2112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef89ae2c",
+   "id": "20361838",
    "metadata": {
     "editable": true
    },
@@ -2202,7 +2124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "924ac782",
+   "id": "02d95914",
    "metadata": {
     "editable": true
    },
@@ -2213,7 +2135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50053e83",
+   "id": "524bb980",
    "metadata": {
     "editable": true
    },
@@ -2225,7 +2147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc7df77b",
+   "id": "6cb0b956",
    "metadata": {
     "editable": true
    },
@@ -2247,7 +2169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fe669ae",
+   "id": "142be577",
    "metadata": {
     "editable": true
    },
@@ -2259,7 +2181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28ceb188",
+   "id": "4e765569",
    "metadata": {
     "editable": true
    },
@@ -2272,7 +2194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d89d075b",
+   "id": "f79ebfdf",
    "metadata": {
     "editable": true
    },
@@ -2289,7 +2211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3816234",
+   "id": "eb09c88a",
    "metadata": {
     "editable": true
    },
@@ -2301,7 +2223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ddf946bd",
+   "id": "bf69319c",
    "metadata": {
     "editable": true
    },
@@ -2311,7 +2233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec48e2fd",
+   "id": "eec8df19",
    "metadata": {
     "editable": true
    },
@@ -2323,7 +2245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "462f1772",
+   "id": "313f9634",
    "metadata": {
     "editable": true
    },
@@ -2333,7 +2255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dabcb01d",
+   "id": "83b87f63",
    "metadata": {
     "editable": true
    },
@@ -2345,7 +2267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be2a6bd1",
+   "id": "be859efd",
    "metadata": {
     "editable": true
    },
@@ -2355,7 +2277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb37d8f8",
+   "id": "a976e227",
    "metadata": {
     "editable": true
    },
@@ -2367,7 +2289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de325e18",
+   "id": "9f1dc803",
    "metadata": {
     "editable": true
    },
@@ -2383,7 +2305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c68a0c1",
+   "id": "4fe2c4e9",
    "metadata": {
     "editable": true
    },
@@ -2395,7 +2317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3492a4b",
+   "id": "bd8e264d",
    "metadata": {
     "editable": true
    },
@@ -2406,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "207b9ead",
+   "id": "9a0e9ac7",
    "metadata": {
     "editable": true
    },
@@ -2418,7 +2340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d74c7ddc",
+   "id": "9b20bc14",
    "metadata": {
     "editable": true
    },
@@ -2432,7 +2354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a02ea08c",
+   "id": "5fae8c2f",
    "metadata": {
     "editable": true
    },
@@ -2444,7 +2366,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9863119",
+   "id": "c955d871",
    "metadata": {
     "editable": true
    },
@@ -2469,7 +2391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92ca8aa6",
+   "id": "10feca33",
    "metadata": {
     "editable": true
    },
@@ -2485,14 +2407,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "32b99126",
+   "execution_count": 27,
+   "id": "cd57c0cd",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2533,7 +2452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "185af862",
+   "id": "ed4e2094",
    "metadata": {
     "editable": true
    },
@@ -2550,13 +2469,10 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "2ae9db3d",
+   "id": "309536df",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2574,7 +2490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ade787b",
+   "id": "27597f0a",
    "metadata": {
     "editable": true
    },
@@ -2587,14 +2503,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "e0d18716",
+   "execution_count": 29,
+   "id": "1c60fe5f",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2620,7 +2533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd819c63",
+   "id": "36972bcb",
    "metadata": {
     "editable": true
    },
@@ -2639,38 +2552,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "fc0346bc",
+   "execution_count": 30,
+   "id": "85ffeaf7",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            N    Z    A Element  Ebinding\n",
-      "A                                        \n",
-      "4   0       1    3    4      Li  1.153760\n",
-      "5   2       3    2    5      He  5.512132\n",
-      "6   7       3    3    6      Li  5.332331\n",
-      "7   12      4    3    7      Li  5.606439\n",
-      "8   17      4    4    8      Be  7.062435\n",
-      "...       ...  ...  ...     ...       ...\n",
-      "264 3297  156  108  264      Hs  7.298375\n",
-      "265 3303  157  108  265      Hs  7.296247\n",
-      "266 3310  158  108  266      Hs  7.298273\n",
-      "269 3331  159  110  269      Ds  7.250154\n",
-      "270 3337  160  110  270      Ds  7.253775\n",
-      "\n",
-      "[264 rows x 5 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "A = Masses['A']\n",
     "Z = Masses['Z']\n",
@@ -2682,7 +2570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f70ca63",
+   "id": "799627e9",
    "metadata": {
     "editable": true
    },
@@ -2693,14 +2581,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "d56956d6",
+   "execution_count": 31,
+   "id": "365fbba9",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2715,7 +2600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b3df36c",
+   "id": "0d3c5663",
    "metadata": {
     "editable": true
    },
@@ -2725,14 +2610,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "d10ecf29",
+   "execution_count": 32,
+   "id": "60ba302b",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -2742,7 +2624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2289c78e",
+   "id": "af13881a",
    "metadata": {
     "editable": true
    },
@@ -2753,38 +2635,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "286afac2",
+   "execution_count": 33,
+   "id": "a5cba204",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 0.02\n",
-      "Variance score: 0.95\n",
-      "Mean absolute error: 0.05\n",
-      "[ 0.00000000e+00 -2.96611194e-02  2.01719003e-01  1.08078025e+01\n",
-      " -4.03097597e+01] 5.294399745619595\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The mean squared error                               \n",
     "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n",
@@ -2810,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fa56ca8",
+   "id": "db2d6c19",
    "metadata": {
     "editable": true
    },
@@ -2823,95 +2680,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "76f0ddd2",
+   "execution_count": 34,
+   "id": "66c27da6",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 1.73\n",
-      "Mean squared error: 19.48\n",
-      "Mean squared error: 12.11\n",
-      "Mean squared error: 23.42\n",
-      "Mean squared error: 0.23\n",
-      "Mean squared error: 0.18\n",
-      "Mean squared error: 0.29\n",
-      "Mean squared error: 0.20\n",
-      "Mean squared error: 0.26\n",
-      "Mean squared error: 4.07\n",
-      "Mean squared error: 2.12\n",
-      "Mean squared error: 7.54\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 15.78\n",
-      "Mean squared error: 122.02\n",
-      "Mean squared error: 51.22\n",
-      "Mean squared error: 152.55\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyQAAANbCAYAAACzflsQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABl90lEQVR4nO3dd3hU5brG4WdCEkIIoZPQCZ3QFRSVJsWGW0FA8QjSqwIiCgiIVBXpKFgowgYUhCAiitQtiNJBEEIvoScQQkKA1JnzRyQ6JuACMnwpv/u6ch2z1pqZd+bMnvDM+65v2RwOh0MAAAAAYICb6QIAAAAAZF0EEgAAAADGEEgAAAAAGEMgAQAAAGAMgQQAAACAMQQSAAAAAMYQSAAAAAAYQyABAAAAYAyBBAAAAIAxBBIAGdKgQYNUoUKF2/40atTonh5j6dKlqlChgs6cOePS29yr06dPq2LFinrooYcUExNz3x4XAIC0YHM4HA7TRQDAnTp16pQuX76c/Pv06dMVHBysTz75JHmbp6enAgMD7/oxLl++rFOnTikwMFCenp4uu829mjRpklatWqUzZ85o1KhRatGixX15XAAA0gKBBECmMGjQIG3btk3r1683Xcp9Zbfb1ahRIzVv3lzBwcGKjIzUokWLTJcFAIBljGwByNS2bt2qChUqaOHChXr88cf16KOPatOmTZKkxYsX64UXXlCNGjVUrVo1Pf/88/rxxx+Tb/vP8atBgwapQ4cOCgoK0pNPPqkqVaroueee04YNG+7pNpK0e/duvfLKK6pRo4YaNmyouXPnqkOHDho0aNBtn9+mTZt0/vx5Pf7443ruuef0+++/6+DBgymOCw8P1+DBg/Xoo4+qZs2aeuWVV7Rz587k/fHx8Zo2bZqaNGmiatWqqVmzZgoKCkre365dO7Vr1y7V13br1q3Jzz0wMFCLFy9W3bp1Vb9+fR05ckSJiYn64osv9Oyzz6patWqqUaOG2rRpo82bNzvd3759+9SlSxc9+OCDqlOnjvr166fz588rISFBdevWVf/+/VM8r6efflrvvPPObV8jAED6RiABkCVMmjRJAwcO1MCBA1WjRg0tWLBAw4YNU+PGjfX5559r3Lhx8vDw0Ntvv61z587d8n727dunWbNmqU+fPpo2bZrc3d3Vp08fRUZG3vVtjh07pg4dOkiSJk6cqN69e+uLL75wCgy3EhQUpICAAFWvXl1NmzaVr6+vvv76a6djrl+/rjZt2ui3335T//799cknnyhnzpzq0qWLjh07JkkaOHCgvvjiC7Vq1Uqff/65GjRooMGDB2vZsmX/WsPfJSYm6rPPPtPo0aP1xhtvqGzZsho/frymTZuml156STNnztTIkSMVERGhvn376vr165KkgwcP6uWXX9aNGzf04YcfauTIkQoODlanTp3kcDjUvHlzrV27VtHR0cmPtWfPHh0/flwvvPDCHdUIAEhf3E0XAAD3Q5s2bfTUU08l/3769Gl16tRJr732WvK2YsWK6YUXXtCuXbtUpEiRVO/n6tWrWrp0qUqUKCFJ8vb2Vtu2bbVlyxY9+eSTd3Wbzz//XD4+Ppo5c6Zy5MghSSpdurTatGlz2+d05coVrV+/Xr1795YkZc+eXc2aNdPy5cs1YMAA5cyZU5L07bff6vTp01q2bJkqVqwoSapVq5aaN2+u7du3y26364cfftCQIUP06quvSpIeeeQRnTt3Tlu3blXz5s1vW8c/9ejRQw0bNkz+PSwsTP369XPqsHh5eal37946dOiQatasqenTpyt37tyaPXu2smfPLkny9/fXG2+8oUOHDqlly5aaMWOGVq1apZYtWyY/rxIlSqhWrVp3VB8AIH0hkADIEipUqOD0+81RqKtXr+rkyZM6efJk8ghRfHz8Le8nX758ycFCSvpHsyTduHHjrm+zZcsWNWjQIDmMSFLNmjVVtGjR2z6n5cuXKyEhQY0aNVJUVJQk6cknn9TXX3+t77//PjnQ7NixQ8WKFUsOI1JSeFm5cqUkJXdUmjZt6nT/kydPvu3j30r58uWdfp8wYYKkpBP+Q0JCdOLEieRzfW6+1jt37lSDBg2Sw4gkVatWzemcoAcffFDfffedWrZsqbi4OP34449q3769bDbbXdUJAEgfCCQAsoT8+fM7/X7q1CkNGzZMW7Zskbu7u0qXLp0cWm631sffQ4Ok5H8M2+32u77N5cuXU9QnSQULFrzlfUpJ52zY7XY1a9Ysxb6FCxcmB5IrV66kev83XblyRVLK1+hu/fN+/vjjD40YMUJ//PGHvLy8VLZs2eSwdfO1/rcaJalVq1YaPHiwzp07pz179igqKooVxQAgEyCQAMhy7Ha7unXrJg8PD33zzTcKDAyUu7u7jh49quXLl9/3evz9/RUeHp5ie3h4uAICAlK9TXBwsA4cOKDXX39dDz30kNO+9evXa86cOdqzZ4+qV6+uXLlypXpdlN27d8vHx0e+vr6SkoLRze6NJB0/flyXL19OHolKTEx0uv3N8z9uJzo6Wl26dFGFChW0YsUKlSlTRm5ubtqwYYNWrVqVfFyuXLmclnG+acOGDapYsaL8/Pz01FNPafTo0Vq1apV2796tRx555JajdQCAjIOT2gFkORERETpx4oRatWqlatWqyd096buZjRs3Srp9t8MVateurY0bNyo2NjZ524EDB257ccUlS5bI09NTHTp00MMPP+z007lzZ2XLlk0LFy6UlHS+yOnTp3Xo0KHk28fFxal379765ptv9OCDD0qS1q5d6/QYkyZN0qhRoyRJPj4+unDhgtP+Xbt2/etzO378uK5cuaJXX31V5cqVk5tb0p+df77WtWrV0i+//KK4uLjk2x46dEjdunXTH3/8ISnp3JtnnnlGK1as0C+//EJ3BAAyCTokALKc/Pnzq2jRolqwYIH8/f3l6+urTZs2ae7cuZJufz6IK/To0UM//vijunTpok6dOikqKkpTpkyRzWZL9fyIuLg4/fDDD2rQoIFy5cqVYn+hQoX02GOP6ccff9Q777yjF154QfPmzVPPnj3Vt29f5cuXTwsWLFBMTIzatWunEiVK6KmnntL48eMVExOjypUra9OmTVqzZk3yeSSPP/641q9frzFjxqhJkybauXOnpRW4AgIC5OPjo88++0zu7u5yd3fXqlWrtGTJEkl/vda9evXSSy+9pK5du6p9+/aKi4vTlClTVLlyZdWvXz/5/lq1aqWXXnpJPj4+euKJJ+7i1QYApDd0SABkSdOnT5efn58GDRqkN954Q7///rs+/fRTlS5dWjt27LivtZQsWVKzZs1SbGys+vTpo0mTJqlr164qWLBg8kpZf7d27VpduXJFzz777C3vs0WLFoqJidG3334rHx8fzZ8/XzVr1tSYMWPUt29fxcbGat68eckn248bN06vvvqq5s2bp+7du2vTpk2aPHly8spkLVu2VNeuXfXjjz+qa9eu2rVrl6ZMmfKvzy1XrlyaPn26HA6H+vbtqwEDBujcuXOaP3++cubMmfxaBwYGat68ebLb7erXr59GjhypGjVqaMaMGU5XvK9Ro4by5s2rZs2aycvL645eZwBA+sSV2gHAsM2bN8vDw8Np+drIyEg99thjGjBgQPJSvJD27t2r1q1bKygoSFWqVDFdDgAgDTCyBQCG7d+/X1OnTtWbb76pypUrKyIiQrNnz1auXLlu2wXJSrZu3aqtW7dq2bJlqlOnDmEEADIRAgkAGNapUyfFxcXp66+/1vnz5+Xt7a2HHnpIY8eOVb58+UyXly5EREToyy+/VNmyZfXBBx+YLgcAkIYY2QIAAABgDCe1AwAAADCGQAIAAADAGAIJAAAAAGMIJAAAAACMyVKrbD3h0cZ0Ccgi3CqVNV0CsgjblWjTJSCLSPRnxTfcH6u3DTNdwi3ZL5Q3XUKq3PwPmy7hntAhAQAAAGAMgQQAAACAMVlqZAsAAAC4W3bZTZeQqozeYcjo9QMAAADIwAgkAAAAAIxhZAsAAACwINGRPke2Mvo/6OmQAAAAADCGQAIAAADAmIze4QEAAADuC7scpkvIlOiQAAAAADCGQAIAAADAGEa2AAAAAAvS64URMzo6JAAAAACMIZAAAAAAMIaRLQAAAMCCRAerbLkCHRIAAAAAxhBIAAAAABjDyBYAAABgARdGdA06JAAAAACMIZAAAAAAMIaRLQAAAMCCREa2XIIOCQAAAABjCCQAAAAAjGFkCwAAALCAVbZcgw4JAAAAAGMIJAAAAACMYWQLAAAAsCDRwciWK9AhAQAAAGAMgQQAAACAMYxsAQAAABbYTReQSdEhAQAAAGAMgQQAAACAMYxsAQAAABYkcmFEl6BDAgAAAMAYAgkAAAAAYxjZAgAAACxIZGLLJeiQAAAAADCGQAIAAADAGEa2AAAAAAu4MKJr0CEBAAAAYAyBBAAAAIAxjGwBAAAAFiTKZrqETIkOCQAAAABjCCQAAAAAjGFkCwAAALDAzoURXYIOCQAAAABjCCQAAAAAjGFkCwAAALCAVbZcgw4JAAAAAGMIJAAAAACMYWQLAAAAsICRLdegQwIAAADAGAIJAAAAAGMY2QIAAAAssDsY2XIFOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAAA0ffp0tWvX7pb7hw4dqkaNGjlts9vtmjp1qurVq6fq1aurU6dOCgkJuaPHJZAAAAAAWdycOXM0derUW+5fu3atFi9enGL79OnTtXDhQo0ePVqLFi2SzWZT165dFRcXZ/mxCSQAAACABYlyS5c/9yI0NFRdunTRlClTFBAQkOoxYWFhevfdd/XQQw85bY+Li9Ps2bPVu3dvNWjQQBUrVtSkSZMUGhqqNWvWWK6BQAIAAABkUfv371fu3Lm1fPlyVa9ePcV+h8OhQYMG6fnnn08RSA4ePKhr166pTp06ydt8fX0VGBio7du3W66BQAIAAABkUY0aNdKECRNUvHjxVPfPmTNHFy9e1Jtvvpli34ULFyRJhQsXdtpeqFAhnT9/3nINrLIFAAAAWJBeL4zYuHHj2+5ft27dXd3vwYMH9cknn2jBggXy9PRMsf/GjRuSlGJf9uzZFRkZaflx6JAAAAAAcBIbG6u33npLPXv2VMWKFVM9xsvLS5JSnMAeGxurHDlyWH4sOiQAAABABna3HZDb2bNnj44cOaJPPvlE06ZNkyTFx8crISFBNWvW1IgRI1SqVClJSSe9lyhRIvm2YWFhtwwxqSGQAAAAABZkpQsjVqtWTatXr3baNm/ePK1evVrz5s1T/vz55enpKR8fH23dujU5kERFRSk4OFht27a1/FgEEgAAAABOvLy8VLJkSadtuXPnlru7u9P2tm3bavz48cqXL5+KFi2qcePGyd/fX02bNrX8WAQSAAAAAHelT58+SkhI0NChQxUTE6PatWtr1qxZqZ4Efys2h8PhcGGN6coTHm1Ml4Aswq1SWdMlIIuwXYk2XQKyiET/fKZLQBaxetsw0yXc0i8n0+ff93qljpou4Z6wyhYAAAAAYwgkAAAAAIzhHBIAAADAAjvf5bsEryoAAAAAYwgkAAAAAIxhZAsAAACwICtdGPF+okMCAAAAwBgCCQAAAABjGNkCAAAALEh08F2+K/CqAgAAADCGQAIAAADAGEa2AAAAAAvsrLLlEnRIAAAAABhDIAEAAABgDCNbAAAAgAWJfJfvEryqAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgavKoAAAAAjKFDAhUsll+f7/5Iw1tO0N6Nwake0/TVBnp7Vs9b3se4TtO1Zt5GV5WITKiAX259FvS6Rr7xlfbuOJG8/aH6FfRK98cVUN5PUVduaNPafZr78VrduB5nsFpkZAUK59GnqwdqZNdZ+mPL0eTtVeuUVbs3n1KpSkUUH5ugAztPaNb73+t8yCWD1SIjeqb5A3qudW0VLppXVyKuafPGQ/rvFz/r+rWkz61HG1TQK53qqXipAoq8cl2rV+zR11/+ooQEu+HKgfSBQJLFFSpRQB/88I588uS87XHbftylPnWHOm2z2Wzq92k3efvm0LaVu11ZJjKZQoXzaMxn7eXjm8Np+6ONKmnoxJe1d8dJvf/2ImVzd9PLXRtq7MxOeqPdF7In8scbd6ZQ0bwaPa+HfHI7v9cqPlBK7y/oqS1r9mlcn3nKnsNTbXo/ofFBfdSz6VhFRVwzVDEymtbtHlWnno20eP5v2r39hIoUz6f23RuqVJlCGvT6fNV+tKyGjX1Rq1f8rpmfrFPxUgXUqVcj5S/go8kf/GC6fNwhO8NFLkEgyaJsNpuavlpf3ca2tXR85KWrirx01Wlb895Pq3iloupXf1iKfUBqbDabmjxXU137P5Xq/rY9G+nU8Ysa2mOuEhISJUn7doVozo9v6onmD+inoB33s1xkYDabTU1a1VaXoc+nuv+l15ro9NFQvd9zjhwOhyRp/44TmrflPTVt/ZCCvvjf/SwXGZTNJrVp/5h++HanZk9fL0navf2EoiKv690PWqtcpcJq06GuDu0/q4mjv0/enzuPt17uWFefTVqtmJh4k08BSBeIeVlUQLUS6vNJZ62Zt1FjO0y749vn9cutDiNe1IrP1+jgtqP/fgNAUkB5P/Ue+h+tXb5b4wYvSbG/eOmC2vnrkeQwIkmRl6/p9PGLerh+hftZKjK4gEpF9PqY1lq7ZLvGvzE/xf5Dv4do2awNyWFEkiLConQ9OkaFSxa4n6UiA/POmV3rfvpD61ftc9p+9tRlSVKRonk1fsR3GjfiO6f98fGJcnNzUzZ3/hkGSHRIsqyLpy6pQ8U3dOnsZVWrH3jHt28//EXZE+2aM2yRC6pDZhV2PlKdnp2kS6FRqlYrIMX+yMvX5Vc0r9O2bO5uKlg4jzw8+biCdWFnI9S5/mhduhCpqnXKpti/8OM1KbZVe6SscuXJqZOHzt+PEpEJXIuO1fTxP6XY/ljDipKkk8cv6vzZiOTt3jmz64GHAtTqlUe0/qc/dC069r7VirSR6LCZLiFTMv4XPiEhQatXr9aOHTt07tw5xcXFKUeOHPL391etWrXUtGlTubsbLzPTuRpxTVfvckY6T0FfNWlbT0smrtC1yOtpXBkys+ioG4qOunHL/Wu+26WXuzVU6471tHrZTnlm91D73k3knTM7J7XjjkRHXld0pPXjc+fLqb5jX9LFcxFau2Sb6wpDphdYtZheevUx/frzQYUcv5i8PX/BXPr6h36SpPNnIzRvxgZTJQLpjtFe4alTp9SsWTMNHjxYBw8elJeXlwoWLCgPDw8dOHBA77zzjp577jmdO3fOZJn4h6c7N5LNzU3ffrzSdCnIZOZ9ul6LZm3Uq6831qINgzX7h366cS1Wm/93QLE3CCRwjXx+vvpg4WvKkz+XRnebrRjCL+5SlRolNHry/+nc2cuaOHq5076YG3Ea0Ou/GjFgkaIir+uTuV1UIoDxQEAy3CEZMWKEihUrpiVLlihXrlwp9kdFRalfv34aOXKkPvvsMwMVIjX1Wj6snWv2ciI70pw90a4vp6zW/E/Xq3CxvAq/eFXXrsboo9mddTXy1p0V4G6VqlBYI+Z0U46cnhr66mc6vPe06ZKQQTVsWllvDXtep09d0uA+C3Q1KsZp/7XoWP2+46Qkac/OEP13WR+98HIdTX5/hYFqgfTFaIdk586dGjBgQKphRJJ8fX319ttva/v27fe5MtxKgaL5VLZGgDYu2Wy6FGRCVR8spQcfLav4uASdOn5R167GyC2bmwLK++voATqlSFvVHy2nCUv7ymaT3m71sQ7sPGm6JGRQrds+okGjXtCBfWfUv9tcRYQnjUS7ZbOpQZNAlSnv73R89NUYnT9zWQX9fE2Ui3uQKLd0+ZPRGX0Gvr6+CgsLu+0x586dk5eX132qCP+mQu0ykqT9vx02XAkyo3pPVFHf95o7rTzzZPMHlMs3h35dn/pFO4G7UaZyUQ2f3UVh5yLU7/nJCjl8wXRJyKCatXhAXfs01cZ1wXqn93xdv/bXier2RIe69G6iLq83drpNQT9fFQ8oqONHQu93uUC6ZHRkq1WrVnrnnXfUp08fPfzwwypcuLA8PT0VFxen0NBQbdu2TePHj1erVq1MlpkleefKoRKBRXX+WKjTaFZAlRKKi4nT+eN8iCLt/bB4m55qWUtvjW6pVd/uVEA5f3Xq94R+XrlX+3eFmC4PmcgbH7VRNvdsWjDpJxUskkcFi+RJ3hd5OVrnQ8LNFYcMI2/+nOre70ldOHdF332zTWUrFnbaf/5M0snrbw17Xm8MflYb1uxX/oK59Ern+roaeV1BC5g2ACTDgaR3795yc3PT2LFjdf16ytWacubMqVdeeUV9+/Y1UF3WVrZmgMavG6ZxnT/Vmv/+tRJIXr/cir7CylpwjZCjYXrv9Xnq2PcJDZ/aVhHh0Vo4Y4MWzmQ1GqQd/xL5VbZqcUnSkM86pti/ZvE2Tez/1f0uCxnQQ4+Wk5eXh/yL5NGkGSnfS+NGfKfVK/boxo04vfTqY2r0ZBXFxMRr++ajmj1tva5E8Pc0o7E7Mv54VHpkc/z9qlCGxMfH68CBAwoNDdWNGzfk5eUlf39/VaxYUZ6enmn2OE94tEmz+wJux61SyuseAK5guxJtugRkEYn++UyXgCxi9bZhpku4pUVHa5suIVUvlc3Y51uniwt8eHh4qFq1aqbLAAAAAHCfpYtAAgAAAKR3mWFFq/SIVxUAAACAMQQSAAAAAMYwsgUAAABYkOiwmS4hU6JDAgAAAMAYAgkAAAAAYxjZAgAAACyw812+S/CqAgAAADCGQAIAAADAGEa2AAAAAAsSHXyX7wq8qgAAAACMIZAAAAAAMIaRLQAAAMACu7gwoivQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa/CqAgAAADCGQAIAAADAGEa2AAAAAAsS+S7fJXhVAQAAABhDIAEAAABgDCNbAAAAgAV2BxdGdAU6JAAAAACMIZAAAAAAMIaRLQAAAMACVtlyDV5VAAAAAMYQSAAAAAAYw8gWAAAAYIHdwXf5rsCrCgAAAMAYAgkAAAAAYxjZAgAAACxIFBdGdAU6JAAAAACMIZAAAAAAMIaRLQAAAMACVtlyDV5VAAAAAMYQSAAAAAAYw8gWAAAAYAGrbLkGHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQavKgAAAABjCCQAAAAAjGFkCwAAALAgkZEtl+BVBQAAAGAMgQQAAACAMYxsAQAAABbYuTCiS9AhAQAAAGAMgQQAAACAMYxsAQAAABawypZr8KoCAAAAMIZAAgAAAMAYRrYAAAAAC+wOVtlyBTokAAAAAIwhkAAAAAAwhpEtAAAAwIJEvst3CV5VAAAAAMYQSAAAAABo+vTpateundO29evXq2XLlqpZs6YaNWqksWPHKiYmJnm/3W7X1KlTVa9ePVWvXl2dOnVSSEjIHT0ugQQAAACwwO6wpcuftDBnzhxNnTrVaduOHTv0+uuv68knn9SyZcs0fPhwrVy5UiNGjEg+Zvr06Vq4cKFGjx6tRYsWyWazqWvXroqLi7P82AQSAAAAIIsKDQ1Vly5dNGXKFAUEBDjtW7hwoerUqaNu3bqpZMmSql+/vvr166fly5crLi5OcXFxmj17tnr37q0GDRqoYsWKmjRpkkJDQ7VmzRrLNRBIAAAAgCxq//79yp07t5YvX67q1as77evUqZMGDBiQ4jYJCQmKjo7WwYMHde3aNdWpUyd5n6+vrwIDA7V9+3bLNbDKFgAAAGCBPRN+l9+oUSM1atQo1X2BgYFOv8fFxenLL79U5cqVlS9fPu3YsUOSVLhwYafjChUqpPPnz1uugUACAAAAZGCNGze+7f5169bd82MkJCRowIABOnr0qBYsWCBJunHjhiTJ09PT6djs2bMrMjLS8n1nvpgHAAAAIM1ER0erR48eWrdunaZOnZo82uXl5SVJKU5gj42NVY4cOSzfPx0SAAAAwILENFrRKq2lRQfkVsLCwtS1a1edOXNGM2bMcDpf5OaoVlhYmEqUKOF0m4oVK1p+DDokAAAAAFKIjIxU+/btdfnyZX311VdOYUSSKlasKB8fH23dujV5W1RUlIKDg1WrVi3Lj0OHBAAAAEAKH3zwgU6fPq2ZM2cqX758unjxYvK+fPnyydPTU23bttX48eOVL18+FS1aVOPGjZO/v7+aNm1q+XEIJAAAAIAFaXURwozAbrfrxx9/VHx8vNq3b59i/7p161SsWDH16dNHCQkJGjp0qGJiYlS7dm3NmjUrxYnut2NzOByOtCw+PXvCo43pEpBFuFUqa7oEZBG2K9GmS0AWkeifz3QJyCJWbxtmuoRb6rv7ZdMlpGpKza9Nl3BPOIcEAAAAgDGMbAEAAAAW2B18l+8KvKoAAAAAjCGQAAAAADCGkS0AAADAgkRlnVW27ic6JAAAAACMIZAAAAAAMIaRLQAAAMCCrHRhxPuJDgkAAAAAYwgkAAAAAIxhZAsAAACwgAsjugavKgAAAABjCCQAAAAAjGFkCwAAALDAzoURXYIOCQAAAABjCCQAAAAAjGFkCwAAALAgkQsjugQdEgAAAADGEEgAAAAAGMPIFgAAAGABF0Z0DV5VAAAAAMYQSAAAAAAYk6VGthyJiaZLQBaRuO+Q6RIAIG2dOWu6AsA4O6tsuQQdEgAAAADGEEgAAAAAGJOlRrYAAACAu2UXI1uuQIcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYYHfwXb4r8KoCAAAAMIZAAgAAAMAYRrYAAAAAC1hlyzXokAAAAAAwhkACAAAAwBhGtgAAAAAL7GJkyxXokAAAAAAwhkACAAAAwBhGtgAAAAALWGXLNeiQAAAAADCGQAIAAADAGEa2AAAAAAsY2XINOiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALCAkS3XoEMCAAAAwBgCCQAAAABjGNkCAAAALLCLkS1XoEMCAAAAwBgCCQAAAABjGNkCAAAALGCVLdegQwIAAADAGAIJAAAAAGMY2QIAAAAsYGTLNeiQAAAAADCGQAIAAADAGEa2AAAAAAsY2XINOiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALDAwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABbYxciWK9AhAQAAAGAMgQQAAACAMYxsAQAAABZwYUTXoEMCAAAAwBgCCQAAAABjGNkCAAAALODCiK5BhwQAAACAMQQSAAAAAMYwsgUAAABYwCpbrkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjAKluuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABY4HCYriBzokMCAAAAQNOnT1e7du2cth04cEBt27ZVjRo11LBhQ82aNctpv91u19SpU1WvXj1Vr15dnTp1UkhIyB09LoEEAAAAyOLmzJmjqVOnOm2LiIhQx44dVapUKQUFBal3796aMmWKgoKCko+ZPn26Fi5cqNGjR2vRokWy2Wzq2rWr4uLiLD82I1sAAACABXZlvlW2QkNDNWTIEO3cuVMBAQFO+7755ht5enpq+PDhcnd3V5kyZRQSEqIZM2aoZcuWiouL0+zZs/X222+rQYMGkqRJkyapXr16WrNmjZo1a2apBjokAAAAQBa1f/9+5c6dW8uXL1f16tWd9u3YsUO1a9eWu/tfPYw6deroxIkTCg8P18GDB3Xt2jXVqVMneb+vr68CAwO1fft2yzXQIQEAAACyqEaNGqlRo0ap7rtw4YLKly/vtK1QoUKSpHPnzunChQuSpMKFC6c45vz585ZrIJAAAAAAFjjS6YURGzdufNv969atu6v7jYmJkaenp9O27NmzS5JiY2N148YNSUr1mMjISMuPw8gWAAAAgBS8vLxSnJweGxsrSfL29paXl5ckpXpMjhw5LD8OHRIAAAAgA7vbDsi/8ff3V1hYmNO2m7/7+fkpISEheVuJEiWcjqlYsaLlx6FDAgAAAFhgd9jS5Y+r1K5dWzt37lRiYmLyts2bNysgIED58+dXxYoV5ePjo61btybvj4qKUnBwsGrVqmX5cQgkAAAAAFJo2bKloqOjNWTIEB09elRLly7V3Llz1b17d0lJ5460bdtW48eP17p163Tw4EH169dP/v7+atq0qeXHYWQLAAAAQAr58+fXzJkzNWbMGLVo0UIFCxbUgAED1KJFi+Rj+vTpo4SEBA0dOlQxMTGqXbu2Zs2aleJE99uxORwOhyueQHrU1K216RIAAABwG2vsi02XcEvVV7xruoRU7Xl2lOkS7gkjWwAAAACMIZAAAAAAMIZzSAAAAAAL0uuFETM6OiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALDAzsiWS9AhAQAAAGAMHZIspNaTNdRxVBuVCCymyItRWvH5ai38cNktj3f3cFer/s+q6asNVbB4fl06c1nrv/pFCz9cpoT4hOTjnu7SWC/0babCpf0UduqSvv90lb6d+uN9eEZIr+70vfZ35R4orambx6hD+T4KDbnotO/R52vrlaGtVLxCEV2+cEVr52/Qwg+c34/IWlz1ufZE+4Zq1f8/KlrWX+Hnr2jtvA1aMDpIiQmJ9+FZISNw1XsPyIoIJFlE4CPlNfK7gdqw6Dd9+e5CValbUR1Hvyw3Nzd99f7SVG/Tc3IHNX21gRaMDtLh7UdV9oHSavdeaxUqWVATu3wqSXq2xxPqO72rFo5dpl1r9qjiw+XUffyr8sqZXV9/8O39fIpIJ+7mvXZT6WolNXrFO3L3SPnR9ECTanov6C1tWPSbZr2zQAFVS6jTmJeVp2BufdJ7lqueDtIxV32utejzjHpN7qiNizdrxoB5yl3AV68Of1EBVUtqRMtx9/MpIp1y1XsP6Z/DYbqCzIlAkkW0G9Zax34/qbHtP5Yk7Vj1u9w9sumlgc21ZOIKxcXEOR2fK6+Pnu3eVDMHLdDi8cslSbvX75MkdfuonWYNWqDIS1FqM7C5fv7zH4g3jylWroief/1pAkkWdafvNSnpm8PmvZ9S+5FtFHcj5X5JerLD4wo7dUkftvtYdrtdu9buVZ5CufXCG830ab85fHOdBbnic+3q5Wi1HdZaO1fv0aiXJibf9vDO45q1f5IeaFJNu9buvU/PEOmVq/6mAlkV55BkAR6e7qrWsLI2fbvVafvGJVvknSuHqtarmOI2OXN7a8Xna7R5+Q6n7WcOn5MkFS5dSJI0+OkxmjFwntMx8fEJ8sjukZZPARnE3bzXJOmhZ2qq7bDW+vr9pZo5aH7q953dXTHXYmW325O3RV6Kkmd2D3nnypF2TwIZgqs+1/L65ZZvPh9tXuF8zKkDZ3TlYpTqPPtgGj8TZDSu/JsKZFUEkiygcGk/eWb30Nk/P/huOnf0giSpWPkiKW5z4WSYPn5tZvKH5U11Wzys+LgEnTl8XpJ06uBZhZ26JCnpG6CnOzdS03b1tXz6T654Kkjn7ua9JkmHth9Tu4DX9NX7S5WYYE/1mO+m/aSi5Qqr9VvPKWdub1V6uJxe6NtMW3/YpasR0Wn7RJDuuepzLfrKNSXEJ8i/lPM/EH3y5FSuvDlTbEfW48q/qUj/HA5buvzJ6BjZygJy5skpSboWdcNp+/WrSb97+1r7drnuCw+rSbv6+nbqj4q+cs1pX+VHK2jyptGSpMM7jum7j1fea9nIgO72vRZ+7vK/3veen/frm3HfqdtH7dTto3aSpCO7juv9V6bcS8nIoFz5ufbzot/03GtP6eT+0/r1223KUyi3ek3uqIT4RHnlzJ6GzwIZ0f34mwpkNXRIsgA3tz+T8y3OxLLb//0MrXot6+idBX21d0OwZg1akGL/hRNh6t/wPb3/yhTlzO2tadvHKk+h3PdUNzKetHiv3Urfz7rpxbef1/zRS9T/8fc0vtM05S7gqw9WDlH2HJ53fb/ImFz5uTal5wytW/CL3pzRQ99enqPpO8cqeMshHdp+VDHXYtOkfmRc9+NvKpDVGO+QtGvXTjabtVbTf//7XxdXkznd/ObF29fbafvNuftrkddve/uW/Z5V14/aae/P+zWs+UeKj0u5PGH4+QiFn4+QJB3cekRzDk/VM10a/+uqSshc7vW9div5i+TTM10a6+sPvtXcYYskSXs3BOvQ9mOa8cdEPdWpkb6bxphgVuLKz7WYazGa2OVTTe/7pfxKFlDoyYuKuR6rpzo20p7j+9P4mSCjuR9/U5F+ZYbxqPTIeCB55JFH9PHHH6t06dKqVq2a6XIypXPHQpWYkKgiZf2dtt/8/VTwmVve9rWpndT89af186Lf9FH7j50+OHP4eOmR52rp4NajOnfsQvL288dDFR1xTQWL50/jZ4L07l7ea7dTqEQBubm5af+vB522n9x/WpGXolSycvG7KxgZlqs+1yTp4WYPKDrimvb/dkghf95PnoK+Klg8v47sPp7GzwQZjSvfe0BWZTyQ9OrVS97e3po6dao+//xzFStWzHRJmU58bLz2bjygui0eTl5uUJLqt6qjqxHROrjtaKq36/T+/6n5609ryaQV+rz/3BT77Yl29Z/ZU2vmbdTk7p8nby9fq4x88+fSsT0haf9kkK7d7Xvt35w7ekGJCYmqWq+Stv/0e/L2YuWLKHcBX104EXqvpSODcdXnmiQ92/0J+ebPpb6PDUne9sIbzWRPtGvLip1p+0SQ4bjyvQdkVcYDiSR16NBBmzZt0uTJkzV+/HjT5WRKX40J0tg17+rdRW/qpy/XK/DRCmr91nOaOWiB4mLi5J0rh0oGFtO5Y6GKvBSlMtVL6aUBz+vQ9qPa+M1vqvRwOaf7Cwk+o+tXb2jRR9/plaEtFRV+VbvX7lXR8kX06ntJ67Ov+vJ/hp4tTLrT95oVkZeitHTKD2r91nOSpJ1r9sqvZEG1HdZaoSEX9eOMda58SkinXPW5tuzjH/XhqnfVc1IHbV6+QzUaVdHL77ygrz/8VhdOhBl6tkhPXPXeQ/rHdRFdw+ZwpI9rToaGhio4OFiPP/64yx6jqVtrl913RvBY84f06vAXVaxCEYWfvazl03/SkokrJEnVGgRqwv9GaFzHaVo992e1H/GS2r7b6pb31f/x97R3Q7BsNpuadW+q53o+qSJl/XX1crQ2fbtVXw5dqOtRd3e+ADK+O3mv/dMT7Rvq7S9fU9uAXgoNuei0r0XfZ/Rs9yfkH1BIl89HaOeavfpyyNdcUCwLc8XnmiQ93uYx/d+QlvIPKKSwkIta/ukqffcJ5ynhL65670FaY19suoRbqrB0pOkSUnXohWGmS7gn6SaQ3A9ZPZAAAACkdwSSO5fRA0m6GNkCAAAA0jtW2XINrkMCAAAAwBgCCQAAAABjGNkCAAAArMgyZ17fX3RIAAAAABhDIAEAAABgDCNbAAAAgAWssuUadEgAAAAAGEMgAQAAAGAMI1sAAACABQ5W2XIJOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIALI7oGHRIAAAAAxhBIAAAAABjDyBYAAABgBSNbLkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjg4MKILkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjBKlsuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYwSpbLkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjCKluuQIcEAAAAgDEEEgAAAADGMLIFAAAAWMEqWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFY4uDCiK9AhAQAAAGAMgQQAAACAMYxsAQAAABY4WGXLJeiQAAAAADCGQAIAAADAGEa2AAAAACsY2XIJOiQAAAAAjCGQAAAAADCGkS0AAADACi6M6BJ0SAAAAAAYQyABAAAAYAwjWwAAAIAFNlbZcgk6JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIILI7oEHRIAAAAAxhBIAAAAABhDIAEAAACscKTTn3sQHx+vSZMmqWHDhqpZs6b+7//+T7t27Uref+DAAbVt21Y1atRQw4YNNWvWrHt7wFQQSAAAAIAs6tNPP1VQUJBGjx6tZcuWqXTp0uratatCQ0MVERGhjh07qlSpUgoKClLv3r01ZcoUBQUFpWkNnNQOAAAAZFHr1q3Ts88+q7p160qSBg0apMWLF+v333/XyZMn5enpqeHDh8vd3V1lypRRSEiIZsyYoZYtW6ZZDXRIAAAAACtMj2a5YGQrT548+t///qczZ84oMTFRixYtkqenpypVqqQdO3aodu3acnf/q4dRp04dnThxQuHh4ff2wH9DhwQAAADIooYMGaJ+/fqpcePGypYtm9zc3DRlyhSVKFFCFy5cUPny5Z2OL1SokCTp3Llzyp8/f5rUQCABAAAAMrDGjRvfdv+6detuue/YsWPy9fXVtGnT5Ofnp8WLF2vgwIGaP3++YmJi5Onp6XR89uzZJUmxsbH3XvifCCQAAACAFfc4HpXenD17Vm+//bbmzJmjWrVqSZKqVq2qo0eP6uOPP5aXl5fi4uKcbnMziHh7e6dZHQQSAAAAIAO7XQfkdvbu3av4+HhVrVrVaXv16tW1ceNGFSlSRGFhYU77bv7u5+d3d8WmgpPaAQAAgCyocOHCkqRDhw45bT98+LBKliyp2rVra+fOnUpMTEzet3nzZgUEBKTZ+SMSgQQAAACwxmFLnz93qVq1aqpVq5YGDhyoLVu26OTJk5o8ebI2b96sbt26qWXLloqOjtaQIUN09OhRLV26VHPnzlX37t3T8EVlZAsAAADIktzc3DR9+nRNnjxZ77zzjiIjI1W+fHnNmTNHNWrUkCTNnDlTY8aMUYsWLVSwYEENGDBALVq0SNM6bA6HI5OdnnNrTd1amy4BAAAAt7HGvth0CbdUatoE0yWk6uRr/U2XcE/okAAAAAAW2LLM1/j3F+eQAAAAADCGQAIAAADAGEa2AAAAACsY2XIJOiQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGPu6KT2ixcvatq0aTp9+rQKFCigSpUqqUqVKqpcubJy5MjhqhoBAAAAZFJ3FEgGDx6sTZs2qVy5cjpz5oy+//57ORwOubm5qXTp0qpSpYqqVq2qqlWrqmLFivLw8HBV3QAAAMB9xYURXeOOAsnu3bv19ttvq1OnTpKk69eva//+/frjjz/0xx9/aPv27fr2228lSZ6entq7d2/aVwwAAAAg07ijQJI9e3YFBgYm/+7t7a3atWurdu3ayduuXLmivXv3at++fWlXJQAAAIBM6Y4CSZMmTRQcHKw6derc8pg8efKofv36ql+//j0XB2RUbpxThfvEEZ9gugRkEW6+PqZLAMxz2ExXkCnd0SpbLVu21MqVK3X06FFX1QMAAAAgC7mjDsmLL74om82m1q1b66mnnlK9evVUuXJllSxZ0lX1AQAAAMjE7iiQjB49WgcOHND+/fu1cuVKffvtt7LZbMqZM6cCAwNVpUoVDRgwwFW1AgAAAOawypZL3FEgadWqVfJ/2+12HTt2TPv379e+ffsUHByshQsXEkgAAAAAWHZHgeTv3NzcVK5cOZUrV07NmzeXJDkcxEYAAAAA1t11IEmNzcbKAwAAAMik+O7dJe5olS0AAAAASEsEEgAAAADGpOnIFgAAAJBZ2RjZcgk6JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIKRLZegQwIAAADAGAIJAAAAAGMY2QIAAAAs4MKIrkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjhsJmuIFOiQwIAAADAGAIJAAAAAGMY2QIAAACsYJUtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACzgwoiuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMHIlkvQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGABF0Z0DTokAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjGGVLQAAAMAKVtlyCTokAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgadEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMCFEV2DDgkAAAAAYwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACxglS3XoEMCAAAAwBgCCQAAAABjGNkCAAAArGBkyyXokAAAAAAwhkACAAAAwBhGtgAAAAArGNlyCTokAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgadEgAAAAAGEMgAQAAAGAMgQQAAACwwpFOf+7RsmXL9Mwzz6hq1apq1qyZVq5cmbzvwIEDatu2rWrUqKGGDRtq1qxZ9/6A/0AgAQAAALKo7777ToMHD9ZLL72kFStW6JlnntGbb76p3bt3KyIiQh07dlSpUqUUFBSk3r17a8qUKQoKCkrTGjipHQAAAMiCHA6HpkyZovbt26t9+/aSpNdee027du3Stm3btG3bNnl6emr48OFyd3dXmTJlFBISohkzZqhly5ZpVgcdEgAAAMAK06NZaTyydfz4cZ09e1b/+c9/nLbPmjVL3bt3144dO1S7dm25u//Vw6hTp45OnDih8PDwu3/gf6BDAgAAAGRgjRs3vu3+devWpbr95MmTkqTr16+rc+fOCg4OVrFixdSzZ081atRIFy5cUPny5Z1uU6hQIUnSuXPnlD9//nsvXnRIAAAAgCwpOjpakjRw4EA9++yzmj17th577DH16tVLmzdvVkxMjDw9PZ1ukz17dklSbGxsmtVBhwQAAACwIL1eGPFWHZB/4+HhIUnq3LmzWrRoIUmqVKmSgoOD9eWXX8rLy0txcXFOt7kZRLy9ve+hYmd0SAAAAIAsyN/fX5JSjGWVLVtWZ86ckb+/v8LCwpz23fzdz88vzeogkAAAAABZUGBgoHLmzKk9e/Y4bT98+LBKlCih2rVra+fOnUpMTEzet3nzZgUEBKTZ+SMSgQQAAACwxvRqWmm8ypaXl5e6dOmiadOmacWKFTp16pQ+/fRT/frrr+rYsaNatmyp6OhoDRkyREePHtXSpUs1d+5cde/e/e4fNBWcQwIAAABkUb169VKOHDk0adIkhYaGqkyZMvr444/18MMPS5JmzpypMWPGqEWLFipYsKAGDBiQfL5JWrE5HI50enpO2mvq1tp0CUbVerKGOo5qoxKBxRR5MUorPl+thR8uu+Xx7h7uatX/WTV9taEKFs+vS2cua/1Xv2jhh8uUEJ+QfFzxCkXUdWw7VWsYqMT4RO3deECfvzVXF06E3fK+Mzu3HDlMl5BuvftVH5WtUVLtA/tbOr5sjVKa8vMwdao2QKGnLrm4uozH8bf/LUJ6pnMjPdfrCRUOKKQrYVHavGKn/jtiia5fvfGvty1XM0BTNo1Qx8A3FRrCe+2f3Hx9TJeQLlR7rLw++u6tW+6fN3a5FoxbkWJ7sbL+mrllZIrtp49cUNdHhqVpjRndT5e+MF3CLVV5e5LpElK1b1w/0yXcEzokWUTgI+U18ruB2rDoN3357kJVqVtRHUe/LDc3N331/tJUb9Nzcgc1fbWBFowO0uHtR1X2gdJq915rFSpZUBO7fCpJKlgsvyZvGq3Th87pg1emKHsOT3UY9bI+XPWuulXrr7iYuFTvG1lTozaPqu7ztXQh5KKl40tXLa5RS9+UuwcfVfh3rfs/q06jXtLiiSu0e/1+FSnjp/bDW6tU5eIa9PT7t71t6WolNOq7t3mv4V8d3XNKbzz5QYrt7Qc3V/mapfTz0m2p3q5M1eKSpAHPj1dcTHzy9ti//TfSv/S6ylZGxydvFtFuWGsd+/2kxrb/WJK0Y9XvcvfIppcGNteSiStSBIdceX30bPemmjlogRaPXy5J2r1+nySp20ftNGvQAkVeitKrI17U9as3NLDpSMXeSLqPCyfCNPK7gSpfq7T2bTp4H58l0rN8/nnUa1xbXTzz71d2dffIpud7NtWr77ZU3A3+WOPf2Ww2tRnwvH6YsU6zhy6SlPSZFXU5Wu9+3VflHgjQkV0nUtzO3SObnn/tSbUf3lpxN/gCBf/uenSMDu50fi/Vebq6ajaopNEdP9PZY6lPB5SuUkwXQi5p76+H70eZQIbCSe1ZgIenu6o1rKxN32512r5xyRZ558qhqvUqprhNztzeWvH5Gm1evsNp+5nD5yRJhUsnXaWzbouH9dPs9clhRJIO7zyuNsW6E0bgpN/0ztq5bp92/xz8r8fWfrK6XnmnhRZ+tFyz3l10H6pDRuftm0Prvtqk9Qt/c9p+9sh5SVKRMqkvT/nQ0zXVdugL+vrDZZo5+GuX14nMx9PLQ70+eFlbV+/Vpu933fK4MlWK69i+0/exMiDjIJBkAYVL+8kzu4fO/hkmbjp39IIkqVj5Iiluc+FkmD5+bWZyALmpbouHFR+XoDOHz8u/VCH55Mmp0JMX1fuTzgq6OFs/XF+gkd8NVMHiBVz3hJDhPNW+gcrVLKVp/f9r6fjDO0+ofeCb+nrc905LDQK3ci3yuqb3m6vgzc7fPj/WvLYk6eT+1P8heGjHMb1arq++/vA7JSbYXV4nMp8WPZoov39ufTbk9l+elK5SXDl9c2jSyoFafmaavto/Th3fbaFs7tnuU6VIE6ZX00rjVbbSC+OB5MSJE/r44481evRobdiwIcX+6OhovfPOOwYqyzxy5skpSboW5XxS582TPL19rZ2AXfeFh9WkXX0tn/6Toq9cU+6CvpKkLh++ovxF8un9/5usiV0/U5kapTR+/Xvy8s6ehs8CGVWh4vnV7cP/0ydvzFVUeLSl24Sfj9DViGsurgyZXWCdcnrprf/o1++2KyT4bKrHhJ/jvYa75+6RTc93baQN327X+RO3PjcuT8FcyueXW8XL+WvF7A0a8uIUrZz3i1r0aKL+n3S4fwUD6ZTRc0h27typzp07y8/PTw6HQwsWLFCTJk00YcIEeXp6SpJiYmK0bNkyffBByhPIYI2bmy3pP26xoJrd/u/Rul7LOho0v4/2bgjWrEELJEnunklvn4jQSI1oOV43F2w7d/SCpm5+X43b1tMPX6xNg2eAjOzNz7po+6o92vTdjn8/GEgjVR6roJHfvqVzx8M0sVv6XbEHGVu95x9UPr/cWvzJ6tsed/1qjN55YZJOH72gS+ciJEl//HZY8bEJ6jCkub6e8INOH7lwP0oG0iWjHZIJEyaoVatWWrVqlVavXq2JEyfq119/VY8ePRQfz4msaSX6StK3f96+3k7bvXMldUauRV6/7e1b9ntWQxb20/5NB/Xuc2MVH5e0zOiNPzss23/arb+vHn1g6xFdjYhWmRoBafYckDE9172JAioX12cDF8gtm5vcsrnJZksKyH//byAtNXzxEX248h2FnrqkgU+OoQMCl6n7nwd18sBZndh/5rbHxcXEa/fGA8lh5KZta/ZKShrnQgZhejSLka20d+jQIbVt2zb596efflozZszQ7t27NWDAAIOVZS7njoUqMSFRRcr6O22/+fup4Ft/kL42tZN6TGivX5Zs0ZBm7yvmWozz/Sba5ZHdI8Xt3D3cnU50R9ZUt3lt5Snoq4XHP9bKqDlaGTVHTV+pK/+SBbUyao5eeae56RKRybR+81kN+u9rOrD1qPo3GqmI0EjTJSGTyuaeTQ82DNRGC93fYmX99EyH+vL28XLant0raRrE6jgrkFkZHdny8fFRRESESpUqlbztwQcf1Lhx49SnTx998MEH6tq1q7kCM4n42Hjt3XhAdVs8nLyEryTVb1VHVyOidXDb0VRv1+n9/1Pz15/Wkkkr9Hn/uSn2x1yL0b5fDuixFg9r9uCvkjsnNRtVUQ4fL+375YBrnhAyjKl9vlQOH+dzlNoObq5yNUvpvdaTFX4+4ha3BO5csy6N1PXD/9PPizfrow7TlRDPgghwnYDAovLKmV3BW4/967H5C+dRn/FtlRCXqNVf/Zq8vX7zWrp29YaO7AlxZalAumc0kDRo0EAjR47U8OHDFRgYKA+PpG/amzRposGDB2v06NE6f/68yRIzja/GBGnsmnf17qI39dOX6xX4aAW1fus5zRy0QHExcfLOlUMlA4vp3LFQRV6KUpnqpfTSgOd1aPtRbfzmN1V6uJzT/YUEn9H1qzc0a/BXGv+/4Rrzw2AtnrBcef3yqMuHr+jAlsMplgxG1nMmlZnoqMvRio9L0JHdSev4e+fyUomKRXX+RJgiL1293yUik8jrl1vdx7fThZMX9d201Spb03lk9PzxUMXHJqhEpaI6fzyU9xruWalKRSVJpw6n/HeKt4+XSlQorPMnLyoyPFp//HpYezYdUrdRreXl7anTRy7ooaZV9Xy3Rpo5PEjR/zI6DWR2RgNJ//791a9fP7Vp00aff/656tevn7yvbdu2cnNz0/vv3/7qurDm9//t08hWE/Tq8Bc1/NsBCj97WTMGzNOSiSskSWUfCNCE/43QuI7TtHruz6r7wsNyc3NThdplNXVzyv8f9H/8Pe3dEKwDWw7r7UbD1XH0yxq25C3FXo/Vb99t1+dv/Vd2O0to4t+VrVFK434arPHdv9Ca+ZtMl4MM6qGnasjLO7v8SxXUpJ/fS7F/XOfPFBpyUePXvqtxnT/TmnkbDVSJzCRPoaSVJq9eSXmOUtnqJfTRd29pwutfas3CzbLbHRrRbrraDvyPXujZVPn8cuv8yYua2n++fprH515GwpmPrmFzOG6x9NJ9dOrUKeXNm1e5cuVKse/EiRNavXq1unfvfs+P09St9T3fB2CFWw5rSykD98oRn2C6BGQRbr4+pktAFvHTpfS7Ml61NyeZLiFVeyf2M13CPTHaIbmpRIkSt9wXEBCQJmEEAAAAQPqTLgIJAAAAkO4ZnyvKnIxfqR0AAABA1kUgAQAAAGAMI1sAAACABTZGtlyCDgkAAAAAYwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAAKxgZMsl6JAAAAAAMIZAAgAAAMAYRrYAAAAAC7gwomvQIQEAAABgDIEEAAAAgDGMbAEAAABWMLLlEnRIAAAAABhDIAEAAABgDCNbAAAAgAWssuUadEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGABq2y5Bh0SAAAAAMYQSAAAAAAYw8gWAAAAYAUjWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABZwYUTXoEMCAAAAwBgCCQAAAABjGNkCAAAArGBkyyXokAAAAAAwhkACAAAAwBhGtgAAAAALbA5mtlyBDgkAAAAAYwgkAAAAAIxhZAsAAACwgoktl6BDAgAAAMAYAgkAAAAAYxjZAgAAACywMbLlEnRIAAAAABhDIAEAAABgDCNbAAAAgBWMbLkEHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQYdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABawypZr0CEBAAAAYAyBBAAAAIAxjGwBAAAAVjiY2XIFOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsMBmN11B5kSHBAAAAMjiTpw4oZo1a2rp0qXJ2w4cOKC2bduqRo0aatiwoWbNmuWSxyaQAAAAAFlYfHy83nrrLV2/fj15W0REhDp27KhSpUopKChIvXv31pQpUxQUFJTmj8/IFgAAAGBFJl1l6+OPP1bOnDmdtn3zzTfy9PTU8OHD5e7urjJlyigkJEQzZsxQy5Yt0/Tx6ZAAAAAAWdT27du1aNEijR071mn7jh07VLt2bbm7/9W/qFOnjk6cOKHw8PA0rYEOCQAAAJCBNW7c+Lb7161bl+r2qKgoDRgwQEOHDlXhwoWd9l24cEHly5d32laoUCFJ0rlz55Q/f/57qNgZgQQAAACwwJbJRraGDx+uGjVq6D//+U+KfTExMfL09HTalj17dklSbGxsmtZBIAEAAAAysFt1QG5n2bJl2rFjh77//vtU93t5eSkuLs5p280g4u3tfedF3gaBBAAAAMhigoKCFB4eroYNGzptf++99zRr1iwVKVJEYWFhTvtu/u7n55emtRBIAAAAACscmWdma/z48YqJiXHa9sQTT6hPnz565pln9MMPP2jhwoVKTExUtmzZJEmbN29WQEBAmp4/IrHKFgAAAJDl+Pn5qWTJkk4/kpQ/f34VLVpULVu2VHR0tIYMGaKjR49q6dKlmjt3rrp3757mtRBIAAAAADjJnz+/Zs6cqRMnTqhFixb65JNPNGDAALVo0SLNH4uRLQAAAMCCzLbK1j8dOnTI6fdq1app0aJFLn9cOiQAAAAAjCGQAAAAADAmS41suZctbboEZBEPLDliugRkESs/rWe6BGQRO9771HQJgHmZfGTLFDokAAAAAIwhkAAAAAAwJkuNbAEAAAB3K7OvsmUKHRIAAAAAxhBIAAAAABjDyBYAAABghYOZLVegQwIAAADAGAIJAAAAAGMY2QIAAAAsYJUt16BDAgAAAMAYAgkAAAAAYxjZAgAAAKxgZMsl6JAAAAAAMIZAAgAAAMAYRrYAAAAAC1hlyzXokAAAAAAwhkACAAAAwBhGtgAAAAAr7MxsuQIdEgAAAADGEEgAAAAAGMPIFgAAAGAFE1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMCFEV2DDgkAAAAAYwgkAAAAAIxhZAsAAACwwsHMlivQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACABTYujOgSdEgAAAAAGEMgAQAAAGAMI1sAAACAFXbTBWROdEgAAAAAGEMgAQAAAGAMI1sAAACABayy5Rp0SAAAAAAYQyABAAAAYAwjWwAAAIAVTGy5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYAWrbLkEHRIAAAAAxhBIAAAAABjDyBYAAABggY2JLZegQwIAAADAGAIJAAAAAGMY2QIAAACsYJUtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACyw2U1XkDnRIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwseUSdEgAAAAAGEMgAQAAAGAMI1sAAACABTZW2XIJOiQAAAAAjCGQAAAAADCGkS0AAADACka2XIIOCQAAAABjCCQAAAAAjGFkCwAAALDCbrqAzIkOCQAAAABjCCQAAAAAjGFkCwAAALCACyO6Bh0SAAAAAMYQSAAAAAAYw8gWAAAAYAUjWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFbYTReQOdEhAQAAAGAMgQQAAACAMQQSAAAAwAKbw5Euf+7FlStXNGzYMNWvX18PPPCAXn75Ze3YsSN5/4EDB9S2bVvVqFFDDRs21KxZs+71ZUyBQAIAAABkUW+++ab27NmjiRMnasmSJapcubI6d+6sY8eOKSIiQh07dlSpUqUUFBSk3r17a8qUKQoKCkrTGjipHU4K+OfWpyv6aWSv/+qPbceTt+f381Xnt5/Rg/XKy90jmw7tPa1ZY3/UsQPnDFaL++X6pXit6X9UjwwooUKVc97yOHu8XYdXhCvk5yu6Hh6vHPk8VKJeblVsXkBuHq75/uPC7qvavzBMUWdild3XXaWfyKsKzQvIZrMlHxO2/5qCF4Up8lSM3NzdlL9CDlVr6yefwtldUhPM8/Jw1y+TX1M2N+f3XWx8gh7p87HTNnc3N81660X9tv+kPv9hy/0sEwadD5Oe7yh9Mlp6qOatjzseIjV71ZZie0AJh36c55raftkqTZklHTsp5c0jtXlO6vqK9LePNW3bLX38pXT4mOTpKdWoLL3VQypZzDU1IXMKCQnRr7/+qq+//loPPPCAJGnIkCHauHGjVqxYIS8vL3l6emr48OFyd3dXmTJlFBISohkzZqhly5ZpVgeBBMkKFcmj0bM7y8c3h9P2HDk9NW5BD8XHJ+rjYd8qLjZeL7/WWGPmdFHPZycp4uJVQxXjfrh2MU6bRoco/vq/Ly3y+5wLCtlwRZVaFlTesjl05XiMgheH6frFeNXqVTTNa7t06Lp+HXtaxR/1VeU2hXTp4HXt+zpMDrtUqWVBSVL4oev6ZdRJFamVSw/1KabEWLsOBF3S/949qScmllF2Xz4GM6NyxQoom5ubBs36QefDo5K32+3Oow3ZPbJpdMenVTWgsH7bf/I+VwlTzl6Qur4tXY22Sbr9uMuBo0n/d+5khzw9/9ru5aLvM3bvk14bLD31uNS3s7TzD2nyTMnukHq0++uYzv2lxx+TPnpXiomRPpsnte0tLf8yKcTARTLZhRHz5s2rL774QlWqVEneZrPZ5HA4FBkZqX379ql27dpyd//rb2WdOnX0+eefKzw8XPnz50+TOvhLDNlsNjVp8aC6DHwm1f0tOtSTb96c6vrU+OTwcWTfGU39to+qPVRaG37Ycz/LxX3isDsUsuGK9v431NLxcVcTdHxNhKq+4qcKzxeQJPlV9ZEk/TE/VFVf8VP23Hf+kbP/mzCF/HxFz0wvn2LfgcVhylPKSw/1SfpK0L9mLtkTHTq07JLKP5tf2bK76eC3l+RbNLvqvFlcNrekrxfzV/TWjz0O6+TPV1ThuQJ3XBPSv/LFCikuPkHrdx1Vgj31MF2zbFENeulxFczjc5+rgyl2u7TsJ+mjT63f5uBRqai/47ZdlDv1yZfStz9J6xal3DdtjlSxrPTR0KTf6z0sJSRIMxZIHV5MCkIzFkilS0qTR0g3m4A1q0qNWifdb6c2aVcrMobGjRvfdv+6detS3e7r66sGDRo4bVu5cqVOnTqlunXratKkSSpf3vnvb6FChSRJ586dS7NAwjkkUEBFf70+ornWLtul8QNSfjo+9mQVbfrpD6dOSMSlaLWr9z5hJBOLDInRrhnnVbJBHtXu/e/djfjrdpVumldFauVy2p6rSNJXitFhccnbzm2P0rqBx7T0/4L1fZdD+n32eSXE3Nni7onxdl3cf11FH3Z+vGJ1cishxq6LB69LkvKVy6GyzfInhxFJypHXQ+45sunahTghc6pQrKCOX7h8yzAiSZN6Pqfzl6/qlQ8W3MfKYNKhY9KISVLzJ6WxQ6zd5uDRpIDwb9Ztklp1k6o3leq1kN6fKl2/cWf1xcVJ236XmtZ33v5kA+n6DZt27k36vWol6dVWf4URSSqUX/Lxlk6dvbPHBP5u586dGjx4sBo3bqxGjRopJiZGnn9vDUrKnj2pPRgbG5tmj2u8QxIbG6sjR46obNmy8vLy0oEDBzR//nyFhoaqXLlyat++vfz9/U2XmamFnbuizk3G6VJopKo+VNppXzZ3N5Uo46f13+1Wu75P6KnWteWbN6cO7A7R9JHf6eThC4aqhqt5F/DQUx+Xk3d+D4Xtv/avx+f089QDXYuk2H52a5Rs2WzK9ef5Gqd+uaJtU8+qRL3cqtymkK5djNe+r5LOAan3bsmkVnGi468hij//PWlP/KtN7pbNpmuh8bInOJSriPPchI//nwHoXKxU3Sd5dOvvwvZdU/y1RPmW4BySzKpC8YJyOBya3ucFVS9dRHEJiVq767AmBW3U9dh4SVKXCd/o6Llww5XifirsJ61aIPkXSjoHw4qDR6UyJaU2PZPGt3x9pOZPSX06Sx5//itqxRrp7dE2/aepQ307J42ETZ4hHTkpzZ6QdO5HYuJf0z43c3JCwl+P4+4unT4nxcfbVLK481hQiT/PCzl5WnqsttTz1ZR1bt0lRV61qVzpzDVSlO6k05GtW3VA7sTatWv11ltvqXr16po4caIkycvLS3Fxzl/e3Qwi3t7e9/yYNxkNJMeOHVOHDh108eJFFSlSRKNHj1avXr1UrFgxlSlTRmvXrtXSpUv11VdfqUyZMiZLzdSiI28oOjL1r3F8fHPI3SObWnSopwtnwjV5aJA8PLOpXZ8nNHZeN/V6brLCQ6NSvS0yNs9c7vL898Nu68yWKIVsjFTZZ/LJ0yebHA6H/lgQKr8aPsljVpLkU9hTv4wM0YVd0Sr8YC5tGHlSl4KvO93X0jbByf/99LRyir+WKElyz+Hc6L35e/yN1L8Zj41M0M7PzilHfneVapDnHp8h0iObTSpbpIAS7XZ9++smzfxxqwJL+qlbszoqXTi/ukz8Rg6HCCNZUB5fSb7Wj790Wbp02SabzaH+3ZMCzZad0syvpQth0rh3k/59OuFzqd5DjuQxKynp5PJOb9q0YYtDDR+ROr4pbf/d+eT4qn+bslm70KGo6KT/9vnHv/Ny/nlqZ/Qtvhu6fEUaNl7yL+hQ8yetPz/gpvnz52vMmDFq2rSpxo8fn9wV8ff3V1hYmNOxN3/38/NLs8c3Gkg++ugj1axZU7169dKsWbPUs2dPPffccxo5cqRsNpsSEhI0YMAAffDBB5o5c6bJUrMsD4+/3iJDO89WzPWklHzkj7OateZt/afto5oz4SdT5SEdO7M5UtumnlXBQG9VfSXpQ+vquTjdCE9QxRa5nDoeBQNzyj2Hm0L3JgWSB7sVUfyfI1wn1kTo/K6renRgieTjc+R1143Lf361mHLxm6TNqWy/cTlev4wOUWxUguoPKyX3HNnS5skiXbHJpj7Tlik86ppOhkZIknYdPavwqOsa0+lpPRJYihPYYYlPTmnWBIcCSkiFk8bm9VCNpFWtpsy0qUc7h2w26cJFm7q1dTh1PGpXl3xyOvTbDqnhI9KI/tK160mfe998L/28WZr+/l/HFywghV5K+u/UPr8k5xGtm8IuSV3eksIjpC8nSjnT7ktrZBFfffWVRo0apXbt2mnw4MFy+9sbrXbt2lq4cKESExOVLVvS38zNmzcrICAgzc4fkQwHkm3btikoKEilS5fWwIED9f333+vll19OXq7T3d1dPXr00EsvvWSyzCzt+rWkttzebceSw4gkXTx/RaeOhal0pZQjOsDh7y9p77xQFaycU48NKK5sfy75G3c1qauxe+Z57Z55PsXtYiKS/prnKvrXKNX5nVfl5m5TvjLOq7955Ey6z4R/rP6V8GdnxMPbOWxEhsRo0wenlBBjV90hJZWvrPP9IfOwOxzaeeRMiu2/7DshSSpftCCBBJZ4ZZcerZVye4M60pSZ0sFjfwWVkZNsGjkp5bEXLyWFkIC/vlPRz5slDw+pSkXnY3P9ub7CPzsh1/4cYvD5x6rrh49JPQYlnasyY1zSuSVwsXQ6snW3Tpw4offff19NmzZV9+7dFR7+V+fYy8tLLVu21MyZMzVkyBB16dJFe/fu1dy5czVixIg0rcNoIPHy8lJMTIwkqUCBAnrxxReTT5S5KSoqSrly5Urt5rgPrkfHKOLSVXl4pnyruLtnU1xMvIGqkF45HA79PvuCjv10WcUe8VXt3kWTw4j0V4io2s5PBVO5nolnTusdCx8/T9ncpOh/nJh+83ffYn99loT9Ea3fxp2Wh7ebGo4spdwlvO7oeSFjKZg7p+pWCdCv+08q7Ep08navPzu+V67d4ZnGyLJOnJK27paebeIcBmL+PJc3b+6/QsTbPR16qEbK+/C9g3/ClCgiZcvmSHFi+qk/83WZUn9t27JLen2IlCun9N+pUnnnU0ABS1atWqX4+HitWbNGa9ascdrXokULffjhh5o5c6bGjBmjFi1aqGDBghowYIBatGiRpnUYDSR169bVqFGjNHr0aJUpU0YjR45M3udwOLRt2zaNGDFCTZo0MVgldmw8pEebVpFvXm9FRSTN9RcNKKBiAQX00+JthqtDerLvqzAd++myyjXLr2rt/ZwuTihJvkWyK3vubLoeFqd8f1tuNyYiXts+PqvSTfMmn5T+b7J5uqlApZw6uzVK5Z/Ln/xYZ7ZEyiOnW3IHJOLEDf069pRyFvJUvSEllSO/Rxo9W6RXnh7uerdtU33xwxZ9tmJz8vYnapVXot2u3UdZhgjWhF6SRky0ycPdoZbN/tq+8n9STm+HKldIOt8jf16Hzpx3Xm73Yrg0YEzSRQ1LWLwMU/bsUq1q0pqNSfd18yN01QbJ18ehan92QIIPS73ekYoWlmaOk/xSrt0BWNKjRw/16NHjtsdUq1ZNixalskZ1GjIaSN555x316NFD06dP14QJE5z2/fjjj+rfv7/q1aunN99801CFkKSvpq3TI00qa8zsLvpq2lplc8+mDm8+pYvnI7WKQJJlxV9PVNSZWPn4eSp7bnddOXFDh767pLxlvFTsUV9dPuL8LbRvsezy8M6mKi/7aefn52Rzs6nwg7kUfz1RB5Zc1PXwBOUtnXKMqvKLhVT5xUKp1lCpZQFtHBWiLRPPqFSjPAo/dF2Hl4er6it+ypY9qRuz89NzsidIga0L6np4vK6H/9XVy+7rbjkAIeM4eylSK7YEq8MTtRSfkKg/TpxXjbJF1enJ2lq8YY9C/jyvBPin6GvS0ZNJASJfnqTzQB6q4dDY6dKNmKSxqw1bpPlB0ts9pdx/dj/6dpGGT5CyuUkNH5WuRkuf/lcKvShVrpDycV7vmPSTmh6vSp3elPq9J73wjLR7vzR7odS/+18XYxz6UdIKXa93SLri/Pm/nXOcL4/1AIS7cGcr1MMio4EkX758+uabb3TlypUU+x555BEtW7ZMFStWTHlD3FcXTl9W/5emq9PbT+utj9rIbrdr969H9MX7K3TjGtdxyKoiTsRo4/CTqtWriEo9nldnt16VHFLEsRj9b8iJFMfXH15KhSrnVEDjvHLP4abD313S8bURcvdyU/4K3nqoTzHl9LuzcFCoqo8e6V9cwd+EafNHp5Ujn7uqtfNT+f8kdV+iQ+N05UTSWOiWiSnPKSjZII9qv85f7sxo9IK1OhV2Rc/WCVSXZx5W2JVofbZii/67Zofp0pCOBR+W2r9h0/uDHGrxtJQtm/TJGOmTOdKcb6SLl6XiRaTh/aUX//PX7Vo/m9QpmbVQ+maF5J1DeqCKNG6oVKzwndVQ5wFpysikiye+PlTyK5AUfjr+eTrt6XPSgSNJrZM33kt5++ZPOfTBO3f3/AFTbA5HJjs75zaeLj/QdAnIIh5YcsR0CcgiVn5az3QJyCJ2vHcHlzcH7oGb/2HTJdzSU5UtXlHzPvtp/xjTJdwT4xdGBAAAADICW9b5Hv++SmVFawAAAAC4PwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAAKywM7LlCnRIAAAAABhDIAEAAABgDCNbAAAAgBWssuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWGFnZMsV6JAAAAAAMIZAAgAAAMAYRrYAAAAAKxx20xVkSnRIAAAAABhDIAEAAABgDCNbAAAAgBVcGNEl6JAAAAAAMIZAAgAAAMAYRrYAAAAAK7gwokvQIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGCF3W66gkyJDgkAAAAAYwgkAAAAAIxhZAsAAACwglW2XIIOCQAAAABjCCQAAAAAjGFkCwAAALCCkS2XoEMCAAAAwBgCCQAAAABjGNkCAAAArLAzsuUKdEgAAAAAGEMgAQAAAGAMI1sAAACABQ6H3XQJmRIdEgAAAADGEEgAAAAAGMPIFgAAAGAFq2y5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYIWDkS1XoEMCAAAAwBgCCQAAAABjGNkCAAAArLBzYURXoEMCAAAAwBgCCQAAAABjGNkCAAAArGCVLZegQwIAAADAGAIJAAAAAGMY2QIAAAAscLDKlkvQIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFbYGdlyBTokAAAAAIwhkAAAAAAwhpEtAAAAwAoHF0Z0BTokAAAAAIwhkAAAAAAwhpEtAAAAwAIHq2y5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYAWrbLkEHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQYdEgAAAADGEEgAAAAAGMPIFgAAAGAFq2y5BB0SAAAAAMYQSAAAAAAYY3M4HCwXAAAAAMAIOiQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGMIJAAAAACMIZAAAAAAMIZAAgAAAMAYAgkAAAAAYwgkAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjCGQIFV2u11Tp05VvXr1VL16dXXq1EkhISGmy0ImN336dLVr1850GciErly5omHDhql+/fp64IEH9PLLL2vHjh2my0ImFR4errffflt16tRRzZo11a1bNx09etR0WUC6RSBBqqZPn66FCxdq9OjRWrRokWw2m7p27aq4uDjTpSGTmjNnjqZOnWq6DGRSb775pvbs2aOJEydqyZIlqly5sjp37qxjx46ZLg2ZUM+ePXX69GnNmDFDS5YskZeXlzp06KAbN26YLg1IlwgkSCEuLk6zZ89W79691aBBA1WsWFGTJk1SaGio1qxZY7o8ZDKhoaHq0qWLpkyZooCAANPlIBMKCQnRr7/+qvfee0+1atVS6dKlNWTIEPn5+WnFihWmy0MmExERoWLFimnUqFGqWrWqypQpo169eunixYs6cuSI6fKAdIlAghQOHjyoa9euqU6dOsnbfH19FRgYqO3btxusDJnR/v37lTt3bi1fvlzVq1c3XQ4yobx58+qLL75QlSpVkrfZbDY5HA5FRkYarAyZUd68eTVx4kSVK1dOknTp0iXNmjVL/v7+Klu2rOHqgPTJ3XQBSH8uXLggSSpcuLDT9kKFCun8+fMmSkIm1qhRIzVq1Mh0GcjEfH191aBBA6dtK1eu1KlTp1S3bl1DVSErePfdd/XNN9/I09NTn376qby9vU2XBKRLdEiQws0ZV09PT6ft2bNnV2xsrImSACDN7Ny5U4MHD1bjxo0Jw3Cp9u3bKygoSM8995xee+017d+/33RJQLpEIEEKXl5ekpTiBPbY2FjlyJHDREkAkCbWrl2rzp07q1q1apo4caLpcpDJlS1bVlWqVNGoUaNUrFgxzZ8/33RJQLpEIEEKN0e1wsLCnLaHhYXJ39/fREkAcM/mz5+v3r17q379+poxY0byly9AWgoPD9eKFSuUmJiYvM3NzU1lypRJ8XcVQBICCVKoWLGifHx8tHXr1uRtUVFRCg4OVq1atQxWBgB356uvvtKoUaP0yiuvaPLkySlGUoG0EhYWpv79+2vbtm3J2+Lj4xUcHKwyZcoYrAxIvzipHSl4enqqbdu2Gj9+vPLly6eiRYtq3Lhx8vf3V9OmTU2XBwB35MSJE3r//ffVtGlTde/eXeHh4cn7vLy8lCtXLoPVIbOpWLGi6tatqxEjRmj06NHy9fXVZ599pqioKHXo0MF0eUC6RCBBqvr06aOEhAQNHTpUMTExql27tmbNmsW3igAynFWrVik+Pl5r1qxJcS2lFi1a6MMPPzRUGTIjm82myZMna8KECXrjjTd09epV1apVSwsWLFCRIkVMlwekSzaHw+EwXQQAAACArIlzSAAAAAAYQyABAAAAYAyBBAAAAIAxBBIAAAAAxhBIAAAAABhDIAEAAABgDIEEAAAAgDEEEgAAAADGEEgAIIOJjY1VYGCgatasqVGjRpkuBwCAe0IgAYAMxmazae7cuapWrZrmz5+vEydOmC4JAIC7RiABgAzG09NTtWvXVpcuXSRJ+/fvN1wRAAB3j0ACABlU6dKlJUkHDhwwXAkAAHePQAIAGdSMGTMkSQcPHjRcCQAAd49AAgAZ0KZNm/T1118rd+7cCg4ONl0OAAB3jUACABlMVFSUBg8erMaNG+vll1/W5cuXFRoaarosAADuCoEEADKYESNGKCEhQaNHj1ZgYKAkxrYAABkXgQQAMpCffvpJK1as0JgxY5QvX77kQMKJ7QCAjIpAAgAZxMWLF/Xee+/ppZde0uOPPy5JKl68uHx9fTmPBACQYRFIACCDePfdd5U7d24NGjTIaXulSpUY2QIAZFgEEgDIABYvXqyNGzfqo48+kre3t9O+wMBAnTp1StHR0YaqAwDg7tkcDofDdBEAAAAAsiY6JAAAAACMIZAAAAAAMIZAAgAAAMAYAgkAAAAAYwgkAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGP+HxPLK3XyXLGWAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  1.7304881   19.47958494  12.11340253  23.41589548]\n",
-      " [  0.22948497   0.18392847   0.29364655   0.20072279]\n",
-      " [  0.26303845   4.06730814   2.11590451   7.5411205 ]\n",
-      " [ 15.77893972 122.02334824  51.2167072  152.54894451]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -2951,7 +2726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19fec57c",
+   "id": "50a1e770",
    "metadata": {
     "editable": true
    },
@@ -2971,7 +2746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bcb8b34",
+   "id": "5af218e3",
    "metadata": {
     "editable": true
    },
@@ -3003,7 +2778,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ebf620c",
+   "id": "55450c7b",
    "metadata": {
     "editable": true
    },
@@ -3027,7 +2802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db27019b",
+   "id": "be42ca6d",
    "metadata": {
     "editable": true
    },
@@ -3055,7 +2830,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ab4acd0",
+   "id": "4b7a4ceb",
    "metadata": {
     "editable": true
    },
@@ -3070,7 +2845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b60035b5",
+   "id": "e00d9920",
    "metadata": {
     "editable": true
    },
@@ -3082,7 +2857,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29af7e67",
+   "id": "6243c073",
    "metadata": {
     "editable": true
    },
@@ -3097,7 +2872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "740b4af2",
+   "id": "009ea7de",
    "metadata": {
     "editable": true
    },
@@ -3110,7 +2885,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ad3976",
+   "id": "712c684d",
    "metadata": {
     "editable": true
    },
@@ -3122,7 +2897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0051e649",
+   "id": "b4bcd2a5",
    "metadata": {
     "editable": true
    },
@@ -3132,7 +2907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a7712be",
+   "id": "143b6359",
    "metadata": {
     "editable": true
    },
@@ -3143,7 +2918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27e08926",
+   "id": "d2e56068",
    "metadata": {
     "editable": true
    },
@@ -3161,7 +2936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ec61954",
+   "id": "2541505d",
    "metadata": {
     "editable": true
    },
@@ -3172,7 +2947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7aab6da",
+   "id": "725efee7",
    "metadata": {
     "editable": true
    },
@@ -3184,7 +2959,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "523fa0e8",
+   "id": "cb15e9f1",
    "metadata": {
     "editable": true
    },
@@ -3194,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4172710",
+   "id": "87026e15",
    "metadata": {
     "editable": true
    },
@@ -3206,7 +2981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a21de62",
+   "id": "cd09c85a",
    "metadata": {
     "editable": true
    },
@@ -3216,7 +2991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a68d2930",
+   "id": "c7845cfd",
    "metadata": {
     "editable": true
    },
@@ -3228,7 +3003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "519a6c63",
+   "id": "20e94324",
    "metadata": {
     "editable": true
    },
@@ -3238,7 +3013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f28767d",
+   "id": "46cc127e",
    "metadata": {
     "editable": true
    },
@@ -3257,7 +3032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60e193f4",
+   "id": "b83e3f3f",
    "metadata": {
     "editable": true
    },
@@ -3267,7 +3042,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b76cff50",
+   "id": "d3c3d5b8",
    "metadata": {
     "editable": true
    },
@@ -3279,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c65fa96",
+   "id": "c6e741c3",
    "metadata": {
     "editable": true
    },
@@ -3289,7 +3064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "554a1508",
+   "id": "510abc9b",
    "metadata": {
     "editable": true
    },
@@ -3305,7 +3080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec603f5c",
+   "id": "1a6464a4",
    "metadata": {
     "editable": true
    },
@@ -3325,7 +3100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98c74b9b",
+   "id": "0f403e2d",
    "metadata": {
     "editable": true
    },
@@ -3335,7 +3110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f4d2c5c",
+   "id": "4a8138dc",
    "metadata": {
     "editable": true
    },
@@ -3346,7 +3121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "654bf9a6",
+   "id": "726cbaa4",
    "metadata": {
     "editable": true
    },
@@ -3365,7 +3140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f80909ca",
+   "id": "6b7adfb6",
    "metadata": {
     "editable": true
    },
@@ -3375,7 +3150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee25c0f7",
+   "id": "84165f3e",
    "metadata": {
     "editable": true
    },
@@ -3387,7 +3162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1947e0c",
+   "id": "ebf366ef",
    "metadata": {
     "editable": true
    },
@@ -3397,7 +3172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05229723",
+   "id": "02ffad05",
    "metadata": {
     "editable": true
    },
@@ -3408,7 +3183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb0abff0",
+   "id": "d54237d5",
    "metadata": {
     "editable": true
    },
@@ -3428,7 +3203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc16c9f8",
+   "id": "8a7e9dbb",
    "metadata": {
     "editable": true
    },
@@ -3440,7 +3215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7f65472",
+   "id": "d4d5ff52",
    "metadata": {
     "editable": true
    },
@@ -3455,13 +3230,10 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "acf08e73",
+   "id": "297415bf",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -3538,7 +3310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "937591a1",
+   "id": "2ce99829",
    "metadata": {
     "editable": true
    },
@@ -3548,7 +3320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "132c386c",
+   "id": "f6a44999",
    "metadata": {
     "editable": true
    },
@@ -3560,7 +3332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "869bdef2",
+   "id": "990c34c3",
    "metadata": {
     "editable": true
    },
@@ -3570,7 +3342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7781a1c4",
+   "id": "a06f9457",
    "metadata": {
     "editable": true
    },
@@ -3581,7 +3353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd8e693c",
+   "id": "19cd9843",
    "metadata": {
     "editable": true
    },
@@ -3593,7 +3365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5c29ebf",
+   "id": "cc8c11ab",
    "metadata": {
     "editable": true
    },
@@ -3603,7 +3375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a798f40c",
+   "id": "d3d4e384",
    "metadata": {
     "editable": true
    },
@@ -3615,7 +3387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bae820af",
+   "id": "6187929e",
    "metadata": {
     "editable": true
    },
@@ -3625,7 +3397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06c75a81",
+   "id": "e0ba2cb1",
    "metadata": {
     "editable": true
    },
@@ -3637,7 +3409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59236913",
+   "id": "468fec32",
    "metadata": {
     "editable": true
    },
@@ -3650,7 +3422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81b5da1b",
+   "id": "ba618e10",
    "metadata": {
     "editable": true
    },
@@ -3662,7 +3434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e74c517f",
+   "id": "a74d3f1c",
    "metadata": {
     "editable": true
    },
@@ -3672,7 +3444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "648beb7f",
+   "id": "fb434398",
    "metadata": {
     "editable": true
    },
@@ -3684,7 +3456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d9326d3",
+   "id": "42558cc8",
    "metadata": {
     "editable": true
    },
@@ -3696,7 +3468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f51ed614",
+   "id": "d851da5b",
    "metadata": {
     "editable": true
    },
@@ -3707,7 +3479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ced37c54",
+   "id": "49533cb3",
    "metadata": {
     "editable": true
    },
@@ -3719,7 +3491,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebbeea54",
+   "id": "a62f6a4c",
    "metadata": {
     "editable": true
    },
@@ -3738,7 +3510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b5d06a0",
+   "id": "381182cd",
    "metadata": {
     "editable": true
    },
@@ -3751,7 +3523,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74890321",
+   "id": "04b172c8",
    "metadata": {
     "editable": true
    },
@@ -3761,7 +3533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad33aa43",
+   "id": "265d8614",
    "metadata": {
     "editable": true
    },
@@ -3773,7 +3545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "626df4e6",
+   "id": "68c1005e",
    "metadata": {
     "editable": true
    },
@@ -3783,7 +3555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6594383b",
+   "id": "61fb0fbc",
    "metadata": {
     "editable": true
    },
@@ -3795,7 +3567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec04a0b3",
+   "id": "acb3fa59",
    "metadata": {
     "editable": true
    },
@@ -3805,7 +3577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a82796e0",
+   "id": "82f920b5",
    "metadata": {
     "editable": true
    },
@@ -3817,7 +3589,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9607cc25",
+   "id": "26de4671",
    "metadata": {
     "editable": true
    },
@@ -3828,7 +3600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f84698d3",
+   "id": "071a86ff",
    "metadata": {
     "editable": true
    },
@@ -3840,7 +3612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1245df8a",
+   "id": "fdb51c1d",
    "metadata": {
     "editable": true
    },
@@ -3850,7 +3622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6df8204",
+   "id": "5532b00a",
    "metadata": {
     "editable": true
    },
@@ -3862,7 +3634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6250497e",
+   "id": "a7c57982",
    "metadata": {
     "editable": true
    },
@@ -3872,7 +3644,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f788dc8f",
+   "id": "022ce912",
    "metadata": {
     "editable": true
    },
@@ -3884,7 +3656,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdb72b3a",
+   "id": "12518c37",
    "metadata": {
     "editable": true
    },
@@ -3905,7 +3677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d498ca67",
+   "id": "89f9bbda",
    "metadata": {
     "editable": true
    },
@@ -3916,7 +3688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "786a3fef",
+   "id": "51458daa",
    "metadata": {
     "editable": true
    },
@@ -3928,7 +3700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0080ea0",
+   "id": "eaf4a93e",
    "metadata": {
     "editable": true
    },
@@ -3938,7 +3710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27b8b857",
+   "id": "2fea3537",
    "metadata": {
     "editable": true
    },
@@ -3950,7 +3722,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bf5241a",
+   "id": "ca4f1123",
    "metadata": {
     "editable": true
    },
@@ -3960,7 +3732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b578bc7",
+   "id": "09de659a",
    "metadata": {
     "editable": true
    },
@@ -3972,7 +3744,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11220b8a",
+   "id": "77f9fd08",
    "metadata": {
     "editable": true
    },
@@ -3984,7 +3756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a920365b",
+   "id": "beab9d54",
    "metadata": {
     "editable": true
    },
@@ -3998,13 +3770,10 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "0b7b8c69",
+   "id": "ce0d14e1",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4016,7 +3785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c209e80c",
+   "id": "20bbbdf2",
    "metadata": {
     "editable": true
    },
@@ -4027,13 +3796,10 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "947eedad",
+   "id": "d1b21aae",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4043,7 +3809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55e5aa0a",
+   "id": "02bb27d6",
    "metadata": {
     "editable": true
    },
@@ -4054,13 +3820,10 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "3fb277ad",
+   "id": "98630c99",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4080,7 +3843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36c1d2bc",
+   "id": "9af04036",
    "metadata": {
     "editable": true
    },
@@ -4094,13 +3857,10 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "6db56b3d",
+   "id": "2d8cd3da",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4110,7 +3870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0ee98a4",
+   "id": "71899eed",
    "metadata": {
     "editable": true
    },
@@ -4121,13 +3881,10 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "8774bce4",
+   "id": "f8cc2dd0",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4136,7 +3893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d29c10a",
+   "id": "44f3dd69",
    "metadata": {
     "editable": true
    },
@@ -4147,13 +3904,10 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "93c8b0c6",
+   "id": "acdf8d3d",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4166,7 +3920,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff36a22e",
+   "id": "26d00774",
    "metadata": {
     "editable": true
    },
@@ -4177,13 +3931,10 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "35feafa3",
+   "id": "fcf52c3c",
    "metadata": {
     "collapsed": false,
-    "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -4194,7 +3945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c4ac2dc",
+   "id": "82083461",
    "metadata": {
     "editable": true
    },
@@ -4216,7 +3967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc2eacf3",
+   "id": "c247a29f",
    "metadata": {
     "editable": true
    },
@@ -4228,7 +3979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48c5a8b6",
+   "id": "5c6266aa",
    "metadata": {
     "editable": true
    },
@@ -4238,7 +3989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc4699e",
+   "id": "c36f8d5c",
    "metadata": {
     "editable": true
    },
@@ -4250,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4db48e9d",
+   "id": "5b1a5174",
    "metadata": {
     "editable": true
    },
@@ -4262,7 +4013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "350fec9b",
+   "id": "81c82695",
    "metadata": {
     "editable": true
    },
@@ -4272,7 +4023,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b474ddf5",
+   "id": "7dc8a3bc",
    "metadata": {
     "editable": true
    },
@@ -4284,7 +4035,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67fc1d79",
+   "id": "41789a38",
    "metadata": {
     "editable": true
    },
@@ -4294,7 +4045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6aa9f684",
+   "id": "4a2e42bf",
    "metadata": {
     "editable": true
    },
@@ -4306,7 +4057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce023be0",
+   "id": "acd67ee6",
    "metadata": {
     "editable": true
    },
@@ -4316,7 +4067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3754c848",
+   "id": "ee90b0eb",
    "metadata": {
     "editable": true
    },
@@ -4328,7 +4079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d884286",
+   "id": "54eaab7f",
    "metadata": {
     "editable": true
    },
@@ -4340,7 +4091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8446340",
+   "id": "f941f1ac",
    "metadata": {
     "editable": true
    },
@@ -4350,7 +4101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6830538",
+   "id": "89e02a88",
    "metadata": {
     "editable": true
    },
@@ -4362,7 +4113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84f9e6b4",
+   "id": "3d1f3b65",
    "metadata": {
     "editable": true
    },
@@ -4372,7 +4123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "586509f5",
+   "id": "1dda0ac2",
    "metadata": {
     "editable": true
    },
@@ -4384,7 +4135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff04787a",
+   "id": "aafe7aae",
    "metadata": {
     "editable": true
    },
@@ -4396,7 +4147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fab40e86",
+   "id": "37f9c1fa",
    "metadata": {
     "editable": true
    },
@@ -4408,7 +4159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "175c9026",
+   "id": "373adac2",
    "metadata": {
     "editable": true
    },
@@ -4418,7 +4169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b8d1d83",
+   "id": "bdd6d800",
    "metadata": {
     "editable": true
    },
@@ -4430,7 +4181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c64f049",
+   "id": "913df005",
    "metadata": {
     "editable": true
    },
@@ -4440,7 +4191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a591f8f",
+   "id": "76f6737e",
    "metadata": {
     "editable": true
    },
@@ -4452,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "956b5691",
+   "id": "48f6aa66",
    "metadata": {
     "editable": true
    },
@@ -4462,7 +4213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85b1bd72",
+   "id": "513a1986",
    "metadata": {
     "editable": true
    },
@@ -4474,7 +4225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6950fb95",
+   "id": "20fbbe07",
    "metadata": {
     "editable": true
    },
@@ -4485,7 +4236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2d998e7",
+   "id": "4dd97daa",
    "metadata": {
     "editable": true
    },
@@ -4497,7 +4248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79e10d20",
+   "id": "032afd0e",
    "metadata": {
     "editable": true
    },
@@ -4507,7 +4258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6585dbe2",
+   "id": "25eac667",
    "metadata": {
     "editable": true
    },
@@ -4519,7 +4270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4291e4ad",
+   "id": "18c343cd",
    "metadata": {
     "editable": true
    },
@@ -4529,7 +4280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a50444a",
+   "id": "9864986f",
    "metadata": {
     "editable": true
    },
@@ -4541,7 +4292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58d08cf2",
+   "id": "1ecdde02",
    "metadata": {
     "editable": true
    },
@@ -4554,7 +4305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8215c3c6",
+   "id": "9d62dddc",
    "metadata": {
     "editable": true
    },
@@ -4566,7 +4317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09efd53b",
+   "id": "db398f5b",
    "metadata": {
     "editable": true
    },
@@ -4578,7 +4329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c694cf4",
+   "id": "2bbf293c",
    "metadata": {
     "editable": true
    },
@@ -4590,7 +4341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "909419d5",
+   "id": "d82e1506",
    "metadata": {
     "editable": true
    },
@@ -4602,7 +4353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d35d3a4",
+   "id": "1ddd417c",
    "metadata": {
     "editable": true
    },
@@ -4614,7 +4365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "845b476b",
+   "id": "e7904873",
    "metadata": {
     "editable": true
    },
@@ -4624,7 +4375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "644470d4",
+   "id": "19604164",
    "metadata": {
     "editable": true
    },
@@ -4636,7 +4387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c4ad6c8",
+   "id": "0f262e03",
    "metadata": {
     "editable": true
    },
@@ -4648,7 +4399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "839e60fb",
+   "id": "cdb18e0d",
    "metadata": {
     "editable": true
    },
@@ -4660,25 +4411,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }