From 8afe465b5ff4afd2fc199ea3a75d635b3bad26e4 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Tue, 17 Sep 2024 14:17:00 +0200 Subject: [PATCH] update on jupyter-book --- doc/LectureNotes/DataFiles/cancer.dot | 16 +- doc/LectureNotes/DataFiles/cancer.png | Bin 251113 -> 248436 bytes .../_build/.doctrees/chapter1.doctree | Bin 558285 -> 562180 bytes .../_build/.doctrees/chapter10.doctree | Bin 277966 -> 418727 bytes .../_build/.doctrees/chapter11.doctree | Bin 788006 -> 778564 bytes .../_build/.doctrees/chapter2.doctree | Bin 505415 -> 505420 bytes .../_build/.doctrees/chapter3.doctree | Bin 1641268 -> 1643475 bytes .../_build/.doctrees/chapter6.doctree | Bin 522988 -> 532624 bytes .../_build/.doctrees/chapter8.doctree | Bin 213426 -> 209573 bytes .../_build/.doctrees/environment.pickle | Bin 338937 -> 337777 bytes .../_build/.doctrees/linalg.doctree | Bin 190615 -> 190198 bytes .../_build/.doctrees/statistics.doctree | Bin 273491 -> 274360 bytes 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doc/LectureNotes/_build/html/_images/chapter10_59_2.png create mode 100644 doc/LectureNotes/_build/html/_images/chapter10_63_0.png create mode 100644 doc/LectureNotes/_build/html/_images/chapter10_63_1.png create mode 100644 doc/LectureNotes/_build/jupyter_execute/chapter10_59_1.png create mode 100644 doc/LectureNotes/_build/jupyter_execute/chapter10_59_2.png create mode 100644 doc/LectureNotes/_build/jupyter_execute/chapter10_63_0.png create mode 100644 doc/LectureNotes/_build/jupyter_execute/chapter10_63_1.png diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 20dfde934..1b90f1552 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -6,11 +6,11 @@ edge [fontname="helvetica"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ; 1 -> 2 ; -3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; +3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="mean compactness <= 0.063\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="area error <= 51.38\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 6 ; @@ -30,11 +30,11 @@ edge [fontname="helvetica"] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="worst smoothness <= 0.106\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="symmetry error <= 0.014\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; 15 -> 17 ; 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 17 -> 18 ; @@ -42,16 +42,16 @@ edge [fontname="helvetica"] ; 17 -> 19 ; 20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ; 14 -> 20 ; -21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; +21 [label="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; 20 -> 21 ; 22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; +24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; 20 -> 24 ; -25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; +25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; 24 -> 25 ; -26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; +26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 24 -> 26 ; } \ No newline at end of file diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index 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zOH0puFRQqa%jM2#`M;>bhF#BO#sW|yo#F2arH^4y4xcUr zNJ~^rd?HbX_=}uN_+(hws;fzWFi6rZAN^Q141a}ycaM~wUUig|kdS~?o+Xo+-nA26 zfXA-JKIpQxvGGLSVHSDIeGrScd3(o@8W6dic{eDxD+HGTULua1#M%#TqW|Ag{^J*) bOiI44(TLksU!ehiRSM0TVRd8e*28}QTYNT1 diff --git a/doc/LectureNotes/_build/html/_sources/week38.ipynb b/doc/LectureNotes/_build/html/_sources/week38.ipynb index 343d401bc..68998261e 100644 --- a/doc/LectureNotes/_build/html/_sources/week38.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week38.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "54608ce4", + "id": "a811ba80", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "702fc25a", + "id": "e5014a9c", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "708a3ac3", + "id": "023eb6d1", "metadata": { "editable": true }, @@ -47,7 +47,7 @@ }, { "cell_type": "markdown", - "id": "ca760e72", + "id": "e981c015", "metadata": { "editable": true }, @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "a9fd5329", + "id": "11590c09", "metadata": { "editable": true }, @@ -85,12 +85,14 @@ "\n", " * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at \n", "\n", - " * Work on project 1, in particular resampling methods like cross-validation and bootstrap." + " * Work on project 1, in particular resampling methods like cross-validation and bootstrap.\n", + "\n", + " * [Video on cross-validation from exercise session](https://youtu.be/T9jjWsmsd1o)" ] }, { "cell_type": "markdown", - "id": "63b2922e", + "id": "57e011be", "metadata": { "editable": true }, @@ -100,7 +102,7 @@ }, { "cell_type": "markdown", - "id": "d5f9f534", + "id": "0896e712", "metadata": { "editable": true }, @@ -122,7 +124,7 @@ }, { "cell_type": "markdown", - "id": "0143d162", + "id": "44bb3650", "metadata": { "editable": true }, @@ -148,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "ef85093d", + "id": "921c6771", "metadata": { "editable": true }, @@ -172,7 +174,7 @@ }, { "cell_type": "markdown", - "id": "99320f0f", + "id": "f80e9666", "metadata": { "editable": true }, @@ -197,7 +199,7 @@ }, { "cell_type": "markdown", - "id": "44bcf7a3", + "id": "952f8119", "metadata": { "editable": true }, @@ -209,7 +211,7 @@ }, { "cell_type": "markdown", - "id": "403aa0cd", + "id": "9b587b40", "metadata": { "editable": true }, @@ -227,7 +229,7 @@ }, { "cell_type": "markdown", - "id": "7703e42f", + "id": "bfb711d7", "metadata": { "editable": true }, @@ -245,7 +247,7 @@ }, { "cell_type": "markdown", - "id": "025d7cc2", + "id": "0acaaf3c", "metadata": { "editable": true }, @@ -256,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "02656176", + "id": "73564ce7", "metadata": { "editable": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "190a0e98", + "id": "ef6011fd", "metadata": { "editable": true }, @@ -296,7 +298,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "d3aa4736", + "id": "3444ad7b", "metadata": { "collapsed": false, "editable": true @@ -363,7 +365,7 @@ }, { "cell_type": "markdown", - "id": "b52054ec", + "id": "01d01242", "metadata": { "editable": true }, @@ -376,7 +378,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "8c348f42", + "id": "143c59fe", "metadata": { "collapsed": false, "editable": true @@ -395,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "6df16129", + "id": "42136436", "metadata": { "editable": true }, @@ -406,7 +408,7 @@ }, { "cell_type": "markdown", - "id": "9a89924c", + "id": "e8a7f059", "metadata": { "editable": true }, @@ -418,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "bf2297a8", + "id": "f1c0bcf8", "metadata": { "editable": true }, @@ -437,7 +439,7 @@ }, { "cell_type": "markdown", - "id": "a865cd76", + "id": "e4fd2845", "metadata": { "editable": true }, @@ -459,7 +461,7 @@ }, { "cell_type": "markdown", - "id": "4137be07", + "id": "f4bb77ad", "metadata": { "editable": true }, @@ -471,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "42c4a103", + "id": "47fc800d", "metadata": { "editable": true }, @@ -481,7 +483,7 @@ }, { "cell_type": "markdown", - "id": "9b193f51", + "id": "0fe9154b", "metadata": { "editable": true }, @@ -494,7 +496,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "02f9e114", + "id": "150c4acd", "metadata": { "collapsed": false, "editable": true @@ -559,7 +561,7 @@ }, { "cell_type": "markdown", - "id": "cf81cc5c", + "id": "9c1d64b9", "metadata": { "editable": true }, @@ -571,7 +573,7 @@ }, { "cell_type": "markdown", - "id": "905fc981", + "id": "d1929423", "metadata": { "editable": true }, @@ -586,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "2c734796", + "id": "1698b9e7", "metadata": { "editable": true }, @@ -598,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "c72d9c5f", + "id": "eff2f862", "metadata": { "editable": true }, @@ -610,7 +612,7 @@ }, { "cell_type": "markdown", - "id": "bc0af9ee", + "id": "640f9f45", "metadata": { "editable": true }, @@ -627,7 +629,7 @@ }, { "cell_type": "markdown", - "id": "7362c186", + "id": "f94fafba", "metadata": { "editable": true }, @@ -641,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "553b6180", + "id": "5d457b2e", "metadata": { "editable": true }, @@ -651,7 +653,7 @@ }, { "cell_type": "markdown", - "id": "4a4286b1", + "id": "683657ba", "metadata": { "editable": true }, @@ -663,7 +665,7 @@ }, { "cell_type": "markdown", - "id": "5873ef6a", + "id": "3d17d95b", "metadata": { "editable": true }, @@ -675,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "97f8406f", + "id": "76cd7541", "metadata": { "editable": true }, @@ -687,7 +689,7 @@ }, { "cell_type": "markdown", - "id": "d492a29a", + "id": "b88061e7", "metadata": { "editable": true }, @@ -698,7 +700,7 @@ }, { "cell_type": "markdown", - "id": "b384d3ea", + "id": "3c95fe37", "metadata": { "editable": true }, @@ -710,7 +712,7 @@ }, { "cell_type": "markdown", - "id": "257c19a6", + "id": "4f573bed", "metadata": { "editable": true }, @@ -721,7 +723,7 @@ }, { "cell_type": "markdown", - "id": "4fcc7b46", + "id": "08a700a8", "metadata": { "editable": true }, @@ -737,7 +739,7 @@ }, { "cell_type": "markdown", - "id": "cccc7482", + "id": "9bd6709b", "metadata": { "editable": true }, @@ -749,7 +751,7 @@ }, { "cell_type": "markdown", - "id": "ff305cbe", + "id": "98c81b67", "metadata": { "editable": true }, @@ -759,7 +761,7 @@ }, { "cell_type": "markdown", - "id": "ab1aa23e", + "id": "5540b76a", "metadata": { "editable": true }, @@ -771,7 +773,7 @@ }, { "cell_type": "markdown", - "id": "6799086b", + "id": "0018d823", "metadata": { "editable": true }, @@ -786,7 +788,7 @@ }, { "cell_type": "markdown", - "id": "cda3c102", + "id": "ee63f4f9", "metadata": { "editable": true }, @@ -798,7 +800,7 @@ }, { "cell_type": "markdown", - "id": "b5be8ca2", + "id": "413ff641", "metadata": { "editable": true }, @@ -809,7 +811,7 @@ }, { "cell_type": "markdown", - "id": "28868ab9", + "id": "337a2c56", "metadata": { "editable": true }, @@ -821,7 +823,7 @@ }, { "cell_type": "markdown", - "id": "e92418db", + "id": "8c3e92fe", "metadata": { "editable": true }, @@ -833,7 +835,7 @@ }, { "cell_type": "markdown", - "id": "5533c3f5", + "id": "ba84fae7", "metadata": { "editable": true }, @@ -845,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "9312b78f", + "id": "bddd73d3", "metadata": { "editable": true }, @@ -855,7 +857,7 @@ }, { "cell_type": "markdown", - "id": "edbb0614", + "id": "fce6aba6", "metadata": { "editable": true }, @@ -867,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "394f6fae", + "id": "63325aad", "metadata": { "editable": true }, @@ -881,7 +883,7 @@ }, { "cell_type": "markdown", - "id": "883253c4", + "id": "1c5878f6", "metadata": { "editable": true }, @@ -893,7 +895,7 @@ }, { "cell_type": "markdown", - "id": "7c00e2f2", + "id": "2c8a1b85", "metadata": { "editable": true }, @@ -903,7 +905,7 @@ }, { "cell_type": "markdown", - "id": "6a4795d0", + "id": "cced4ec8", "metadata": { "editable": true }, @@ -915,7 +917,7 @@ }, { "cell_type": "markdown", - "id": "deed99f4", + "id": "6efd1ce1", "metadata": { "editable": true }, @@ -925,7 +927,7 @@ }, { "cell_type": "markdown", - "id": "3ca93d73", + "id": "933753b8", "metadata": { "editable": true }, @@ -937,7 +939,7 @@ }, { "cell_type": "markdown", - "id": "89672774", + "id": "ba94450f", "metadata": { "editable": true }, @@ -948,7 +950,7 @@ }, { "cell_type": "markdown", - "id": "8a75d099", + "id": "8f174f5d", "metadata": { "editable": true }, @@ -971,7 +973,7 @@ }, { "cell_type": "markdown", - "id": "780a498d", + "id": "9ba36ed7", "metadata": { "editable": true }, @@ -983,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "f19ad43b", + "id": "b5b5ecc6", "metadata": { "editable": true }, @@ -993,7 +995,7 @@ }, { "cell_type": "markdown", - "id": "25956ba0", + "id": "e6b33699", "metadata": { "editable": true }, @@ -1005,7 +1007,7 @@ }, { "cell_type": "markdown", - "id": "4cb5ea2b", + "id": "b49a6a23", "metadata": { "editable": true }, @@ -1022,7 +1024,7 @@ }, { "cell_type": "markdown", - "id": "1d6c4caf", + "id": "e9bfd38c", "metadata": { "editable": true }, @@ -1041,7 +1043,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "2cc35ceb", + "id": "5dc4fd0e", "metadata": { "collapsed": false, "editable": true @@ -1096,7 +1098,7 @@ }, { "cell_type": "markdown", - "id": "d5c16758", + "id": "70944449", "metadata": { "editable": true }, @@ -1108,7 +1110,7 @@ }, { "cell_type": "markdown", - "id": "67640d94", + "id": "1538973c", "metadata": { "editable": true }, @@ -1123,7 +1125,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6aee13c4", + "id": "1c1fdba0", "metadata": { "collapsed": false, "editable": true @@ -1176,7 +1178,7 @@ }, { "cell_type": "markdown", - "id": "fbf16323", + "id": "dd6f78eb", "metadata": { "editable": true }, @@ -1191,7 +1193,7 @@ }, { "cell_type": "markdown", - "id": "b32612ca", + "id": "20b7afcb", "metadata": { "editable": true }, @@ -1211,7 +1213,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "ec7749b1", + "id": "4810b670", "metadata": { "collapsed": false, "editable": true @@ -1265,7 +1267,7 @@ }, { "cell_type": "markdown", - "id": "8e0c1ec4", + "id": "0696dfc9", "metadata": { "editable": true }, @@ -1280,7 +1282,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "9edb0920", + "id": "c55d1159", "metadata": { "collapsed": false, "editable": true @@ -1307,7 +1309,7 @@ }, { "cell_type": "markdown", - "id": "72beef44", + "id": "b83cd520", "metadata": { "editable": true }, @@ -1321,7 +1323,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "bf09a6eb", + "id": "5497a1d8", "metadata": { "collapsed": false, "editable": true @@ -1366,7 +1368,7 @@ }, { "cell_type": "markdown", - "id": "2d081372", + "id": "e9552a3c", "metadata": { "editable": true }, @@ -1391,7 +1393,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "cd0fad75", + "id": "623ddee7", "metadata": { "collapsed": false, "editable": true @@ -1403,7 +1405,7 @@ }, { "cell_type": "markdown", - "id": "0cf534b2", + "id": "7a61e306", "metadata": { "editable": true }, @@ -1414,7 +1416,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "6429451b", + "id": "859552c6", "metadata": { "collapsed": false, "editable": true @@ -1426,7 +1428,7 @@ }, { "cell_type": "markdown", - "id": "ed0138cc", + "id": "43d915d7", "metadata": { "editable": true }, @@ -1439,7 +1441,7 @@ }, { "cell_type": "markdown", - "id": "9b32814b", + "id": "5c8e892e", "metadata": { "editable": true }, @@ -1450,7 +1452,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "bfe200b0", + "id": "08b680f2", "metadata": { "collapsed": false, "editable": true @@ -1493,7 +1495,7 @@ }, { "cell_type": "markdown", - "id": "13691108", + "id": "fe0c7fda", "metadata": { "editable": true }, @@ -1514,7 +1516,7 @@ }, { "cell_type": "markdown", - "id": "9622bff1", + "id": "9df4ecc4", "metadata": { "editable": true }, @@ -1531,7 +1533,7 @@ }, { "cell_type": "markdown", - "id": "bffe481f", + "id": "a0a65501", "metadata": { "editable": true }, @@ -1546,7 +1548,7 @@ }, { "cell_type": "markdown", - "id": "f967b579", + "id": "8dc1194c", "metadata": { "editable": true }, @@ -1556,7 +1558,7 @@ }, { "cell_type": "markdown", - "id": "929b2860", + "id": "62d70952", "metadata": { "editable": true }, @@ -1572,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "4d50befc", + "id": "41787d1e", "metadata": { "editable": true }, @@ -1584,7 +1586,7 @@ }, { "cell_type": "markdown", - "id": "0c55b82a", + "id": "86fc7282", "metadata": { "editable": true }, @@ -1595,7 +1597,7 @@ }, { "cell_type": "markdown", - "id": "69de59e3", + "id": "8f4c640e", "metadata": { "editable": true }, @@ -1607,7 +1609,7 @@ }, { "cell_type": "markdown", - "id": "5615f25a", + "id": "f37d28ca", "metadata": { "editable": true }, @@ -1617,7 +1619,7 @@ }, { "cell_type": "markdown", - "id": "48df73cf", + "id": "9b5cb6dd", "metadata": { "editable": true }, @@ -1631,7 +1633,7 @@ }, { "cell_type": "markdown", - "id": "cb6d2d95", + "id": "f474d68a", "metadata": { "editable": true }, @@ -1643,7 +1645,7 @@ }, { "cell_type": "markdown", - "id": "7f1e6122", + "id": "ff928190", "metadata": { "editable": true }, @@ -1653,7 +1655,7 @@ }, { "cell_type": "markdown", - "id": "830f7b44", + "id": "a9e9efc2", "metadata": { "editable": true }, @@ -1665,7 +1667,7 @@ }, { "cell_type": "markdown", - "id": "1860e409", + "id": "93061994", "metadata": { "editable": true }, @@ -1677,7 +1679,7 @@ }, { "cell_type": "markdown", - "id": "49bb5265", + "id": "08acd443", "metadata": { "editable": true }, @@ -1697,7 +1699,7 @@ }, { "cell_type": "markdown", - "id": "9142c2ca", + "id": "caa94b50", "metadata": { "editable": true }, @@ -1713,7 +1715,7 @@ }, { "cell_type": "markdown", - "id": "d98a052f", + "id": "ac3e7ef2", "metadata": { "editable": true }, @@ -1729,7 +1731,7 @@ }, { "cell_type": "markdown", - "id": "3d013035", + "id": "6bd1aafd", "metadata": { "editable": true }, @@ -1740,7 +1742,7 @@ }, { "cell_type": "markdown", - "id": "67e37396", + "id": "699697a1", "metadata": { "editable": true }, @@ -1752,7 +1754,7 @@ }, { "cell_type": "markdown", - "id": "aaad3cb4", + "id": "4efbdd72", "metadata": { "editable": true }, @@ -1762,7 +1764,7 @@ }, { "cell_type": "markdown", - "id": "1c4c599d", + "id": "4bd64a59", "metadata": { "editable": true }, @@ -1774,7 +1776,7 @@ }, { "cell_type": "markdown", - "id": "947ebe9a", + "id": "358dc6db", "metadata": { "editable": true }, @@ -1784,7 +1786,7 @@ }, { "cell_type": "markdown", - "id": "f7a44bbf", + "id": "8a007c48", "metadata": { "editable": true }, @@ -1796,7 +1798,7 @@ }, { "cell_type": "markdown", - "id": "6db59422", + "id": "e0828d1d", "metadata": { "editable": true }, @@ -1818,7 +1820,7 @@ }, { "cell_type": "markdown", - "id": "d8287387", + "id": "26efa0c4", "metadata": { "editable": true }, @@ -1831,7 +1833,7 @@ }, { "cell_type": "markdown", - "id": "80c8b46c", + "id": "8af30001", "metadata": { "editable": true }, @@ -1844,7 +1846,7 @@ }, { "cell_type": "markdown", - "id": "49a9d35c", + "id": "77528641", "metadata": { "editable": true }, @@ -1854,7 +1856,7 @@ }, { "cell_type": "markdown", - "id": "78a67613", + "id": "d10154f0", "metadata": { "editable": true }, @@ -1872,7 +1874,7 @@ }, { "cell_type": "markdown", - "id": "5eb2659a", + "id": "58a6cb05", "metadata": { "editable": true }, @@ -1882,7 +1884,7 @@ }, { "cell_type": "markdown", - "id": "8a8fe9da", + "id": "87917443", "metadata": { "editable": true }, @@ -1897,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "71b1c4c8", + "id": "316440eb", "metadata": { "editable": true }, @@ -1907,7 +1909,7 @@ }, { "cell_type": "markdown", - "id": "881e1a96", + "id": "4ec22184", "metadata": { "editable": true }, @@ -1921,7 +1923,7 @@ }, { "cell_type": "markdown", - "id": "6551a36e", + "id": "9da35b82", "metadata": { "editable": true }, @@ -1931,7 +1933,7 @@ }, { "cell_type": "markdown", - "id": "c0e75b50", + "id": "61c4f7fc", "metadata": { "editable": true }, @@ -1945,7 +1947,7 @@ }, { "cell_type": "markdown", - "id": "ac897f51", + "id": "ffd39c16", "metadata": { "editable": true }, @@ -1960,7 +1962,7 @@ }, { "cell_type": "markdown", - "id": "8c19a119", + "id": "de590520", "metadata": { "editable": true }, @@ -1977,7 +1979,7 @@ }, { "cell_type": "markdown", - "id": "782860fa", + "id": "6a0e0292", "metadata": { "editable": true }, @@ -1989,7 +1991,7 @@ }, { "cell_type": "markdown", - "id": "5ec3bde0", + "id": "ec6877a5", "metadata": { "editable": true }, @@ -2003,7 +2005,7 @@ }, { "cell_type": "markdown", - "id": "ddb67ab3", + "id": "b7e72c2f", "metadata": { "editable": true }, @@ -2018,7 +2020,7 @@ }, { "cell_type": "markdown", - "id": "5a8be57f", + "id": "cae90d84", "metadata": { "editable": true }, @@ -2030,7 +2032,7 @@ }, { "cell_type": "markdown", - "id": "4a60b86d", + "id": "1ab31b86", "metadata": { "editable": true }, @@ -2041,7 +2043,7 @@ }, { "cell_type": "markdown", - "id": "9ff0c7db", + "id": "87d0d18e", "metadata": { "editable": true }, @@ -2069,7 +2071,7 @@ }, { "cell_type": "markdown", - "id": "0c9c1201", + "id": "c92a82a1", "metadata": { "editable": true }, @@ -2091,7 +2093,7 @@ }, { "cell_type": "markdown", - "id": "d8f01f9e", + "id": "b5a9af46", "metadata": { "editable": true }, @@ -2113,7 +2115,7 @@ }, { "cell_type": "markdown", - "id": "fc87a9cf", + "id": "77ee5272", "metadata": { "editable": true }, @@ -2125,7 +2127,7 @@ }, { "cell_type": "markdown", - "id": "a0b661cc", + "id": "282df4c7", "metadata": { "editable": true }, @@ -2162,7 +2164,7 @@ }, { "cell_type": "markdown", - "id": "180f4fbd", + "id": "e435596b", "metadata": { "editable": true }, @@ -2190,7 +2192,7 @@ }, { "cell_type": "markdown", - "id": "e1ddb722", + "id": "7bc1bf29", "metadata": { "editable": true }, @@ -2220,7 +2222,7 @@ }, { "cell_type": "markdown", - "id": "f30489e3", + "id": "90fef1a2", "metadata": { "editable": true }, @@ -2244,7 +2246,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "d8c2af3d", + "id": "1c59342a", "metadata": { "collapsed": false, "editable": true @@ -2257,7 +2259,7 @@ }, { "cell_type": "markdown", - "id": "a9356dfb", + "id": "79d0e3da", "metadata": { "editable": true }, @@ -2268,7 +2270,7 @@ }, { "cell_type": "markdown", - "id": "1c332ff0", + "id": "ec79a08a", "metadata": { "editable": true }, @@ -2280,7 +2282,7 @@ }, { "cell_type": "markdown", - "id": "6e4cff1f", + "id": "fa4910ae", "metadata": { "editable": true }, @@ -2290,7 +2292,7 @@ }, { "cell_type": "markdown", - "id": "239b1e2c", + "id": "e7665e13", "metadata": { "editable": true }, @@ -2302,7 +2304,7 @@ }, { "cell_type": "markdown", - "id": "6c8f0818", + "id": "90ffc363", "metadata": { "editable": true }, @@ -2316,7 +2318,7 @@ }, { "cell_type": "markdown", - "id": "3b591ee3", + "id": "3aa073fa", "metadata": { "editable": true }, @@ -2332,7 +2334,7 @@ }, { "cell_type": "markdown", - "id": "4c93f254", + "id": "e1ddc571", "metadata": { "editable": true }, @@ -2342,7 +2344,7 @@ }, { "cell_type": "markdown", - "id": "16996523", + "id": "5709f3d7", "metadata": { "editable": true }, @@ -2354,7 +2356,7 @@ }, { "cell_type": "markdown", - "id": "3262b60e", + "id": "b7b3b90f", "metadata": { "editable": true }, @@ -2364,7 +2366,7 @@ }, { "cell_type": "markdown", - "id": "dfcf41ff", + "id": "6651ef6c", "metadata": { "editable": true }, @@ -2376,7 +2378,7 @@ }, { "cell_type": "markdown", - "id": "4fbc9b44", + "id": "646be0cc", "metadata": { "editable": true }, @@ -2390,7 +2392,7 @@ }, { "cell_type": "markdown", - "id": "1972249c", + "id": "b6f528c2", "metadata": { "editable": true }, @@ -2400,7 +2402,7 @@ }, { "cell_type": "markdown", - "id": "7e4e1d18", + "id": "ae40f47b", "metadata": { "editable": true }, @@ -2411,7 +2413,7 @@ }, { "cell_type": "markdown", - "id": "64c801f1", + "id": "592c656d", "metadata": { "editable": true }, @@ -2426,7 +2428,7 @@ }, { "cell_type": "markdown", - "id": "16f76226", + "id": "aaff093b", "metadata": { "editable": true }, @@ -2436,7 +2438,7 @@ }, { "cell_type": "markdown", - "id": "51aca262", + "id": "dd177bee", "metadata": { "editable": true }, @@ -2448,7 +2450,7 @@ }, { "cell_type": "markdown", - "id": "3e7e9594", + "id": "94ead835", "metadata": { "editable": true }, @@ -2460,7 +2462,7 @@ }, { "cell_type": "markdown", - "id": "31584669", + "id": "75c4e856", "metadata": { "editable": true }, @@ -2475,7 +2477,7 @@ }, { "cell_type": "markdown", - "id": "850d7725", + "id": "228edb14", "metadata": { "editable": true }, @@ -2488,7 +2490,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "20e6fb89", + "id": "46647c95", "metadata": { "collapsed": false, "editable": true @@ -2545,7 +2547,7 @@ }, { "cell_type": "markdown", - "id": "36c762c7", + "id": "e0bb3c65", "metadata": { "editable": true }, @@ -2556,7 +2558,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "1ba65efc", + "id": "d29a0ccf", "metadata": { "collapsed": false, "editable": true @@ -2583,7 +2585,7 @@ }, { "cell_type": "markdown", - "id": "845acf36", + "id": "7d20e2cc", "metadata": { "editable": true }, @@ -2595,7 +2597,7 @@ }, { "cell_type": "markdown", - "id": "ae354b8b", + "id": "52a46927", "metadata": { "editable": true }, @@ -2607,7 +2609,7 @@ }, { "cell_type": "markdown", - "id": "a6349642", + "id": "446851f9", "metadata": { "editable": true }, @@ -2617,7 +2619,7 @@ }, { "cell_type": "markdown", - "id": "d4a4a710", + "id": "dc10da38", "metadata": { "editable": true }, @@ -2631,7 +2633,7 @@ }, { "cell_type": "markdown", - "id": "3a8c4b43", + "id": "e15d77aa", "metadata": { "editable": true }, @@ -2641,7 +2643,7 @@ }, { "cell_type": "markdown", - "id": "a4efb50c", + "id": "89cd7379", "metadata": { "editable": true }, @@ -2653,7 +2655,7 @@ }, { "cell_type": "markdown", - "id": "d180548c", + "id": "20a6a0b6", "metadata": { "editable": true }, @@ -2664,7 +2666,7 @@ }, { "cell_type": "markdown", - "id": "87acd12f", + "id": "2bcf31af", "metadata": { "editable": true }, @@ -2679,7 +2681,7 @@ }, { "cell_type": "markdown", - "id": "869f478b", + "id": "3f9a5445", "metadata": { "editable": true }, @@ -2693,7 +2695,7 @@ }, { "cell_type": "markdown", - "id": "9035cf3a", + "id": "003f6d0d", "metadata": { "editable": true }, @@ -2704,7 +2706,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "10e7bb54", + "id": "bb679580", "metadata": { "collapsed": false, "editable": true @@ -2765,7 +2767,7 @@ }, { "cell_type": "markdown", - "id": "4a349d39", + "id": "2050684c", "metadata": { "editable": true }, @@ -2787,7 +2789,7 @@ }, { "cell_type": "markdown", - "id": "33424a86", + "id": "6b20b26d", "metadata": { "editable": true }, @@ -2799,7 +2801,7 @@ }, { "cell_type": "markdown", - "id": "facda6dc", + "id": "3570021a", "metadata": { "editable": true }, @@ -2809,7 +2811,7 @@ }, { "cell_type": "markdown", - "id": "71006b8a", + "id": "c5f36ff0", "metadata": { "editable": true }, @@ -2834,7 +2836,7 @@ }, { "cell_type": "markdown", - "id": "8a7b6bd2", + "id": "a6e47b16", "metadata": { "editable": true }, @@ -2850,7 +2852,7 @@ }, { "cell_type": "markdown", - "id": "01381fbd", + "id": "5b7545c5", "metadata": { "editable": true }, @@ -2866,7 +2868,7 @@ }, { "cell_type": "markdown", - "id": "db6812a1", + "id": "fa3a49a2", "metadata": { "editable": true }, @@ -2880,7 +2882,7 @@ }, { "cell_type": "markdown", - "id": "e98af5ba", + "id": "685304e1", "metadata": { "editable": true }, @@ -2908,7 +2910,7 @@ }, { "cell_type": "markdown", - "id": "e3fb483e", + "id": "9cac2104", "metadata": { "editable": true }, @@ -2921,7 +2923,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "b78643de", + "id": "1134c2ed", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index c2190833b..870ada975 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38

    • +
  • + + Week 38: Logistic Regression and Optimization + +

    • @@ -1021,11 +1026,11 @@ example of the functionality of Scikit-Learn.

    The intercept alpha: 
- [1.99194201]
+ [1.95647867]
 Coefficient beta : 
- [[4.85108001]]
-Mean squared error: 0.28
-Variance score: 0.87
+ [[5.05401912]]
+Mean squared error: 0.25
+Variance score: 0.90
 Mean squared log error: 0.01
 Mean absolute error: 0.43
    @@ -1127,7 +1132,7 @@ a linear \(x\)-dependence we s
    _images/chapter1_33_0.png -
    0.005
+
    0.004999999999999991
    diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index ba3b56012..b156da0da 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38 +
  • + + Week 38: Logistic Regression and Optimization + +

    • @@ -1159,9 +1164,8 @@ probability that image 0 is in category 0,1,2,...,9 = 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03 9.84443254e-01 3.11507992e-04] probabilities sum up to: 1.0 -

    -
    -
    predictions = (n_inputs) = (1437,)
+
+predictions = (n_inputs) = (1437,)
 prediction for image 0: 8
 correct label for image 0: 6
    @@ -1339,7 +1343,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1673,7 +1677,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1682,7 +1686,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1691,7 +1695,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1700,7 +1704,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1709,7 +1713,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1718,7 +1722,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1727,7 +1731,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1736,11 +1740,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1749,11 +1753,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1762,11 +1766,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1775,11 +1779,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1788,11 +1792,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1801,7 +1805,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1810,11 +1814,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1823,11 +1827,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1836,11 +1840,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1849,11 +1853,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1862,37 +1866,56 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
-
    ---------------------------------------------------------------------------
-KeyboardInterrupt                         Traceback (most recent call last)
-Cell In[8], line 11
-      8 for j, lmbd in enumerate(lmbd_vals):
-      9     dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,
-     10                         n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)
----> 11     dnn.train()
-     13     DNN_numpy[i][j] = dnn
-     15     test_predict = dnn.predict(X_test)
-
-Cell In[6], line 99, in NeuralNetwork.train(self)
-     96 self.Y_data = self.Y_data_full[chosen_datapoints]
-     98 self.feed_forward()
----> 99 self.backpropagation()
-
-Cell In[6], line 64, in NeuralNetwork.backpropagation(self)
-     61 self.output_weights_gradient = np.matmul(self.a_h.T, error_output)
-     62 self.output_bias_gradient = np.sum(error_output, axis=0)
----> 64 self.hidden_weights_gradient = np.matmul(self.X_data.T, error_hidden)
-     65 self.hidden_bias_gradient = np.sum(error_hidden, axis=0)
-     67 if self.lmbd > 0.0:
-
-KeyboardInterrupt: 
+
    Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
@@ -1938,6 +1961,22 @@ Accuracy score on test set:  0.07777777777777778
    +
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
    +
    +_images/chapter10_59_1.png +_images/chapter10_59_2.png +
    @@ -1973,6 +2012,326 @@ performance overall.

    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  1e-05
+Accuracy score on test set:  0.18333333333333332
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.0001
+Accuracy score on test set:  0.18611111111111112
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.001
+Accuracy score on test set:  0.13055555555555556
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.01
+Accuracy score on test set:  0.24444444444444444
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.1
+Accuracy score on test set:  0.23333333333333334
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  1.0
+Accuracy score on test set:  0.12777777777777777
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  10.0
+Accuracy score on test set:  0.1527777777777778
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  1e-05
+Accuracy score on test set:  0.9111111111111111
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.0001
+Accuracy score on test set:  0.8888888888888888
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.01
+Accuracy score on test set:  0.8305555555555556
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.1
+Accuracy score on test set:  0.8888888888888888
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  1.0
+Accuracy score on test set:  0.8805555555555555
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  10.0
+Accuracy score on test set:  0.8944444444444445
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  1e-05
+Accuracy score on test set:  0.975
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.0001
+Accuracy score on test set:  0.9777777777777777
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.001
+Accuracy score on test set:  0.9805555555555555
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.1
+Accuracy score on test set:  0.9805555555555555
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  1.0
+Accuracy score on test set:  0.9777777777777777
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  10.0
+Accuracy score on test set:  0.9444444444444444
+
    +
    +
    /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.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.01
+Lambda =  1e-05
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.0001
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.001
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.1
+Accuracy score on test set:  0.9888888888888889
+
+Learning rate  =  0.01
+Lambda =  1.0
+Accuracy score on test set:  0.9722222222222222
+
    +
    +
    Learning rate  =  0.01
+Lambda =  10.0
+Accuracy score on test set:  0.9527777777777777
+
+Learning rate  =  0.1
+Lambda =  1e-05
+Accuracy score on test set:  0.9027777777777778
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.0001
+Accuracy score on test set:  0.8583333333333333
+
+Learning rate  =  0.1
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.01
+Accuracy score on test set:  0.9055555555555556
+
+Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on test set:  0.8805555555555555
+
+Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on test set:  0.8666666666666667
+
+Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.08611111111111111
+
+Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.10555555555555556
+
    +
    +
    Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.17777777777777778
+
+Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.08333333333333333
+
    +
    +
    Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
+
+Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
+
    +
    +
    Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.11666666666666667
+
+Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.1388888888888889
+
+Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.11388888888888889
+
    +
    +
    Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
    +
    +
    @@ -2016,6 +2375,10 @@ performance overall.

    +
    +_images/chapter10_63_0.png +_images/chapter10_63_1.png +
    @@ -2054,6 +2417,14 @@ and/or if you use anaconda, just write (or install from the gra
    +
    +
      Cell In[12], line 1
+    conda create -n tf tensorflow
+          ^
+SyntaxError: invalid syntax
+
    +
    +

    To install the current release of GPU TensorFlow

    diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index 0c8b78b4e..fbb4349ad 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38 +
  • + + Week 38: Logistic Regression and Optimization + +

    • @@ -2627,138 +2632,58 @@ Using TensorFlow results in a much better execution time. Try it!

    19 x = tuple(args[i] for i in argnum) ---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60, in jacobian(fun, x) - 50 @unary_to_nary - 51 def jacobian(fun, x): - 52 """ - 53 Returns a function which computes the Jacobian of `fun` with respect to - 54 positional argument number `argnum`, which must be a scalar or array. Unlike - (...) - 58 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...). - 59 """ ----> 60 vjp, ans = _make_vjp(fun, x) - 61 ans_vspace = vspace(ans) - 62 jacobian_shape = ans_vspace.shape + vspace(x).shape +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:64, in jacobian(fun, x) + 62 jacobian_shape = ans_vspace.shape + vspace(x).shape + 63 grads = map(vjp, ans_vspace.standard_basis()) +---> 64 return np.reshape(np.stack(grads), jacobian_shape) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x) - 8 def make_vjp(fun, x): - 9 start_node = VJPNode.new_root() ----> 10 end_value, end_node = trace(start_node, fun, x) - 11 if end_node is None: - 12 def vjp(g): return vspace(x).zeros() +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in stack(arrays, axis) + 83 def stack(arrays, axis=0): + 84 # this code is basically copied from numpy/core/shape_base.py's stack + 85 # we need it here because we want to re-implement stack in terms of the + 86 # primitives defined in this file +---> 88 arrays = [array(arr) for arr in arrays] + 89 if not arrays: + 90 raise ValueError('need at least one array to stack') -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x) - 8 with trace_stack.new_trace() as t: - 9 start_box = new_box(x, t, start_node) ----> 10 end_box = fun(start_box) - 11 if isbox(end_box) and end_box._trace == start_box._trace: - 12 return end_box._value, end_box._node +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in <listcomp>(.0) + 83 def stack(arrays, axis=0): + 84 # this code is basically copied from numpy/core/shape_base.py's stack + 85 # we need it here because we want to re-implement stack in terms of the + 86 # primitives defined in this file +---> 88 arrays = [array(arr) for arr in arrays] + 89 if not arrays: + 90 raise ValueError('need at least one array to stack') -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) - 13 else: - 14 subargs = subvals(args, zip(argnum, x)) ----> 15 return fun(*subargs, **kwargs) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14, in make_vjp.<locals>.vjp(g) +---> 14 def vjp(g): return backward_pass(g, end_node) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs) - 18 else: - 19 x = tuple(args[i] for i in argnum) ----> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21, in backward_pass(g, end_node) + 19 for node in toposort(end_node): + 20 outgrad = outgrads.pop(node) +---> 21 ingrads = node.vjp(outgrad[0]) + 22 for parent, ingrad in zip(node.parents, ingrads): + 23 outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60, in jacobian(fun, x) - 50 @unary_to_nary - 51 def jacobian(fun, x): - 52 """ - 53 Returns a function which computes the Jacobian of `fun` with respect to - 54 positional argument number `argnum`, which must be a scalar or array. Unlike - (...) - 58 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...). - 59 """ ----> 60 vjp, ans = _make_vjp(fun, x) - 61 ans_vspace = vspace(ans) - 62 jacobian_shape = ans_vspace.shape + vspace(x).shape - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x) - 8 def make_vjp(fun, x): - 9 start_node = VJPNode.new_root() ----> 10 end_value, end_node = trace(start_node, fun, x) - 11 if end_node is None: - 12 def vjp(g): return vspace(x).zeros() - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x) - 8 with trace_stack.new_trace() as t: - 9 start_box = new_box(x, t, start_node) ----> 10 end_box = fun(start_box) - 11 if isbox(end_box) and end_box._trace == start_box._trace: - 12 return end_box._value, end_box._node - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) - 13 else: - 14 subargs = subvals(args, zip(argnum, x)) ----> 15 return fun(*subargs, **kwargs) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) - 13 else: - 14 subargs = subvals(args, zip(argnum, x)) ----> 15 return fun(*subargs, **kwargs) - -Cell In[9], line 61, in g_trial(point, P) - 59 def g_trial(point,P): - 60 x,t = point ----> 61 return (1-t)*u(x) + x*(1-x)*t*deep_neural_network(P,point) - -Cell In[9], line 37, in deep_neural_network(deep_params, x) - 34 x_prev = np.concatenate((np.ones((1,num_points)), x_prev ), axis = 0) - 36 z_hidden = np.matmul(w_hidden, x_prev) ----> 37 x_hidden = sigmoid(z_hidden) - 39 # Update x_prev such that next layer can use the output from this layer - 40 x_prev = x_hidden - -Cell In[9], line 11, in sigmoid(z) - 10 def sigmoid(z): ----> 11 return 1/(1 + np.exp(-z)) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:39, in ArrayBox.__rtruediv__(self, other) ----> 39 def __rtruediv__(self, other): return anp.true_divide(other, self) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs) - 43 argnums = tuple(argnum for argnum, _ in boxed_args) - 44 ans = f_wrapped(*argvals, **kwargs) ----> 45 node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents) - 46 return new_box(ans, trace, node) - 47 else: - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36, in VJPNode.__init__(self, value, fun, args, kwargs, parent_argnums, parents) - 33 fun_name = getattr(fun, '__name__', fun) - 34 raise NotImplementedError("VJP of {} wrt argnums {} not defined" - 35 .format(fun_name, parent_argnums)) ----> 36 self.vjp = vjpmaker(parent_argnums, value, args, kwargs) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs) - 63 except KeyError: +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g) 64 raise NotImplementedError( 65 "VJP of {} wrt argnum 0 not defined".format(fun.__name__)) ----> 66 vjp = vjpfun(ans, *args, **kwargs) - 67 return lambda g: (vjp(g),) + 66 vjp = vjpfun(ans, *args, **kwargs) +---> 67 return lambda g: (vjp(g),) 68 elif L == 2: + 69 argnum_0, argnum_1 = argnums -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:53, in <lambda>(ans, x, y) - 48 defvjp(anp.logaddexp, lambda ans, x, y : unbroadcast_f(x, lambda g: g * anp.exp(x-ans)), - 49 lambda ans, x, y : unbroadcast_f(y, lambda g: g * anp.exp(y-ans))) - 50 defvjp(anp.logaddexp2, lambda ans, x, y : unbroadcast_f(x, lambda g: g * 2**(x-ans)), - 51 lambda ans, x, y : unbroadcast_f(y, lambda g: g * 2**(y-ans))) - 52 defvjp(anp.true_divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y), ----> 53 lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2)) - 54 defvjp(anp.mod, lambda ans, x, y : unbroadcast_f(x, lambda g: g), - 55 lambda ans, x, y : unbroadcast_f(y, lambda g: -g * anp.floor(x/y))) - 56 defvjp(anp.remainder, lambda ans, x, y : unbroadcast_f(x, lambda g: g), - 57 lambda ans, x, y : unbroadcast_f(y, lambda g: -g * anp.floor(x/y))) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:658, in unbroadcast_f(target, f) - 655 x = anp.real(x) - 656 return x ---> 658 def unbroadcast_f(target, f): +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660, in unbroadcast_f.<locals>.<lambda>(g) + 658 def unbroadcast_f(target, f): 659 target_meta = anp.metadata(target) - 660 return lambda g: unbroadcast(f(g), target_meta) +--> 660 return lambda g: unbroadcast(f(g), target_meta) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:651, in unbroadcast(x, target_meta, broadcast_idx) + 649 while anp.ndim(x) > target_ndim: + 650 x = anp.sum(x, axis=broadcast_idx) +--> 651 for axis, size in enumerate(target_shape): + 652 if size == 1: + 653 x = anp.sum(x, axis=axis, keepdims=True) KeyboardInterrupt:
    diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index ec85c764b..7889e0ae0 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38 +
  • + + Week 38: Logistic Regression and Optimization + +

    • diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index 37df9e46a..a84bbaa29 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38 +

  • + + Week 38: Logistic Regression and Optimization + +

    • diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index 29f9ec9cf..3e5a30fa6 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38 +

  • + + Week 38: Logistic Regression and Optimization + +

    • @@ -1270,10 +1275,10 @@ covariance matrix through the np.linalg.eig() function.

    -
    -0.0369544130635358
-3.662836197064178
-[[1.058997   3.11439407]
- [3.11439407 9.99498272]]
+
    -0.07978850553011713
+3.8232102961414203
+[[ 1.25705685  3.70704566]
+ [ 3.70704566 12.1664372 ]]
    @@ -1310,10 +1315,10 @@ a more brute force way. Here we scale the mean values for each column of the des
    -
    0.08826182458028335
-1.7026722043092946
-[[1.         0.61113781]
- [0.61113781 1.        ]]
+
    0.08931169286872433
+2.047447724189861
+[[1.         0.66729685]
+ [0.66729685 1.        ]]
    @@ -1343,30 +1348,30 @@ this matrix we easily see that it is a positive definite matrix.

    -
    [[-0.7252563  -2.26264849]
- [ 1.19052935  3.11261935]
- [-0.62158409 -2.99662602]
- [-0.06216141 -0.18120973]
- [ 1.32065614  3.50269821]
- [ 0.83995705  2.80855691]
- [ 0.1571284   0.96919021]
- [-0.03404758  1.01551815]
- [-0.23596934  0.77449804]
- [-1.82925222 -6.74259663]]
+
    [[ 2.37582891  5.4242743 ]
+ [-0.81925302 -3.60052901]
+ [ 0.37467515  3.16158074]
+ [-0.40478305 -2.22601408]
+ [-0.0776532  -1.70594608]
+ [-0.99434472 -2.2026985 ]
+ [ 0.71376303  2.77725472]
+ [ 0.94637206  2.69303453]
+ [-0.19791352  0.93366198]
+ [-1.91669165 -5.2546186 ]]
           0         1
-0 -0.725256 -2.262648
-1  1.190529  3.112619
-2 -0.621584 -2.996626
-3 -0.062161 -0.181210
-4  1.320656  3.502698
-5  0.839957  2.808557
-6  0.157128  0.969190
-7 -0.034048  1.015518
-8 -0.235969  0.774498
-9 -1.829252 -6.742597
+0  2.375829  5.424274
+1 -0.819253 -3.600529
+2  0.374675  3.161581
+3 -0.404783 -2.226014
+4 -0.077653 -1.705946
+5 -0.994345 -2.202698
+6  0.713763  2.777255
+7  0.946372  2.693035
+8 -0.197914  0.933662
+9 -1.916692 -5.254619
           0         1
-0  1.000000  0.963187
-1  0.963187  1.000000
+0  1.000000  0.932382
+1  0.932382  1.000000
    @@ -1423,37 +1428,37 @@ this matrix we easily see that it is a positive definite matrix.

         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.077382  0.076459  0.074114  0.076542  0.079074  0.064978  0.067074   
-2   0.0  0.076459  0.078118  0.070150  0.073866  0.078123  0.059624  0.062356   
-3   0.0  0.074114  0.070150  0.076618  0.077248  0.077529  0.070432  0.071572   
-4   0.0  0.076542  0.073866  0.077248  0.078731  0.080082  0.069802  0.071449   
-5   0.0  0.079074  0.078123  0.077529  0.080082  0.082803  0.068607  0.070859   
-6   0.0  0.064978  0.059624  0.070432  0.069802  0.068607  0.066865  0.067168   
-7   0.0  0.067074  0.062356  0.071572  0.071449  0.070859  0.067168  0.067809   
-8   0.0  0.069439  0.065556  0.072752  0.073253  0.073422  0.067367  0.068408   
-9   0.0  0.072092  0.069317  0.073923  0.075198  0.076333  0.067380  0.068897   
-10  0.0  0.056856  0.051021  0.063637  0.062282  0.060288  0.061848  0.061588   
-11  0.0  0.058361  0.052856  0.064604  0.063560  0.061921  0.062263  0.062231   
-12  0.0  0.060085  0.055000  0.065679  0.065007  0.063802  0.062700  0.062932   
-13  0.0  0.062053  0.057514  0.066855  0.066633  0.065963  0.063133  0.063673   
-14  0.0  0.064294  0.060470  0.068116  0.068446  0.068446  0.063525  0.064427   
+1   0.0  0.080947  0.086913  0.082764  0.085436  0.088029  0.075956  0.077683   
+2   0.0  0.086913  0.094044  0.088199  0.091365  0.094439  0.080171  0.082174   
+3   0.0  0.082764  0.088199  0.090624  0.093094  0.095465  0.086730  0.088375   
+4   0.0  0.085436  0.091365  0.093094  0.095805  0.098414  0.088651  0.090440   
+5   0.0  0.088029  0.094439  0.095465  0.098414  0.101263  0.090468  0.092401   
+6   0.0  0.075956  0.080171  0.086730  0.088651  0.090468  0.085390  0.086712   
+7   0.0  0.077683  0.082174  0.088375  0.090440  0.092401  0.086712  0.088127   
+8   0.0  0.079444  0.084218  0.090038  0.092252  0.094362  0.088038  0.089548   
+9   0.0  0.081252  0.086318  0.091731  0.094099  0.096365  0.089376  0.090985   
+10  0.0  0.068684  0.071868  0.080702  0.082126  0.083446  0.081103  0.082115   
+11  0.0  0.069978  0.073339  0.081978  0.083499  0.084915  0.082170  0.083248   
+12  0.0  0.071314  0.074859  0.083288  0.084910  0.086428  0.083260  0.084406   
+13  0.0  0.072697  0.076435  0.084637  0.086364  0.087988  0.084376  0.085592   
+14  0.0  0.074130  0.078071  0.086026  0.087864  0.089600  0.085518  0.086809   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.069439  0.072092  0.056856  0.058361  0.060085  0.062053  0.064294  
-2   0.065556  0.069317  0.051021  0.052856  0.055000  0.057514  0.060470  
-3   0.072752  0.073923  0.063637  0.064604  0.065679  0.066855  0.068116  
-4   0.073253  0.075198  0.062282  0.063560  0.065007  0.066633  0.068446  
-5   0.073422  0.076333  0.060288  0.061921  0.063802  0.065963  0.068446  
-6   0.067367  0.067380  0.061848  0.062263  0.062700  0.063133  0.063525  
-7   0.068408  0.068897  0.061588  0.062231  0.062932  0.063673  0.064427  
-8   0.069486  0.070556  0.061154  0.062058  0.063064  0.064166  0.065352  
-9   0.070556  0.072344  0.060453  0.061656  0.063017  0.064547  0.066256  
-10  0.061154  0.060453  0.058245  0.058254  0.058234  0.058152  0.057961  
-11  0.062058  0.061656  0.058254  0.058427  0.058593  0.058721  0.058770  
-12  0.063064  0.063017  0.058234  0.058593  0.058970  0.059340  0.059669  
-13  0.064166  0.064547  0.058152  0.058721  0.059340  0.059992  0.060652  
-14  0.065352  0.066256  0.057961  0.058770  0.059669  0.060652  0.061706  
+1   0.079444  0.081252  0.068684  0.069978  0.071314  0.072697  0.074130  
+2   0.084218  0.086318  0.071868  0.073339  0.074859  0.076435  0.078071  
+3   0.090038  0.091731  0.080702  0.081978  0.083288  0.084637  0.086026  
+4   0.092252  0.094099  0.082126  0.083499  0.084910  0.086364  0.087864  
+5   0.094362  0.096365  0.083446  0.084915  0.086428  0.087988  0.089600  
+6   0.088038  0.089376  0.081103  0.082170  0.083260  0.084376  0.085518  
+7   0.089548  0.090985  0.082115  0.083248  0.084406  0.085592  0.086809  
+8   0.091068  0.092607  0.083119  0.084319  0.085548  0.086808  0.088102  
+9   0.092607  0.094254  0.084122  0.085392  0.086694  0.088030  0.089404  
+10  0.083119  0.084122  0.078238  0.079089  0.079953  0.080832  0.081728  
+11  0.084319  0.085392  0.079089  0.079989  0.080903  0.081835  0.082785  
+12  0.085548  0.086694  0.079953  0.080903  0.081871  0.082857  0.083864  
+13  0.086808  0.088030  0.080832  0.081835  0.082857  0.083900  0.084967  
+14  0.088102  0.089404  0.081728  0.082785  0.083864  0.084967  0.086096
    diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 91c973fc4..8c33c476d 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output" Exercises week 38 +
  • + + Week 38: Logistic Regression and Optimization + +

    • @@ -824,10 +829,10 @@ number \(i\) is left out. Usin

    -
    Runtime: 0.165272 sec
+
    Runtime: 0.139224 sec
 Jackknife Statistics :
 original           bias      std. error
- 99.6801        99.6702        0.149483
+ 99.9792        99.9692        0.149921
    @@ -1046,7 +1051,7 @@ theorem.

    Bootstrap Statistics :
 original           bias      std. error
- 100.121   15.022        100.121        0.149904
+ 99.8978  15.0232        99.8962        0.149063
    @@ -1258,14 +1263,14 @@ Error: 0.10398646080125035 Bias^2: 0.1007711427354898 Var: 0.0032153180657605116 0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032 -Polynomial degree: 3 +
    +
    +
    Polynomial degree: 3
 Error: 0.06547790180152355
 Bias^2: 0.06208238634231949
 Var: 0.0033955154592040936
 0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359
-
    -
    -
    Polynomial degree: 4
+Polynomial degree: 4
 Error: 0.06844519414009445
 Bias^2: 0.06453579006728324
 Var: 0.003909404072811226
@@ -1275,14 +1280,14 @@ Error: 0.05227921801205686
 Bias^2: 0.0481872773043029
 Var: 0.004091940707753939
 0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
-Polynomial degree: 6
+
    +
    +
    Polynomial degree: 6
 Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
 0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
-
    -
    -
    Polynomial degree: 7
+Polynomial degree: 7
 Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
@@ -1297,7 +1302,9 @@ Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
 0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
-Polynomial degree: 10
+
    +
    +
    Polynomial degree: 10
 Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
@@ -1307,9 +1314,7 @@ Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
 0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
-
    -
    -
    Polynomial degree: 12
+Polynomial degree: 12
 Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
@@ -1636,9 +1641,9 @@ Mean squared error on training data: 0.00060704
 Mean squared error on test data: 3262.26814548
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -1872,7 +1877,7 @@ cross-validation (LOOCV).
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
@@ -2761,7 +2766,7 @@ linear system as an equation would reduce this down to
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2905,7 +2910,7 @@ with the form utilized in linear regression, viz.
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2945,7 +2950,7 @@ cost function is given by
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2980,7 +2985,7 @@ cost function is given by
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3033,43 +3038,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
    -
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+
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    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
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diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index dbf89d63a..5429314b4 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html
index 082e6c7e3..270f89f10 100644
--- a/doc/LectureNotes/_build/html/chapter5.html
+++ b/doc/LectureNotes/_build/html/chapter5.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index 08392b396..5e70bd202 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -752,9 +757,9 @@ predicting the target features of query instances is as follows:
    
 
    
 
    
 
    2nd degree coefficients:
-zero power:  -2.767367275553824
-first power:  0.024011020121022356
-second power:  -0.00021270681344726395
+zero power:  -1.4725246793626128
+first power:  -0.08935132374099551
+second power:  0.00034688149480437765
 
    
 
    
 _images/chapter6_1_1.png
@@ -1621,7 +1626,9 @@ Test set accuracy with Logistic Regression: 0.94
 
    
 
    
 
    Test set accuracy with SVM: 0.63
-Test set accuracy with Decision Trees: 0.90
+
    
+
    
+
    Test set accuracy with Decision Trees: 0.90
 Test set accuracy Logistic Regression with scaled data: 0.96
 Test set accuracy SVM with scaled data: 0.96
 Test set accuracy with Decision Trees and scaled data: 0.89
diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html
index 31e009717..9cb7d94ef 100644
--- a/doc/LectureNotes/_build/html/chapter7.html
+++ b/doc/LectureNotes/_build/html/chapter7.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 97a59af11..39f92bb2d 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -706,10 +711,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.10541723644166373
-4.575870409023631
-[[0.84972787 2.5321613 ]
- [2.5321613  8.59875207]]
+
    0.14934258650797513
+4.548263635652985
+[[ 1.0875061   3.3260513 ]
+ [ 3.3260513  11.10994958]]
 
    
 
    
 
    
@@ -749,10 +754,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.0768805855280187
-1.6568154596723088
-[[1.         0.69438869]
- [0.69438869 1.        ]]
+
    0.09291556244521161
+2.096511363983559
+[[1.        0.7198234]
+ [0.7198234 1.       ]]
 
    
 
    
 
    
@@ -781,30 +786,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[-1.6629598  -6.60625144]
- [-1.59424119 -4.17676247]
- [ 0.13699574 -1.26680052]
- [ 1.67275915  7.04206048]
- [ 1.48931464  4.73718419]
- [ 0.82341746  3.16411163]
- [ 0.56141009  1.13137881]
- [ 0.38616125  0.98338288]
- [-1.28000355 -3.69734609]
- [-0.53285379 -1.31095745]]
+
    [[ 0.20480187  0.26586817]
+ [ 0.72601722  1.13675593]
+ [ 0.02649469 -0.9834505 ]
+ [ 0.97548406  1.6266783 ]
+ [-1.59078383 -4.25673276]
+ [-0.40596423 -0.31486917]
+ [-0.34654596 -1.94627617]
+ [-1.33062878 -3.73785069]
+ [ 2.22810365  9.25389111]
+ [-0.48697869 -1.04401421]]
           0         1
-0 -1.662960 -6.606251
-1 -1.594241 -4.176762
-2  0.136996 -1.266801
-3  1.672759  7.042060
-4  1.489315  4.737184
-5  0.823417  3.164112
-6  0.561410  1.131379
-7  0.386161  0.983383
-8 -1.280004 -3.697346
-9 -0.532854 -1.310957
+0  0.204802  0.265868
+1  0.726017  1.136756
+2  0.026495 -0.983450
+3  0.975484  1.626678
+4 -1.590784 -4.256733
+5 -0.405964 -0.314869
+6 -0.346546 -1.946276
+7 -1.330629 -3.737851
+8  2.228104  9.253891
+9 -0.486979 -1.044014
           0         1
-0  1.000000  0.972149
-1  0.972149  1.000000
+0  1.000000  0.950423
+1  0.950423  1.000000
 
    
 
    
 
    
@@ -861,37 +866,37 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.083793  0.077516  0.081955  0.073791  0.066763  0.071803  0.064695   
-2   0.0  0.077516  0.074112  0.078363  0.072304  0.066838  0.070556  0.064873   
-3   0.0  0.081955  0.078363  0.084619  0.077941  0.071937  0.076833  0.070462   
-4   0.0  0.073791  0.072304  0.077941  0.073102  0.068533  0.072132  0.067141   
-5   0.0  0.066763  0.066838  0.071937  0.068533  0.065108  0.067671  0.063791   
-6   0.0  0.071803  0.070556  0.076833  0.072132  0.067671  0.071608  0.066653   
-7   0.0  0.064695  0.064873  0.070462  0.067141  0.063791  0.066653  0.062797   
-8   0.0  0.058641  0.059876  0.064878  0.062637  0.060171  0.062173  0.059201   
-9   0.0  0.053462  0.055477  0.059977  0.058582  0.056826  0.058133  0.055876   
-10  0.0  0.061862  0.062199  0.067948  0.064820  0.061637  0.064615  0.060898   
-11  0.0  0.056026  0.057316  0.062429  0.060312  0.057964  0.060087  0.057216   
-12  0.0  0.051042  0.053036  0.057609  0.056287  0.054609  0.056041  0.053854   
-13  0.0  0.046764  0.049276  0.053387  0.052692  0.051554  0.052427  0.050796   
-14  0.0  0.043072  0.045963  0.049677  0.049481  0.048781  0.049196  0.048020   
+1   0.0  0.072147  0.072728  0.071758  0.072209  0.072843  0.064428  0.064668   
+2   0.0  0.072728  0.075385  0.069979  0.071530  0.073408  0.061386  0.062260   
+3   0.0  0.071758  0.069979  0.076968  0.076244  0.075522  0.072286  0.071935   
+4   0.0  0.072209  0.071530  0.076244  0.076161  0.076150  0.070898  0.070950   
+5   0.0  0.072843  0.073408  0.075522  0.076150  0.076934  0.069399  0.069885   
+6   0.0  0.064428  0.061386  0.072286  0.070898  0.069399  0.069873  0.069179   
+7   0.0  0.064668  0.062260  0.071935  0.070950  0.069885  0.069179  0.068758   
+8   0.0  0.065062  0.063354  0.071655  0.071103  0.070514  0.068494  0.068360   
+9   0.0  0.065616  0.064690  0.071433  0.071356  0.071291  0.067793  0.067967   
+10  0.0  0.057287  0.053787  0.066153  0.064505  0.062691  0.065212  0.064382   
+11  0.0  0.057387  0.054286  0.065949  0.064573  0.063048  0.064834  0.064202   
+12  0.0  0.057607  0.054932  0.065830  0.064739  0.063518  0.064507  0.064077   
+13  0.0  0.057951  0.055737  0.065788  0.065001  0.064107  0.064218  0.064000   
+14  0.0  0.058422  0.056717  0.065818  0.065358  0.064820  0.063954  0.063959   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.058641  0.053462  0.061862  0.056026  0.051042  0.046764  0.043072  
-2   0.059876  0.055477  0.062199  0.057316  0.053036  0.049276  0.045963  
-3   0.064878  0.059977  0.067948  0.062429  0.057609  0.053387  0.049677  
-4   0.062637  0.058582  0.064820  0.060312  0.056287  0.052692  0.049481  
-5   0.060171  0.056826  0.061637  0.057964  0.054609  0.051554  0.048781  
-6   0.062173  0.058133  0.064615  0.060087  0.056041  0.052427  0.049196  
-7   0.059201  0.055876  0.060898  0.057216  0.053854  0.050796  0.048020  
-8   0.056328  0.053599  0.057420  0.054434  0.051645  0.049060  0.046678  
-9   0.053599  0.051368  0.054197  0.051787  0.049479  0.047299  0.045258  
-10  0.057420  0.054197  0.059243  0.055653  0.052370  0.049380  0.046663  
-11  0.054434  0.051787  0.055653  0.052738  0.050015  0.047489  0.045161  
-12  0.051645  0.049479  0.052370  0.050015  0.047760  0.045630  0.043637  
-13  0.049060  0.047299  0.049380  0.047489  0.045630  0.043839  0.042136  
-14  0.046678  0.045258  0.046663  0.045161  0.043637  0.042136  0.040684  
+1   0.065062  0.065616  0.057287  0.057387  0.057607  0.057951  0.058422  
+2   0.063354  0.064690  0.053787  0.054286  0.054932  0.055737  0.056717  
+3   0.071655  0.071433  0.066153  0.065949  0.065830  0.065788  0.065818  
+4   0.071103  0.071356  0.064505  0.064573  0.064739  0.065001  0.065358  
+5   0.070514  0.071291  0.062691  0.063048  0.063518  0.064107  0.064820  
+6   0.068494  0.067793  0.065212  0.064834  0.064507  0.064218  0.063954  
+7   0.068360  0.067967  0.064382  0.064202  0.064077  0.064000  0.063959  
+8   0.068268  0.068206  0.063526  0.063549  0.063637  0.063781  0.063977  
+9   0.068206  0.068504  0.062616  0.062853  0.063163  0.063545  0.063995  
+10  0.063526  0.062616  0.061724  0.061284  0.060870  0.060471  0.060071  
+11  0.063549  0.062853  0.061284  0.060994  0.060734  0.060490  0.060251  
+12  0.063637  0.063163  0.060870  0.060734  0.060629  0.060547  0.060475  
+13  0.063781  0.063545  0.060471  0.060490  0.060547  0.060631  0.060735  
+14  0.063977  0.063995  0.060071  0.060251  0.060475  0.060735  0.061025  
 
    
 
    
 
    
@@ -1080,10 +1085,10 @@ We can write our own code or simply use either the functionaly of numpy<
 
    
 
    
 
              0         1
-0  3.986362  1.994474
-1  1.994474  2.001468
-[[3.98636199 1.99447418]
- [1.99447418 2.00146807]]
+0  3.935972  1.991047
+1  1.991047  2.000783
+[[3.93597168 1.99104747]
+ [1.99104747 2.00078324]]
 
    
 
    
 
    
@@ -1110,8 +1115,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 
    
 
    
 
    Centered covariance using own code
-[[3.98636199 1.99447418]
- [1.99447418 2.00146807]]
+[[3.93597168 1.99104747]
+ [1.99104747 2.00078324]]
 
    
 
    
 _images/chapter8_65_1.png
@@ -1171,16 +1176,16 @@ questions.
    
 
    
 
    
 
    Eigenvalues of Covariance matrix
-5.221666864828611
-0.766163196245293
+5.182086698929565
+0.7546682196464342
 First eigenvector
-[0.85014487 0.52654886]
+[0.84767088 0.53052247]
 Second eigenvector
-[-0.52654886  0.85014487]
+[-0.53052247  0.84767088]
 
    
 
    
 
    Eigenvector of largest eigenvalue
-[-0.85014487 -0.52654886]
+[0.84767088 0.53052247]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html
index 176b3c4aa..e735f7c49 100644
--- a/doc/LectureNotes/_build/html/chapter9.html
+++ b/doc/LectureNotes/_build/html/chapter9.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html
index 38b4f0676..518a610fd 100644
--- a/doc/LectureNotes/_build/html/chapteroptimization.html
+++ b/doc/LectureNotes/_build/html/chapteroptimization.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html
index 750cdf25a..29eb31190 100644
--- a/doc/LectureNotes/_build/html/clustering.html
+++ b/doc/LectureNotes/_build/html/clustering.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html
index 7260320cd..2b91f64c7 100644
--- a/doc/LectureNotes/_build/html/exercisesweek34.html
+++ b/doc/LectureNotes/_build/html/exercisesweek34.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek35.html b/doc/LectureNotes/_build/html/exercisesweek35.html
index b3b36aa11..db3f59d50 100644
--- a/doc/LectureNotes/_build/html/exercisesweek35.html
+++ b/doc/LectureNotes/_build/html/exercisesweek35.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek36.html b/doc/LectureNotes/_build/html/exercisesweek36.html
index f80eb4cc1..d90623f48 100644
--- a/doc/LectureNotes/_build/html/exercisesweek36.html
+++ b/doc/LectureNotes/_build/html/exercisesweek36.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek37.html b/doc/LectureNotes/_build/html/exercisesweek37.html
index c09db31f2..f08fa4353 100644
--- a/doc/LectureNotes/_build/html/exercisesweek37.html
+++ b/doc/LectureNotes/_build/html/exercisesweek37.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek38.html b/doc/LectureNotes/_build/html/exercisesweek38.html
index 235bc9f5a..f2accfd0f 100644
--- a/doc/LectureNotes/_build/html/exercisesweek38.html
+++ b/doc/LectureNotes/_build/html/exercisesweek38.html
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -504,10 +509,10 @@ You can follow the code example in the jupyter-book at Week 37: Statistical interpretations and Resampling Methods
    
         
    
     
-    
+    
     
    
         
    next
    
-        
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
    
+        
    Week 38: Logistic Regression and Optimization
    
     
    
     
         
diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 2e55d00a7..f09c3e120 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -608,8 +613,8 @@ matrices and vectors.
    
 
    
 
    
 
    
-
    [ 0.93937564  0.92308867  0.34427423 -1.37685349  2.80413696  0.25555619
-  1.121292   -0.42392359 -0.13913033 -0.7228852 ]
+
    [-0.29015871  0.69417174 -1.07998756  0.34332677  0.19547923 -1.09755017
+  0.86197958 -0.15546887  0.14369927  1.96251859]
 
    
 
    
 
    
@@ -830,36 +835,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
 
    
 
    
 
    
-
    [[7.39090680e-02 6.22755839e-01 6.49123502e-01 2.88135577e-01
-  8.94704941e-01 3.03201179e-01 3.18840034e-01 8.41366128e-01
-  8.95033155e-01 5.99812668e-01]
- [9.16312434e-01 9.43984553e-02 9.29253213e-01 5.26464303e-01
-  2.21390371e-01 1.24224776e-01 6.89710385e-01 4.27861634e-01
-  7.43779698e-03 8.17834506e-01]
- [1.51914183e-01 5.02447063e-01 5.99647992e-01 8.36235052e-01
-  8.14046224e-01 1.06662069e-01 2.91416944e-01 5.89463761e-01
-  7.07712197e-01 4.57819238e-01]
- [6.21192602e-01 1.27597777e-01 9.92527681e-01 3.95743466e-01
-  2.66757105e-01 7.72309979e-01 1.18019623e-01 9.40415979e-03
-  9.27352535e-01 9.26378176e-02]
- [3.52989287e-02 1.01080465e-01 7.27602210e-01 4.01826811e-01
-  2.33826500e-01 8.47478922e-01 4.73646130e-01 4.59491404e-01
-  1.56857739e-01 8.65889641e-01]
- [1.29100624e-01 1.30387411e-01 2.49156266e-01 1.86123808e-01
-  3.06942115e-01 7.77873845e-01 6.61979664e-01 7.08699295e-01
-  4.47297587e-02 5.88738394e-01]
- [5.17405911e-01 9.84789579e-01 7.30334592e-01 5.61602014e-01
-  9.95053694e-01 5.73305640e-01 9.36545697e-01 5.29033075e-01
-  1.00989834e-04 6.96379806e-01]
- [8.23093598e-01 9.99201684e-01 9.42197297e-01 8.48268005e-01
-  1.65900052e-01 2.60605083e-01 3.83884553e-01 5.91410559e-02
-  6.89766337e-01 7.91434750e-01]
- [6.39621036e-03 4.53512750e-01 2.85259666e-01 6.82623709e-01
-  4.62905281e-01 9.88236598e-01 6.74272285e-02 5.17547294e-01
-  5.67238764e-01 5.67819487e-01]
- [5.16172219e-01 1.42083463e-01 2.01779091e-01 8.29541992e-02
-  8.23994188e-01 1.28183460e-01 4.64564368e-01 4.61106937e-01
-  7.93891335e-01 6.25458506e-01]]
+
    [[0.08233627 0.43727575 0.01759442 0.01179415 0.34483937 0.32733949
+  0.71332117 0.20347912 0.57521514 0.56042437]
+ [0.64909048 0.55179339 0.13847167 0.04224929 0.89540936 0.5142856
+  0.85871255 0.75080943 0.34026086 0.50850373]
+ [0.03239393 0.87884833 0.41310208 0.82418008 0.01014331 0.24284399
+  0.93668828 0.79247398 0.48062913 0.77803692]
+ [0.73068535 0.47910545 0.95715036 0.11844773 0.76686148 0.31453949
+  0.6029291  0.5443909  0.29398019 0.96585824]
+ [0.0179826  0.51643579 0.53723188 0.03844073 0.25989189 0.20834188
+  0.98619989 0.40833375 0.52876551 0.76612827]
+ [0.44830934 0.94099173 0.07123177 0.96305058 0.11596834 0.69756791
+  0.60664448 0.89457785 0.86016288 0.33615742]
+ [0.35243372 0.77465911 0.37532078 0.38399622 0.20728198 0.1108407
+  0.83020679 0.48170743 0.99003341 0.99956033]
+ [0.82832579 0.42553096 0.02355756 0.28542801 0.96894842 0.70711022
+  0.8825782  0.04072235 0.24369936 0.79622905]
+ [0.60762863 0.21699817 0.20218457 0.63071772 0.02358547 0.62886648
+  0.44286735 0.78745927 0.30982436 0.76277254]
+ [0.52173381 0.9495484  0.47433659 0.73203674 0.95226607 0.40072098
+  0.35098705 0.46544987 0.66456459 0.25512598]]
 
    
 
    
 
    
@@ -919,13 +914,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.2160073466353432
-4.720889407643293
-1.0362483569261072
-[[ 1.17260271  3.60999067  4.15283463]
- [ 3.60999067 12.23423987 13.18824338]
- [ 4.15283463 13.18824338 21.31571995]]
-[31.70587399  0.09156055  2.92512799]
+
    -0.0018310257999764163
+4.092608577084595
+0.31555734196707536
+[[ 0.78671736  2.38337498  2.04657426]
+ [ 2.38337498  8.33235647  6.05002471]
+ [ 2.04657426  6.05002471 10.13152828]]
+[15.98413606  0.08227668  3.18418938]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/project1.html b/doc/LectureNotes/_build/html/project1.html
index 5ea5ab481..0278b47a5 100644
--- a/doc/LectureNotes/_build/html/project1.html
+++ b/doc/LectureNotes/_build/html/project1.html
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -292,6 +292,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -1033,11 +1038,11 @@ of code developers and contributors keeps increasing.
    
             
                 
 
     
-    
+    
         
         
    
             
    previous
    
-            
    Exercises week 38
    
+            
    Week 38: Logistic Regression and Optimization
    
         
    
         
 
    
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 1646a3b8d..cd965b9a5 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -291,6 +291,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 6d4b0046a..13dcaf44a 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization"],titleterms:{"1":[0,15,16,17,18,22,27,28],"16":31,"2":[0,15,16,17,18,27,28,29],"2023":25,"2024":22,"21":[],"23":[],"26":28,"3":[0,15,16,27,28],"34":[15,27],"35":[16,28],"36":[17,29],"37":[18,30],"38":[19,31],"4":[0,28],"5":0,"7":22,"9":30,"case":[8,10,24,28,29,31],"class":31,"do":[1,29,30],"final":[12,28,29],"function":[0,1,6,7,8,10,11,12,13,22,24,27,28,29,30,31],"import":[5,21,27,28,29],"new":[4,29,30],A:[0,1,4,8,9,27,29,30,31],And:[27,28,29,31],For:28,In:25,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,17,20,27,28,29,30,31],To:[27,28],With:[4,29],about:[27,28],abov:29,activ:[1,12,29],ad:[0,6,17,22,27,28],adaboost:10,adagrad:13,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9,31],ai:[22,27],aim:[8,9,17,18,19,27],aka:[27,28],algebra:[21,27],algorithm:[9,10,11,12,28],algortithm:[13,31],all:8,an:[0,4,10,27],analys:[5,28],analysi:[0,5,6,11,20,22,24,27,28,29,30],analyt:[0,16,17],ani:[13,31],anoth:[9,29,30],appli:20,approach:[0,8,14,27,30],approxim:12,architectur:1,arrai:[21,27],assist:25,assumpt:[29,30],august:28,autocorrel:24,autograd:[2,13],automat:13,b:[17,22],back:[1,11,12],background:[20,22,30],bag:10,base:[13,30],basic:[0,5,7,9,10,11,21,28,29,30,31],batch:1,bay:[5,29,30],befor:11,beta:[29,30],better:8,bia:[6,22,30],binari:1,bind:27,bird:10,boldsymbol:[28,29,30],boost:10,bootstrap:[6,10,30],boston:0,breast:1,brief:[27,30,31],bring:12,build:[1,3,9],c:[22,27],calcul:28,can:[27,30],cancer:[1,7,9,11,31],cart:9,center:28,central:[13,20,24,30,31],chain:12,challeng:31,chang:10,channel:27,chi:[0,27],choos:1,cifar01:3,classic:11,classif:[1,9,10,31],classifi:[8,31],clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,27,28,29,30,31],collect:[1,3],come:31,commun:27,compact:31,compar:[2,10],comparison:29,complet:28,complex:[0,6,22,28],complic:6,compon:11,comput:9,computation:30,computerlab:27,con:9,concept:24,condit:[29,30,31],confid:30,conjug:13,contn:27,convex:[8,13,31],convolut:[3,12],correctli:[29,30],correl:[11,28,31],correspond:31,cost:[1,10,28,29,30,31],cours:[20,26,27],covari:[5,11,24,28],cover:27,cross:[6,22,30,31],cython:27,d:22,data:[0,1,3,6,7,9,11,15,16,20,22,24,27,28,29,31],dataset:[1,3],deadlin:[22,27],decai:2,decis:[9,10],decomposit:[5,11,17,21,28],deep:[1,2,27,31],defin:[1,27],degre:[0,28],deliveri:22,delta:30,dens:[0,27],deriv:[5,12,28,29,30,31],descent:[2,10,13,31],descript:22,design:28,detail:[3,27],develop:1,diagon:11,differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8,22,28],disadvantag:9,discret:24,discuss:31,distribut:[5,24,29,30],distrubut:30,doe:[28,29],domain:24,down:1,dropout:1,e:22,each:31,economi:28,electron:22,element:[0,24,27],elimin:21,energi:27,ensembl:10,entropi:[9,31],environ:[0,15,27],equat:[0,2,12,27,28,29,31],error:[0,10,27,28,30],essenti:27,estim:[29,30],etc:27,euler:2,evalu:1,exampl:[0,1,2,3,4,6,7,8,9,10,27,28,29,30,31],exercis:[0,6,15,16,17,18,19,27,28],expect:[18,24,29,30],expens:30,experi:24,explor:[0,15,16,27],exponenti:2,express:[17,18,28,31],extend:31,extrapol:4,extrem:[10,27],ey:10,f:22,fall:25,famili:[1,27,28],famou:21,fantast:28,featur:[9,21,28],feed:[1,12],find:30,fine:1,first:[4,12,27,28,29,31],fit:[0,10,27,29],fix:28,fold:[30,31],forc:3,forest:10,format:[22,27],forward:[1,2,12],fourier:3,frank:[6,22,28],freedom:[0,28],frequent:28,frequentist:[0,27],fridai:[],from:[5,10,12,28,29,30,31],full:2,further:[3,5,28],g:22,gan:4,gaussian:21,gd:13,gener:[4,9,27],geometr:[11,31],gini:9,good:[0,27],grade:[25,27],gradient:[1,2,10,13,31],grid:31,group:31,growth:2,ha:20,handl:[21,27,28],happen:[29,30],hessian:[28,31],hidden:2,histogram:30,homework:31,hous:0,how:31,hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,ideal:31,ident:[29,30],identifi:30,ii:27,iid:[29,30],illustr:29,implement:1,implic:[5,28],improv:1,includ:[13,31],increment:11,independ:[29,30],index:9,inform:25,input:2,instal:[20,22,27],instructor:25,intercept:28,interpret:[5,11,27,28,29,30,31],interv:30,introduc:[11,13,28],introduct:[0,6,20,21,22,27],invers:[5,21,29],invert:28,iter:10,its:28,jacobian:28,jax:13,julia:27,jungl:10,k:[30,31],kera:[1,3],kernel:[8,11],lab:[30,31],lagrangian:8,lambda:31,lasso:[5,6,22,28,29,30,31],last:[28,31],later:[5,28],layer:[1,2,3,12],learn:[0,1,2,11,13,14,15,16,20,22,27,28,29,30,31],least:[5,6,18,22,27,28,29],lectur:[27,28,29,30,31],level:10,librari:[20,27],likelihood:[7,29,30,31],limit:[1,13,24,30,31],linear:[0,8,13,21,27,28,29,31],link:[5,11,23,26,28,29,30],literatur:22,logist:[7,27,31],loss:[28,31],lu:21,machin:[0,8,13,20,22,27,31],made:[29,30],main:[24,27],make:[0,9,10,15,16,27,28],mani:[10,12],manipul:28,margin:[29,30],mass:27,materi:[22,23,27,28,29,30,31],math:[5,28],mathemat:[3,5,8,28],matric:[5,21,27,29],matrix:[1,5,11,12,21,27,28,29,31],matter:[0,27],max:28,maximum:[29,30,31],mean:[0,28,29,31],measur:31,meet:[5,10,24,27,28],mercer:8,method:[6,9,10,13,22,27,30,31],midnight:22,min:28,minim:[27,31],ml:27,mle:[29,30],mlp:12,mnist:[3,4],model:[0,1,4,6,12,27],momentum:13,mondai:[28,29,30,31],moon:[8,9],more:[3,6,21,22,27,28,29,30,31],multilay:12,multipl:[1,3],multipli:8,need:[22,27],net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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html
index 69fec610d..51caeab12 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -980,27 +985,27 @@ uncorrelated.
    
 
    
 
    
 
    
-
    2.076776262573027
-[[ 5.24692014 11.86416941  1.87818331  3.59971727  6.93989887  8.39223923
-   1.96991192  6.69391797  5.22667819  3.39929428]
- [11.86416941 26.82688363  4.24688853  8.13956654 15.69227923 18.97626519
-   4.45430236 15.13607502 11.81839897  7.68637642]
- [ 1.87818331  4.24688853  0.67231298  1.2885519   2.48420061  3.0040792
-   0.70514808  2.39614949  1.87093752  1.21680864]
- [ 3.59971727  8.13956654  1.2885519   2.46963249  4.76120718  5.75760403
-   1.3514835   4.59244881  3.58583003  2.33212971]
- [ 6.93989887 15.69227923  2.48420061  4.76120718  9.17913653 11.1000911
-   2.60552651  8.85378708  6.91312563  4.49611541]
- [ 8.39223923 18.97626519  3.0040792   5.75760403 11.1000911  13.42305151
-   3.15079545 10.70665448  8.35986305  5.43703545]
- [ 1.96991192  4.45430236  0.70514808  1.3514835   2.60552651  3.15079545
-   0.73958682  2.51317506  1.96231225  1.27623637]
- [ 6.69391797 15.13607502  2.39614949  4.59244881  8.85378708 10.70665448
-   2.51317506  8.53996947  6.66809369  4.33675308]
- [ 5.22667819 11.81839897  1.87093752  3.58583003  6.91312563  8.35986305
-   1.96231225  6.66809369  5.20651434  3.38618024]
- [ 3.39929428  7.68637642  1.21680864  2.33212971  4.49611541  5.43703545
-   1.27623637  4.33675308  3.38618024  2.20228273]]
+
    0.9841870203276504
+[[ 3.57492326  2.52888979  5.61382924  2.32419549  4.2986082   3.83718206
+   4.7658599   4.76527062  8.55196383  8.05747369]
+ [ 2.52888979  1.78892891  3.97120565  1.64412879  3.0408223   2.71441086
+   3.37135473  3.37093787  6.04963309  5.69983227]
+ [ 5.61382924  3.97120565  8.81559586  3.64976691  6.75025744  6.02566356
+   7.48399942  7.48307405 13.42945319 12.65293772]
+ [ 2.32419549  1.64412879  3.64976691  1.51104913  2.79469098  2.49470005
+   3.09846933  3.09808622  5.55996153  5.23847441]
+ [ 4.2986082   3.0408223   6.75025744  2.79469098  5.16879134  4.61395701
+   5.73063053  5.72992196 10.28316949  9.68857788]
+ [ 3.83718206  2.71441086  6.02566356  2.49470005  4.61395701  4.11868033
+   5.11548661  5.1148541   9.1793417   8.64857542]
+ [ 4.7658599   3.37135473  7.48399942  3.09846933  5.73063053  5.11548661
+   6.35354073  6.35275513 11.40093324 10.74171049]
+ [ 4.76527062  3.37093787  7.48307405  3.09808622  5.72992196  5.1148541
+   6.35275513  6.35196964 11.39952356 10.74038232]
+ [ 8.55196383  6.04963309 13.42945319  5.55996153 10.28316949  9.1793417
+  11.40093324 11.39952356 20.4580854  19.2751616 ]
+ [ 8.05747369  5.69983227 12.65293772  5.23847441  9.68857788  8.64857542
+  10.74171049 10.74038232 19.2751616  18.16063661]]
 
    
 
    
 
    
@@ -1268,15 +1273,15 @@ more practically oriented methods like the blocking technique.
    
 
    
 
    
 
    
-
    0.09790216282081063
-4.184866696796263
-0.2373286546935638
-0.9722865317690382 10.417644792969273 7.499394932852416
-3.055091049054481 2.2630723514841553 7.22977659964799
-[[ 0.97228653  3.05509105  2.26307235]
- [ 3.05509105 10.41764479  7.2297766 ]
- [ 2.26307235  7.2297766   7.49939493]]
-[17.22169087  0.0637712   1.6038642 ]
+
    0.07950356694388497
+4.124228866018077
+0.3908470004999506
+1.2475908482537097 11.548432150659993 27.870704644539707
+3.6568686180915178 4.6156409232714415 13.395980256477532
+[[ 1.24759085  3.65686862  4.61564092]
+ [ 3.65686862 11.54843215 13.39598026]
+ [ 4.61564092 13.39598026 27.87070464]]
+[36.35957915  0.07241855  4.23472994]
 
    
 
    
 
    
@@ -1606,7 +1611,7 @@ assumption for approximating \(\sigma
 
    
 
    
 
    
-
    0.023295599127611474 0.9904314810719695
+
    -0.05577845931438246 1.0303586629618948
 
    
 
    
 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html
index 4dd04be53..a1a490953 100644
--- a/doc/LectureNotes/_build/html/teachers.html
+++ b/doc/LectureNotes/_build/html/teachers.html
@@ -291,6 +291,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html
index 75521d447..b16d91619 100644
--- a/doc/LectureNotes/_build/html/textbooks.html
+++ b/doc/LectureNotes/_build/html/textbooks.html
@@ -291,6 +291,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index aa6d3b36c..43011e804 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -1668,8 +1673,8 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 
    
 
    
 
    
-
    [ 2.93289669  0.72236654  0.6671767   1.31936237 -0.39865452 -0.86247117
-  0.64411666 -1.56072658  1.17367225  0.96391888]
+
    [-1.24454968  1.12382443  0.67088558  1.12498355  1.0342028  -0.44563226
+  0.14599499  0.07378525  0.03479236 -1.2976637 ]
 
    
 
    
 
    
@@ -1894,26 +1899,26 @@ lowercase letters for vectors and uppercase letters for matrices)
    
 
    
 
    
 
    
-
    [[0.84159992 0.17563274 0.40632663 0.78278086 0.22919456 0.7510197
-  0.57584612 0.46342934 0.4745474  0.65543115]
- [0.59730078 0.77482712 0.70301068 0.29223521 0.15202654 0.74883972
-  0.48827791 0.09346106 0.58890784 0.7766443 ]
- [0.59184268 0.65554958 0.91546928 0.87340054 0.48200792 0.14824254
-  0.36185989 0.98654811 0.0473916  0.24032019]
- [0.87788573 0.61774362 0.82914126 0.23139242 0.32651488 0.61621902
-  0.59908884 0.49381549 0.97716508 0.21531156]
- [0.01654574 0.32393078 0.91854134 0.93909866 0.75300068 0.53942728
-  0.66063786 0.48867802 0.53149078 0.6831505 ]
- [0.57847325 0.42774546 0.24433117 0.07531349 0.98190064 0.68879472
-  0.18485685 0.85422602 0.58493681 0.00348246]
- [0.8517571  0.29620357 0.3096154  0.18409254 0.54880148 0.29881308
-  0.7509571  0.46891823 0.42124182 0.38203725]
- [0.59963873 0.51154388 0.28399125 0.60026673 0.49074536 0.32906581
-  0.4069157  0.89724282 0.48326853 0.43373107]
- [0.28415767 0.95518112 0.68257097 0.59215613 0.64373221 0.81283649
-  0.04262217 0.80979265 0.73337355 0.2077068 ]
- [0.87696461 0.09529067 0.39540235 0.68352799 0.1598058  0.03648711
-  0.73018893 0.60921896 0.33220123 0.50887105]]
+
    [[0.50306335 0.66487438 0.75677341 0.01349244 0.2813829  0.80056922
+  0.07974741 0.63046054 0.05098714 0.26479234]
+ [0.17861414 0.3388112  0.30005983 0.35520169 0.5399913  0.51709529
+  0.36339156 0.71582784 0.34568909 0.23164401]
+ [0.11922031 0.771128   0.20051449 0.07890044 0.96866031 0.34346829
+  0.5116375  0.52732966 0.80637385 0.69435454]
+ [0.94291287 0.29238145 0.84711297 0.22849742 0.56967917 0.1636508
+  0.15833751 0.84254917 0.05068486 0.54057582]
+ [0.17856374 0.71524686 0.66498292 0.00256622 0.72427854 0.50667812
+  0.0894015  0.22688898 0.54873252 0.31727523]
+ [0.73578772 0.81479491 0.45620975 0.2662452  0.47757553 0.64322974
+  0.54921401 0.70630967 0.51136852 0.59683811]
+ [0.37837864 0.71860732 0.35320952 0.67495943 0.16188604 0.41925189
+  0.28956161 0.06685171 0.75654448 0.28923   ]
+ [0.91099021 0.8387133  0.18277213 0.40418675 0.7249499  0.46415522
+  0.35018806 0.87148597 0.94141801 0.50911289]
+ [0.86066395 0.12235593 0.82418352 0.57881465 0.82559478 0.96826039
+  0.291056   0.41675053 0.06430789 0.96432396]
+ [0.91270333 0.64362404 0.18816387 0.81318307 0.47989224 0.20375464
+  0.33145794 0.92192012 0.33596404 0.18085537]]
 
    
 
    
 
    
@@ -1968,13 +1973,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    -0.03681479262838276
-3.936877889972962
--0.43976975777097144
-[[ 1.18742521  3.6407249   4.14122256]
- [ 3.6407249  12.06804775 12.20775115]
- [ 4.14122256 12.20775115 20.86009093]]
-[30.46593404  0.0604888   3.58914105]
+
    0.1385510301651882
+4.344176915013831
+0.13134148011170077
+[[ 0.87981676  2.52698542  2.65591748]
+ [ 2.52698542  8.29861395  7.76790092]
+ [ 2.65591748  7.76790092 13.4831139 ]]
+[19.77845284  0.09028702  2.79280475]
 
    
 
    
 
    
@@ -2199,7 +2204,7 @@ Name: Aragorn, dtype: object
 
    
 
    ---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6690/1326197715.py in ?()
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58840/1326197715.py in ?()
 ----> 6 new_hobbit = {'First Name': ["Peregrin"],
       7               'Last Name': ["Took"],
       8               'Place of birth': ["Shire"],
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index 0c9d8440f..1c052ec01 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -1636,7 +1641,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.9958983289118531
+
    0.9969513794144311
 
    
 
    
 
    
@@ -1653,7 +1658,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.010348289064969316
+
    0.007658477904313023
 
    
 
    
 
    
@@ -1668,23 +1673,23 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    [0.01250964 0.00679013 0.06416459 0.02126205 0.01247909 0.00080497
- 0.07373479 0.00940611 0.04794409 0.02361665 0.03311432 0.00114401
- 0.06392846 0.0813156  0.03578526 0.00691012 0.05978512 0.02928194
- 0.00209403 0.02014577 0.13600596 0.0178462  0.02016185 0.00574735
- 0.029003   0.04877931 0.03796359 0.02374496 0.06622369 0.02965159
- 0.01655384 0.00290613 0.00560098 0.00641423 0.05549529 0.03916813
- 0.01288787 0.02472936 0.02564828 0.03365012 0.03642173 0.02102403
- 0.00633289 0.02631942 0.02164667 0.04899367 0.00593362 0.03664879
- 0.00250787 0.00791263 0.01480256 0.01329403 0.02151521 0.00133638
- 0.01717549 0.01681612 0.03358833 0.01003519 0.00659604 0.02026756
- 0.01234261 0.058967   0.0126711  0.0039259  0.00254973 0.04315855
- 0.02909574 0.02386932 0.02404706 0.02769274 0.05804539 0.00732665
- 0.03868372 0.00513051 0.00172865 0.00353938 0.03076019 0.01643795
- 0.00458631 0.01817048 0.08667591 0.01706166 0.00637188 0.0930438
- 0.01506354 0.02787184 0.0002926  0.09398153 0.00570321 0.03511387
- 0.01075018 0.00170809 0.00943959 0.02654585 0.00399972 0.03057995
- 0.00211418 0.00685287 0.02330799 0.04191323]
+
    [0.00053122 0.03115122 0.00789262 0.02218076 0.00573727 0.00557893
+ 0.00099778 0.01234    0.03960002 0.03596557 0.0134113  0.00556946
+ 0.00136125 0.10719554 0.02754248 0.01409478 0.01760144 0.01032533
+ 0.01241284 0.01039879 0.00100913 0.02944152 0.00512599 0.00747773
+ 0.06260611 0.0231353  0.01624447 0.02923006 0.0046544  0.07332248
+ 0.02338085 0.02920675 0.02286267 0.04353549 0.00569512 0.02664408
+ 0.01098247 0.02156565 0.03529801 0.00507531 0.00554202 0.05141614
+ 0.02031987 0.01244297 0.01551724 0.00174738 0.01044475 0.01161645
+ 0.02622039 0.03285784 0.00522055 0.00687309 0.0195302  0.04101344
+ 0.00816675 0.0206033  0.04046513 0.02189863 0.06777772 0.04832356
+ 0.00114855 0.08660891 0.00586355 0.00625051 0.00939407 0.00108471
+ 0.03948301 0.02527621 0.03205795 0.11042239 0.02594314 0.05176711
+ 0.03396658 0.00889475 0.02632742 0.02502325 0.01266999 0.00455966
+ 0.03853313 0.01543076 0.00617221 0.00552462 0.01573062 0.01035006
+ 0.00162921 0.00974758 0.00812487 0.01881237 0.06690071 0.01499192
+ 0.04652794 0.04061345 0.04495752 0.00566707 0.01006984 0.00519717
+ 0.00151416 0.03214829 0.00891702 0.01844822]
 
    
 
    
 
    
@@ -1753,15 +1758,15 @@ but now splitting the data into a training set and a test set.
    
 
    
 
    
 
    
-
    [ 2.04641529 -0.77354301  7.7511273  -3.70241548  1.69515531]
+
    [ 1.97243946  0.15593478  4.54011398  0.56342158 -0.19299283]
 Training R2
-0.9958589197366403
+0.9948998579029953
 Training MSE
-0.008560581831215528
+0.009396472959497925
 Test R2
-0.997252717901263
+0.9951059931014423
 Test MSE
-0.00683875021199284
+0.010612904886352397
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index 32c45e395..966f1a6e5 100644
--- a/doc/LectureNotes/_build/html/week36.html
+++ b/doc/LectureNotes/_build/html/week36.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index f98298114..878ccd50e 100644
--- a/doc/LectureNotes/_build/html/week37.html
+++ b/doc/LectureNotes/_build/html/week37.html
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   
  
+ 
  • 
+  
+   Week 38: Logistic Regression and Optimization
+  
+ 
    • 
 
 
    
  
@@ -1624,7 +1629,7 @@ theorem.
    
 
    
 
    Bootstrap Statistics :
 original           bias      std. error
- 100.213    14.98        100.211        0.149466
+ 99.8244  15.0449        99.8227        0.150527
 
    
 
    
 
    
@@ -1844,9 +1849,7 @@ Error: 0.08426840630693411
 Bias^2: 0.0796891867672603
 Var: 0.004579219539673834
 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413
-
    
-
    
-
    Polynomial degree: 2
+Polynomial degree: 2
 Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
@@ -1873,9 +1876,7 @@ Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
 0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
-
    
-
    
-
    Polynomial degree: 7
+Polynomial degree: 7
 Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
@@ -1890,16 +1891,17 @@ Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
 0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
-
    
-
    
-
    Polynomial degree: 10
+Polynomial degree: 10
 Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
 0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
 
    
 
    
-
    Polynomial degree: 11
+
    Polynomial degree:
+
    
+
    
+
     11
 Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
@@ -1916,7 +1918,7 @@ Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
 
    
 
    
-_images/week37_139_6.png
+_images/week37_139_4.png
 
    
 
    
 
    
@@ -2267,31 +2269,29 @@ Mean squared error on test data: 10.50427787
 Degree of polynomial:   7
 Mean squared error on training data: 0.47313680
 Mean squared error on test data: 1.53738247
-
    
-
    
-
    Degree of polynomial:   8
+Degree of polynomial:   8
 Mean squared error on training data: 0.04926746
 Mean squared error on test data: 0.14629156
-Degree of polynomial:   9
+
    
+
    
+
    Degree of polynomial:   9
 Mean squared error on training data: 0.02546675
 Mean squared error on test data: 0.11202337
 Degree of polynomial:  10
 Mean squared error on training data: 0.02424794
 Mean squared error on test data: 0.22467274
-
    
-
    
-
    Degree of polynomial:  11
+Degree of polynomial:  11
 Mean squared error on training data: 0.01594452
 Mean squared error on test data: 1.07641937
 Degree of polynomial:  12
 Mean squared error on training data: 0.00805074
 Mean squared error on test data: 0.04295757
-Degree of polynomial:  13
-Mean squared error on training data: 0.00781918
-Mean squared error on test data: 0.56965674
 
    
 
    
-
    Degree of polynomial:  14
+
    Degree of polynomial:  13
+Mean squared error on training data: 0.00781918
+Mean squared error on test data: 0.56965674
+Degree of polynomial:  14
 Mean squared error on training data: 0.00465099
 Mean squared error on test data: 0.28443039
 Degree of polynomial:  15
@@ -2311,31 +2311,29 @@ Mean squared error on test data: 429.25695398
 Degree of polynomial:  19
 Mean squared error on training data: 0.00154853
 Mean squared error on test data: 239.97065359
-
    
-
    
-
    Degree of polynomial:  20
+Degree of polynomial:  20
 Mean squared error on training data: 0.00140846
 Mean squared error on test data: 1350.24493666
-Degree of polynomial:  21
+
    
+
    
+
    Degree of polynomial:  21
 Mean squared error on training data: 0.00119688
 Mean squared error on test data: 1840.50530832
 Degree of polynomial:  22
 Mean squared error on training data: 0.00092898
 Mean squared error on test data: 1184.60929685
-
    
-
    
-
    Degree of polynomial:  23
+Degree of polynomial:  23
 Mean squared error on training data: 0.00089193
 Mean squared error on test data: 3892.17483760
 Degree of polynomial:  24
 Mean squared error on training data: 0.00083355
 Mean squared error on test data: 1332.46736215
-Degree of polynomial:  25
-Mean squared error on training data: 0.00079904
-Mean squared error on test data: 7577.76690383
 
    
 
    
-
    Degree of polynomial:  26
+
    Degree of polynomial:  25
+Mean squared error on training data: 0.00079904
+Mean squared error on test data: 7577.76690383
+Degree of polynomial:  26
 Mean squared error on training data: 0.00075590
 Mean squared error on test data: 1079.36895644
 Degree of polynomial:  27
@@ -2351,13 +2349,13 @@ Mean squared error on training data: 0.00063866
 Mean squared error on test data: 3099.60342978
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
-_images/week37_148_11.png
+_images/week37_148_9.png
 
    
 
    
 
    Note that we kept the intercept column in the fitting here. This means that we need to set the intercept in the call to the Scikit-Learn function as False. Alternatively, we could have set up the design matrix \(X\) without the first column of ones.
    
@@ -2438,7 +2436,7 @@ Mean squared error on test data: 3099.60342978
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html
index c3639e751..1d3a79c7b 100644
--- a/doc/LectureNotes/_build/html/week38.html
+++ b/doc/LectureNotes/_build/html/week38.html
@@ -1099,6 +1099,7 @@ doconce format html week38.do.txt --no_mako -->
 
  • Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method
    • 
 
  • Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at https://youtu.be/omLmp_kkie0
    • 
 
  • Work on project 1, in particular resampling methods like cross-validation and bootstrap.
    • 
+
  • Video on cross-validation from exercise session
    • 
 
 
    
 
    
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index 965c07e22..1494506d6 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -515,7 +515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -583,18 +583,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.99194201]\n",
+      " [1.95647867]\n",
       "Coefficient beta : \n",
-      " [[4.85108001]]\n",
-      "Mean squared error: 0.28\n",
-      "Variance score: 0.87\n",
+      " [[5.05401912]]\n",
+      "Mean squared error: 0.25\n",
+      "Variance score: 0.90\n",
       "Mean squared log error: 0.01\n",
       "Mean absolute error: 0.43\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -822,7 +822,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -838,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.005\n"
+      "0.004999999999999991\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 0a4e596ce..5cc60fd17 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -869,13 +869,7 @@
       " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n",
       " 9.84443254e-01 3.11507992e-04]\n",
       "probabilities sum up to: 1.0\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "predictions = (n_inputs) = (1437,)\n",
       "prediction for image 0: 8\n",
       "correct label for image 0: 6\n"
@@ -1083,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1661,7 +1655,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1679,7 +1673,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1697,7 +1691,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1715,7 +1709,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1733,7 +1727,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1751,7 +1745,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1769,7 +1763,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1787,11 +1781,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1809,11 +1803,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1831,11 +1825,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1853,11 +1847,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1875,11 +1869,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1897,7 +1891,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1915,11 +1909,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1937,11 +1931,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1959,11 +1953,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1981,11 +1975,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2003,25 +1997,88 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m      9\u001b[0m     dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m     10\u001b[0m                         n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m     15\u001b[0m     test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
-      "Cell \u001b[0;32mIn[6], line 99\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeed_forward()\n\u001b[0;32m---> 99\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackpropagation\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[6], line 64\u001b[0m, in \u001b[0;36mNeuralNetwork.backpropagation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h\u001b[38;5;241m.\u001b[39mT, error_output)\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(error_output, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_weights_gradient \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_hidden\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(error_hidden, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlmbd \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
      ]
     }
    ],
@@ -2066,7 +2123,52 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_1.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_2.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visual representation of grid search\n",
     "# uses seaborn heatmap, you can also do this with matplotlib imshow\n",
@@ -2133,7 +2235,584 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.18333333333333332\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.18611111111111112\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.13055555555555556\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.24444444444444444\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.23333333333333334\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.12777777777777777\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.1527777777777778\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9111111111111111\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8305555555555556\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.8944444444444445\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.975\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9777777777777777\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9777777777777777\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9444444444444444\n",
+      "\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9527777777777777\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9027777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8583333333333333\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9055555555555556\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.8666666666666667\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.08611111111111111\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.17777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.08333333333333333\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09444444444444444\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.17222222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.11666666666666667\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.1388888888888889\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09444444444444444\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "# store models for later use\n",
@@ -2171,7 +2850,36 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_0.png"
+      }
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+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_1.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# optional\n",
     "# visual representation of grid search\n",
@@ -2261,7 +2969,16 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2259440937.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    conda create -n tf tensorflow\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "conda create -n tf tensorflow\n",
     "conda activate tf"
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10_59_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter10_59_1.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
index 5f7060cc9..50d4c9e83 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
@@ -2989,25 +2989,14 @@
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:81\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 81\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m     52\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;124;03m    (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m     62\u001b[0m     jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m     52\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;124;03m    (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m     62\u001b[0m     jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[9], line 61\u001b[0m, in \u001b[0;36mg_trial\u001b[0;34m(point, P)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mg_trial\u001b[39m(point,P):\n\u001b[1;32m     60\u001b[0m     x,t \u001b[38;5;241m=\u001b[39m point\n\u001b[0;32m---> 61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39mt)\u001b[38;5;241m*\u001b[39mu(x) \u001b[38;5;241m+\u001b[39m x\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39mx)\u001b[38;5;241m*\u001b[39mt\u001b[38;5;241m*\u001b[39m\u001b[43mdeep_neural_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[9], line 37\u001b[0m, in \u001b[0;36mdeep_neural_network\u001b[0;34m(deep_params, x)\u001b[0m\n\u001b[1;32m     34\u001b[0m x_prev \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate((np\u001b[38;5;241m.\u001b[39mones((\u001b[38;5;241m1\u001b[39m,num_points)), x_prev ), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     36\u001b[0m z_hidden \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(w_hidden, x_prev)\n\u001b[0;32m---> 37\u001b[0m x_hidden \u001b[38;5;241m=\u001b[39m \u001b[43msigmoid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz_hidden\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Update x_prev such that next layer can use the output from this layer\u001b[39;00m\n\u001b[1;32m     40\u001b[0m x_prev \u001b[38;5;241m=\u001b[39m x_hidden\n",
-      "Cell \u001b[0;32mIn[9], line 11\u001b[0m, in \u001b[0;36msigmoid\u001b[0;34m(z)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msigmoid\u001b[39m(z):\n\u001b[0;32m---> 11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:39\u001b[0m, in \u001b[0;36mArrayBox.__rtruediv__\u001b[0;34m(self, other)\u001b[0m\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__rtruediv__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrue_divide\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m     argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum    \u001b[38;5;28;01mfor\u001b[39;00m argnum, _   \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m     44\u001b[0m     ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m     node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m     33\u001b[0m     fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     35\u001b[0m                               \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[0;32m---> 66\u001b[0m     vjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (vjp(g),)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:53\u001b[0m, in \u001b[0;36m\u001b[0;34m(ans, x, y)\u001b[0m\n\u001b[1;32m     48\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlogaddexp,   \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mexp(x\u001b[38;5;241m-\u001b[39mans)),\n\u001b[1;32m     49\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mexp(y\u001b[38;5;241m-\u001b[39mans)))\n\u001b[1;32m     50\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlogaddexp2,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(x\u001b[38;5;241m-\u001b[39mans)),\n\u001b[1;32m     51\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(y\u001b[38;5;241m-\u001b[39mans)))\n\u001b[1;32m     52\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mtrue_divide, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m y),\n\u001b[0;32m---> 53\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : \u001b[43munbroadcast_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     54\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmod,         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m     55\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mfloor(x\u001b[38;5;241m/\u001b[39my)))\n\u001b[1;32m     56\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mremainder,   \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m     57\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mfloor(x\u001b[38;5;241m/\u001b[39my)))\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:658\u001b[0m, in \u001b[0;36munbroadcast_f\u001b[0;34m(target, f)\u001b[0m\n\u001b[1;32m    655\u001b[0m         x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mreal(x)\n\u001b[1;32m    656\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m    659\u001b[0m     target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[1;32m    660\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: unbroadcast(f(g), target_meta)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:64\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m     63\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m     20\u001b[0m     outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m     23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m     66\u001b[0m     vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m     69\u001b[0m     argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660\u001b[0m, in \u001b[0;36munbroadcast_f..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m    659\u001b[0m     target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[0;32m--> 660\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43munbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_meta\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:651\u001b[0m, in \u001b[0;36munbroadcast\u001b[0;34m(x, target_meta, broadcast_idx)\u001b[0m\n\u001b[1;32m    649\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m anp\u001b[38;5;241m.\u001b[39mndim(x) \u001b[38;5;241m>\u001b[39m target_ndim:\n\u001b[1;32m    650\u001b[0m     x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msum(x, axis\u001b[38;5;241m=\u001b[39mbroadcast_idx)\n\u001b[0;32m--> 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, size \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtarget_shape\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    652\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    653\u001b[0m         x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msum(x, axis\u001b[38;5;241m=\u001b[39maxis, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
index 3144e9d56..903a3d745 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
@@ -1798,10 +1798,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.0369544130635358\n",
-      "3.662836197064178\n",
-      "[[1.058997   3.11439407]\n",
-      " [3.11439407 9.99498272]]\n"
+      "-0.07978850553011713\n",
+      "3.8232102961414203\n",
+      "[[ 1.25705685  3.70704566]\n",
+      " [ 3.70704566 12.1664372 ]]\n"
      ]
     }
    ],
@@ -1845,10 +1845,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.08826182458028335\n",
-      "1.7026722043092946\n",
-      "[[1.         0.61113781]\n",
-      " [0.61113781 1.        ]]\n"
+      "0.08931169286872433\n",
+      "2.047447724189861\n",
+      "[[1.         0.66729685]\n",
+      " [0.66729685 1.        ]]\n"
      ]
     }
    ],
@@ -1905,30 +1905,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.7252563  -2.26264849]\n",
-      " [ 1.19052935  3.11261935]\n",
-      " [-0.62158409 -2.99662602]\n",
-      " [-0.06216141 -0.18120973]\n",
-      " [ 1.32065614  3.50269821]\n",
-      " [ 0.83995705  2.80855691]\n",
-      " [ 0.1571284   0.96919021]\n",
-      " [-0.03404758  1.01551815]\n",
-      " [-0.23596934  0.77449804]\n",
-      " [-1.82925222 -6.74259663]]\n",
+      "[[ 2.37582891  5.4242743 ]\n",
+      " [-0.81925302 -3.60052901]\n",
+      " [ 0.37467515  3.16158074]\n",
+      " [-0.40478305 -2.22601408]\n",
+      " [-0.0776532  -1.70594608]\n",
+      " [-0.99434472 -2.2026985 ]\n",
+      " [ 0.71376303  2.77725472]\n",
+      " [ 0.94637206  2.69303453]\n",
+      " [-0.19791352  0.93366198]\n",
+      " [-1.91669165 -5.2546186 ]]\n",
       "          0         1\n",
-      "0 -0.725256 -2.262648\n",
-      "1  1.190529  3.112619\n",
-      "2 -0.621584 -2.996626\n",
-      "3 -0.062161 -0.181210\n",
-      "4  1.320656  3.502698\n",
-      "5  0.839957  2.808557\n",
-      "6  0.157128  0.969190\n",
-      "7 -0.034048  1.015518\n",
-      "8 -0.235969  0.774498\n",
-      "9 -1.829252 -6.742597\n",
+      "0  2.375829  5.424274\n",
+      "1 -0.819253 -3.600529\n",
+      "2  0.374675  3.161581\n",
+      "3 -0.404783 -2.226014\n",
+      "4 -0.077653 -1.705946\n",
+      "5 -0.994345 -2.202698\n",
+      "6  0.713763  2.777255\n",
+      "7  0.946372  2.693035\n",
+      "8 -0.197914  0.933662\n",
+      "9 -1.916692 -5.254619\n",
       "          0         1\n",
-      "0  1.000000  0.963187\n",
-      "1  0.963187  1.000000\n"
+      "0  1.000000  0.932382\n",
+      "1  0.932382  1.000000\n"
      ]
     }
    ],
@@ -1974,37 +1974,37 @@
      "text": [
       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.077382  0.076459  0.074114  0.076542  0.079074  0.064978  0.067074   \n",
-      "2   0.0  0.076459  0.078118  0.070150  0.073866  0.078123  0.059624  0.062356   \n",
-      "3   0.0  0.074114  0.070150  0.076618  0.077248  0.077529  0.070432  0.071572   \n",
-      "4   0.0  0.076542  0.073866  0.077248  0.078731  0.080082  0.069802  0.071449   \n",
-      "5   0.0  0.079074  0.078123  0.077529  0.080082  0.082803  0.068607  0.070859   \n",
-      "6   0.0  0.064978  0.059624  0.070432  0.069802  0.068607  0.066865  0.067168   \n",
-      "7   0.0  0.067074  0.062356  0.071572  0.071449  0.070859  0.067168  0.067809   \n",
-      "8   0.0  0.069439  0.065556  0.072752  0.073253  0.073422  0.067367  0.068408   \n",
-      "9   0.0  0.072092  0.069317  0.073923  0.075198  0.076333  0.067380  0.068897   \n",
-      "10  0.0  0.056856  0.051021  0.063637  0.062282  0.060288  0.061848  0.061588   \n",
-      "11  0.0  0.058361  0.052856  0.064604  0.063560  0.061921  0.062263  0.062231   \n",
-      "12  0.0  0.060085  0.055000  0.065679  0.065007  0.063802  0.062700  0.062932   \n",
-      "13  0.0  0.062053  0.057514  0.066855  0.066633  0.065963  0.063133  0.063673   \n",
-      "14  0.0  0.064294  0.060470  0.068116  0.068446  0.068446  0.063525  0.064427   \n",
+      "1   0.0  0.080947  0.086913  0.082764  0.085436  0.088029  0.075956  0.077683   \n",
+      "2   0.0  0.086913  0.094044  0.088199  0.091365  0.094439  0.080171  0.082174   \n",
+      "3   0.0  0.082764  0.088199  0.090624  0.093094  0.095465  0.086730  0.088375   \n",
+      "4   0.0  0.085436  0.091365  0.093094  0.095805  0.098414  0.088651  0.090440   \n",
+      "5   0.0  0.088029  0.094439  0.095465  0.098414  0.101263  0.090468  0.092401   \n",
+      "6   0.0  0.075956  0.080171  0.086730  0.088651  0.090468  0.085390  0.086712   \n",
+      "7   0.0  0.077683  0.082174  0.088375  0.090440  0.092401  0.086712  0.088127   \n",
+      "8   0.0  0.079444  0.084218  0.090038  0.092252  0.094362  0.088038  0.089548   \n",
+      "9   0.0  0.081252  0.086318  0.091731  0.094099  0.096365  0.089376  0.090985   \n",
+      "10  0.0  0.068684  0.071868  0.080702  0.082126  0.083446  0.081103  0.082115   \n",
+      "11  0.0  0.069978  0.073339  0.081978  0.083499  0.084915  0.082170  0.083248   \n",
+      "12  0.0  0.071314  0.074859  0.083288  0.084910  0.086428  0.083260  0.084406   \n",
+      "13  0.0  0.072697  0.076435  0.084637  0.086364  0.087988  0.084376  0.085592   \n",
+      "14  0.0  0.074130  0.078071  0.086026  0.087864  0.089600  0.085518  0.086809   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.069439  0.072092  0.056856  0.058361  0.060085  0.062053  0.064294  \n",
-      "2   0.065556  0.069317  0.051021  0.052856  0.055000  0.057514  0.060470  \n",
-      "3   0.072752  0.073923  0.063637  0.064604  0.065679  0.066855  0.068116  \n",
-      "4   0.073253  0.075198  0.062282  0.063560  0.065007  0.066633  0.068446  \n",
-      "5   0.073422  0.076333  0.060288  0.061921  0.063802  0.065963  0.068446  \n",
-      "6   0.067367  0.067380  0.061848  0.062263  0.062700  0.063133  0.063525  \n",
-      "7   0.068408  0.068897  0.061588  0.062231  0.062932  0.063673  0.064427  \n",
-      "8   0.069486  0.070556  0.061154  0.062058  0.063064  0.064166  0.065352  \n",
-      "9   0.070556  0.072344  0.060453  0.061656  0.063017  0.064547  0.066256  \n",
-      "10  0.061154  0.060453  0.058245  0.058254  0.058234  0.058152  0.057961  \n",
-      "11  0.062058  0.061656  0.058254  0.058427  0.058593  0.058721  0.058770  \n",
-      "12  0.063064  0.063017  0.058234  0.058593  0.058970  0.059340  0.059669  \n",
-      "13  0.064166  0.064547  0.058152  0.058721  0.059340  0.059992  0.060652  \n",
-      "14  0.065352  0.066256  0.057961  0.058770  0.059669  0.060652  0.061706  \n"
+      "1   0.079444  0.081252  0.068684  0.069978  0.071314  0.072697  0.074130  \n",
+      "2   0.084218  0.086318  0.071868  0.073339  0.074859  0.076435  0.078071  \n",
+      "3   0.090038  0.091731  0.080702  0.081978  0.083288  0.084637  0.086026  \n",
+      "4   0.092252  0.094099  0.082126  0.083499  0.084910  0.086364  0.087864  \n",
+      "5   0.094362  0.096365  0.083446  0.084915  0.086428  0.087988  0.089600  \n",
+      "6   0.088038  0.089376  0.081103  0.082170  0.083260  0.084376  0.085518  \n",
+      "7   0.089548  0.090985  0.082115  0.083248  0.084406  0.085592  0.086809  \n",
+      "8   0.091068  0.092607  0.083119  0.084319  0.085548  0.086808  0.088102  \n",
+      "9   0.092607  0.094254  0.084122  0.085392  0.086694  0.088030  0.089404  \n",
+      "10  0.083119  0.084122  0.078238  0.079089  0.079953  0.080832  0.081728  \n",
+      "11  0.084319  0.085392  0.079089  0.079989  0.080903  0.081835  0.082785  \n",
+      "12  0.085548  0.086694  0.079953  0.080903  0.081871  0.082857  0.083864  \n",
+      "13  0.086808  0.088030  0.080832  0.081835  0.082857  0.083900  0.084967  \n",
+      "14  0.088102  0.089404  0.081728  0.082785  0.083864  0.084967  0.086096  \n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index d40c38662..d5b6a3ec8 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
@@ -489,10 +489,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 0.165272 sec\n",
+      "Runtime: 0.139224 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      " 99.6801        99.6702        0.149483\n"
+      " 99.9792        99.9692        0.149921\n"
      ]
     }
    ],
@@ -917,7 +917,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.121   15.022        100.121        0.149904\n"
+      " 99.8978  15.0232        99.8962        0.149063\n"
      ]
     }
    ],
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -1282,18 +1282,18 @@
       "Error: 0.10398646080125035\n",
       "Bias^2: 0.1007711427354898\n",
       "Var: 0.0032153180657605116\n",
-      "0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032\n",
-      "Polynomial degree: 3\n",
-      "Error: 0.06547790180152355\n",
-      "Bias^2: 0.06208238634231949\n",
-      "Var: 0.0033955154592040936\n",
-      "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n"
+      "0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Polynomial degree: 3\n",
+      "Error: 0.06547790180152355\n",
+      "Bias^2: 0.06208238634231949\n",
+      "Var: 0.0033955154592040936\n",
+      "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n",
       "Polynomial degree: 4\n",
       "Error: 0.06844519414009445\n",
       "Bias^2: 0.06453579006728324\n",
@@ -1303,18 +1303,18 @@
       "Error: 0.05227921801205686\n",
       "Bias^2: 0.0481872773043029\n",
       "Var: 0.004091940707753939\n",
-      "0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n",
-      "Polynomial degree: 6\n",
-      "Error: 0.037813671417389005\n",
-      "Bias^2: 0.033657685071527665\n",
-      "Var: 0.00415598634586135\n",
-      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n"
+      "0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Polynomial degree: 6\n",
+      "Error: 0.037813671417389005\n",
+      "Bias^2: 0.033657685071527665\n",
+      "Var: 0.00415598634586135\n",
+      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n",
       "Polynomial degree: 7\n",
       "Error: 0.02760977349102253\n",
       "Bias^2: 0.022999498260366312\n",
@@ -1329,7 +1329,13 @@
       "Error: 0.02660572763718093\n",
       "Bias^2: 0.010018312644137363\n",
       "Var: 0.016587414993043573\n",
-      "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n",
+      "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 10\n",
       "Error: 0.021592704588025025\n",
       "Bias^2: 0.010516485576645508\n",
@@ -1339,13 +1345,7 @@
       "Error: 0.07160048164233104\n",
       "Bias^2: 0.014436800088904942\n",
       "Var: 0.05716368155342608\n",
-      "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n",
       "Polynomial degree: 12\n",
       "Error: 0.11547777218872497\n",
       "Bias^2: 0.01628578269596628\n",
@@ -1731,9 +1731,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2075,7 +2075,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
@@ -3749,7 +3749,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4059,7 +4059,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4136,7 +4136,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4221,7 +4221,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4282,7 +4282,7 @@
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       ]
@@ -107,7 +107,7 @@
     },
     {
      "data": {
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",
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       "text/plain": [
        "
    "
       ]
@@ -1338,7 +1338,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test set accuracy with SVM: 0.63\n",
+      "Test set accuracy with SVM: 0.63\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Test set accuracy with Decision Trees: 0.90\n",
       "Test set accuracy Logistic Regression with scaled data: 0.96\n",
       "Test set accuracy SVM with scaled data: 0.96\n",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
index 2674163de..f5d9739a4 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
@@ -295,10 +295,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.10541723644166373\n",
-      "4.575870409023631\n",
-      "[[0.84972787 2.5321613 ]\n",
-      " [2.5321613  8.59875207]]\n"
+      "0.14934258650797513\n",
+      "4.548263635652985\n",
+      "[[ 1.0875061   3.3260513 ]\n",
+      " [ 3.3260513  11.10994958]]\n"
      ]
     }
    ],
@@ -340,10 +340,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0768805855280187\n",
-      "1.6568154596723088\n",
-      "[[1.         0.69438869]\n",
-      " [0.69438869 1.        ]]\n"
+      "0.09291556244521161\n",
+      "2.096511363983559\n",
+      "[[1.        0.7198234]\n",
+      " [0.7198234 1.       ]]\n"
      ]
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    ],
@@ -397,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-1.6629598  -6.60625144]\n",
-      " [-1.59424119 -4.17676247]\n",
-      " [ 0.13699574 -1.26680052]\n",
-      " [ 1.67275915  7.04206048]\n",
-      " [ 1.48931464  4.73718419]\n",
-      " [ 0.82341746  3.16411163]\n",
-      " [ 0.56141009  1.13137881]\n",
-      " [ 0.38616125  0.98338288]\n",
-      " [-1.28000355 -3.69734609]\n",
-      " [-0.53285379 -1.31095745]]\n",
+      "[[ 0.20480187  0.26586817]\n",
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+      " [-1.59078383 -4.25673276]\n",
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+      " [-1.33062878 -3.73785069]\n",
+      " [ 2.22810365  9.25389111]\n",
+      " [-0.48697869 -1.04401421]]\n",
       "          0         1\n",
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-      "7  0.386161  0.983383\n",
-      "8 -1.280004 -3.697346\n",
-      "9 -0.532854 -1.310957\n",
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+      "7 -1.330629 -3.737851\n",
+      "8  2.228104  9.253891\n",
+      "9 -0.486979 -1.044014\n",
       "          0         1\n",
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-      "1  0.972149  1.000000\n"
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@@ -461,37 +461,37 @@
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       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.083793  0.077516  0.081955  0.073791  0.066763  0.071803  0.064695   \n",
-      "2   0.0  0.077516  0.074112  0.078363  0.072304  0.066838  0.070556  0.064873   \n",
-      "3   0.0  0.081955  0.078363  0.084619  0.077941  0.071937  0.076833  0.070462   \n",
-      "4   0.0  0.073791  0.072304  0.077941  0.073102  0.068533  0.072132  0.067141   \n",
-      "5   0.0  0.066763  0.066838  0.071937  0.068533  0.065108  0.067671  0.063791   \n",
-      "6   0.0  0.071803  0.070556  0.076833  0.072132  0.067671  0.071608  0.066653   \n",
-      "7   0.0  0.064695  0.064873  0.070462  0.067141  0.063791  0.066653  0.062797   \n",
-      "8   0.0  0.058641  0.059876  0.064878  0.062637  0.060171  0.062173  0.059201   \n",
-      "9   0.0  0.053462  0.055477  0.059977  0.058582  0.056826  0.058133  0.055876   \n",
-      "10  0.0  0.061862  0.062199  0.067948  0.064820  0.061637  0.064615  0.060898   \n",
-      "11  0.0  0.056026  0.057316  0.062429  0.060312  0.057964  0.060087  0.057216   \n",
-      "12  0.0  0.051042  0.053036  0.057609  0.056287  0.054609  0.056041  0.053854   \n",
-      "13  0.0  0.046764  0.049276  0.053387  0.052692  0.051554  0.052427  0.050796   \n",
-      "14  0.0  0.043072  0.045963  0.049677  0.049481  0.048781  0.049196  0.048020   \n",
+      "1   0.0  0.072147  0.072728  0.071758  0.072209  0.072843  0.064428  0.064668   \n",
+      "2   0.0  0.072728  0.075385  0.069979  0.071530  0.073408  0.061386  0.062260   \n",
+      "3   0.0  0.071758  0.069979  0.076968  0.076244  0.075522  0.072286  0.071935   \n",
+      "4   0.0  0.072209  0.071530  0.076244  0.076161  0.076150  0.070898  0.070950   \n",
+      "5   0.0  0.072843  0.073408  0.075522  0.076150  0.076934  0.069399  0.069885   \n",
+      "6   0.0  0.064428  0.061386  0.072286  0.070898  0.069399  0.069873  0.069179   \n",
+      "7   0.0  0.064668  0.062260  0.071935  0.070950  0.069885  0.069179  0.068758   \n",
+      "8   0.0  0.065062  0.063354  0.071655  0.071103  0.070514  0.068494  0.068360   \n",
+      "9   0.0  0.065616  0.064690  0.071433  0.071356  0.071291  0.067793  0.067967   \n",
+      "10  0.0  0.057287  0.053787  0.066153  0.064505  0.062691  0.065212  0.064382   \n",
+      "11  0.0  0.057387  0.054286  0.065949  0.064573  0.063048  0.064834  0.064202   \n",
+      "12  0.0  0.057607  0.054932  0.065830  0.064739  0.063518  0.064507  0.064077   \n",
+      "13  0.0  0.057951  0.055737  0.065788  0.065001  0.064107  0.064218  0.064000   \n",
+      "14  0.0  0.058422  0.056717  0.065818  0.065358  0.064820  0.063954  0.063959   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.058641  0.053462  0.061862  0.056026  0.051042  0.046764  0.043072  \n",
-      "2   0.059876  0.055477  0.062199  0.057316  0.053036  0.049276  0.045963  \n",
-      "3   0.064878  0.059977  0.067948  0.062429  0.057609  0.053387  0.049677  \n",
-      "4   0.062637  0.058582  0.064820  0.060312  0.056287  0.052692  0.049481  \n",
-      "5   0.060171  0.056826  0.061637  0.057964  0.054609  0.051554  0.048781  \n",
-      "6   0.062173  0.058133  0.064615  0.060087  0.056041  0.052427  0.049196  \n",
-      "7   0.059201  0.055876  0.060898  0.057216  0.053854  0.050796  0.048020  \n",
-      "8   0.056328  0.053599  0.057420  0.054434  0.051645  0.049060  0.046678  \n",
-      "9   0.053599  0.051368  0.054197  0.051787  0.049479  0.047299  0.045258  \n",
-      "10  0.057420  0.054197  0.059243  0.055653  0.052370  0.049380  0.046663  \n",
-      "11  0.054434  0.051787  0.055653  0.052738  0.050015  0.047489  0.045161  \n",
-      "12  0.051645  0.049479  0.052370  0.050015  0.047760  0.045630  0.043637  \n",
-      "13  0.049060  0.047299  0.049380  0.047489  0.045630  0.043839  0.042136  \n",
-      "14  0.046678  0.045258  0.046663  0.045161  0.043637  0.042136  0.040684  \n"
+      "1   0.065062  0.065616  0.057287  0.057387  0.057607  0.057951  0.058422  \n",
+      "2   0.063354  0.064690  0.053787  0.054286  0.054932  0.055737  0.056717  \n",
+      "3   0.071655  0.071433  0.066153  0.065949  0.065830  0.065788  0.065818  \n",
+      "4   0.071103  0.071356  0.064505  0.064573  0.064739  0.065001  0.065358  \n",
+      "5   0.070514  0.071291  0.062691  0.063048  0.063518  0.064107  0.064820  \n",
+      "6   0.068494  0.067793  0.065212  0.064834  0.064507  0.064218  0.063954  \n",
+      "7   0.068360  0.067967  0.064382  0.064202  0.064077  0.064000  0.063959  \n",
+      "8   0.068268  0.068206  0.063526  0.063549  0.063637  0.063781  0.063977  \n",
+      "9   0.068206  0.068504  0.062616  0.062853  0.063163  0.063545  0.063995  \n",
+      "10  0.063526  0.062616  0.061724  0.061284  0.060870  0.060471  0.060071  \n",
+      "11  0.063549  0.062853  0.061284  0.060994  0.060734  0.060490  0.060251  \n",
+      "12  0.063637  0.063163  0.060870  0.060734  0.060629  0.060547  0.060475  \n",
+      "13  0.063781  0.063545  0.060471  0.060490  0.060547  0.060631  0.060735  \n",
+      "14  0.063977  0.063995  0.060071  0.060251  0.060475  0.060735  0.061025  \n"
      ]
     }
    ],
@@ -916,10 +916,10 @@
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      "text": [
       "          0         1\n",
-      "0  3.986362  1.994474\n",
-      "1  1.994474  2.001468\n",
-      "[[3.98636199 1.99447418]\n",
-      " [1.99447418 2.00146807]]\n"
+      "0  3.935972  1.991047\n",
+      "1  1.991047  2.000783\n",
+      "[[3.93597168 1.99104747]\n",
+      " [1.99104747 2.00078324]]\n"
      ]
     }
    ],
@@ -949,13 +949,13 @@
      "output_type": "stream",
      "text": [
       "Centered covariance using own code\n",
-      "[[3.98636199 1.99447418]\n",
-      " [1.99447418 2.00146807]]\n"
+      "[[3.93597168 1.99104747]\n",
+      " [1.99104747 2.00078324]]\n"
      ]
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
    "
       ]
@@ -1044,12 +1044,12 @@
      "output_type": "stream",
      "text": [
       "Eigenvalues of Covariance matrix\n",
-      "5.221666864828611\n",
-      "0.766163196245293\n",
+      "5.182086698929565\n",
+      "0.7546682196464342\n",
       "First eigenvector\n",
-      "[0.85014487 0.52654886]\n",
+      "[0.84767088 0.53052247]\n",
       "Second eigenvector\n",
-      "[-0.52654886  0.85014487]\n"
+      "[-0.53052247  0.84767088]\n"
      ]
     },
     {
@@ -1057,7 +1057,7 @@
      "output_type": "stream",
      "text": [
       "Eigenvector of largest eigenvalue\n",
-      "[-0.85014487 -0.52654886]\n"
+      "[0.84767088 0.53052247]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
index b9083d394..b7c594b5f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
@@ -225,8 +225,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.93937564  0.92308867  0.34427423 -1.37685349  2.80413696  0.25555619\n",
-      "  1.121292   -0.42392359 -0.13913033 -0.7228852 ]\n"
+      "[-0.29015871  0.69417174 -1.07998756  0.34332677  0.19547923 -1.09755017\n",
+      "  0.86197958 -0.15546887  0.14369927  1.96251859]\n"
      ]
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@@ -662,36 +662,26 @@
      "name": "stdout",
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-      "  7.93891335e-01 6.25458506e-01]]\n"
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+      "  0.35098705 0.46544987 0.66456459 0.25512598]]\n"
      ]
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    ],
@@ -810,13 +800,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.2160073466353432\n",
-      "4.720889407643293\n",
-      "1.0362483569261072\n",
-      "[[ 1.17260271  3.60999067  4.15283463]\n",
-      " [ 3.60999067 12.23423987 13.18824338]\n",
-      " [ 4.15283463 13.18824338 21.31571995]]\n",
-      "[31.70587399  0.09156055  2.92512799]\n"
+      "-0.0018310257999764163\n",
+      "4.092608577084595\n",
+      "0.31555734196707536\n",
+      "[[ 0.78671736  2.38337498  2.04657426]\n",
+      " [ 2.38337498  8.33235647  6.05002471]\n",
+      " [ 2.04657426  6.05002471 10.13152828]]\n",
+      "[15.98413606  0.08227668  3.18418938]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index df7d9c752..7210ab545 100644
--- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
@@ -1344,27 +1344,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.076776262573027\n",
-      "[[ 5.24692014 11.86416941  1.87818331  3.59971727  6.93989887  8.39223923\n",
-      "   1.96991192  6.69391797  5.22667819  3.39929428]\n",
-      " [11.86416941 26.82688363  4.24688853  8.13956654 15.69227923 18.97626519\n",
-      "   4.45430236 15.13607502 11.81839897  7.68637642]\n",
-      " [ 1.87818331  4.24688853  0.67231298  1.2885519   2.48420061  3.0040792\n",
-      "   0.70514808  2.39614949  1.87093752  1.21680864]\n",
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-      "   1.3514835   4.59244881  3.58583003  2.33212971]\n",
-      " [ 6.93989887 15.69227923  2.48420061  4.76120718  9.17913653 11.1000911\n",
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-      "   3.15079545 10.70665448  8.35986305  5.43703545]\n",
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-      "   1.27623637  4.33675308  3.38618024  2.20228273]]\n"
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+      "   5.73063053  5.72992196 10.28316949  9.68857788]\n",
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+      "   6.35354073  6.35275513 11.40093324 10.74171049]\n",
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+      "   6.35275513  6.35196964 11.39952356 10.74038232]\n",
+      " [ 8.55196383  6.04963309 13.42945319  5.55996153 10.28316949  9.1793417\n",
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+      " [ 8.05747369  5.69983227 12.65293772  5.23847441  9.68857788  8.64857542\n",
+      "  10.74171049 10.74038232 19.2751616  18.16063661]]\n"
      ]
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    ],
@@ -2001,15 +2001,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      " [ 2.26307235  7.2297766   7.49939493]]\n",
-      "[17.22169087  0.0637712   1.6038642 ]\n"
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+      "[[ 1.24759085  3.65686862  4.61564092]\n",
+      " [ 3.65686862 11.54843215 13.39598026]\n",
+      " [ 4.61564092 13.39598026 27.87070464]]\n",
+      "[36.35957915  0.07241855  4.23472994]\n"
      ]
     }
    ],
@@ -2638,7 +2638,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "
    "
       ]
@@ -2764,12 +2764,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.023295599127611474 0.9904314810719695\n"
+      "-0.05577845931438246 1.0303586629618948\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
index 76f39b5e6..9aae2864a 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -874,8 +874,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.93289669  0.72236654  0.6671767   1.31936237 -0.39865452 -0.86247117\n",
-      "  0.64411666 -1.56072658  1.17367225  0.96391888]\n"
+      "[-1.24454968  1.12382443  0.67088558  1.12498355  1.0342028  -0.44563226\n",
+      "  0.14599499  0.07378525  0.03479236 -1.2976637 ]\n"
      ]
     }
    ],
@@ -1313,26 +1313,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.84159992 0.17563274 0.40632663 0.78278086 0.22919456 0.7510197\n",
-      "  0.57584612 0.46342934 0.4745474  0.65543115]\n",
-      " [0.59730078 0.77482712 0.70301068 0.29223521 0.15202654 0.74883972\n",
-      "  0.48827791 0.09346106 0.58890784 0.7766443 ]\n",
-      " [0.59184268 0.65554958 0.91546928 0.87340054 0.48200792 0.14824254\n",
-      "  0.36185989 0.98654811 0.0473916  0.24032019]\n",
-      " [0.87788573 0.61774362 0.82914126 0.23139242 0.32651488 0.61621902\n",
-      "  0.59908884 0.49381549 0.97716508 0.21531156]\n",
-      " [0.01654574 0.32393078 0.91854134 0.93909866 0.75300068 0.53942728\n",
-      "  0.66063786 0.48867802 0.53149078 0.6831505 ]\n",
-      " [0.57847325 0.42774546 0.24433117 0.07531349 0.98190064 0.68879472\n",
-      "  0.18485685 0.85422602 0.58493681 0.00348246]\n",
-      " [0.8517571  0.29620357 0.3096154  0.18409254 0.54880148 0.29881308\n",
-      "  0.7509571  0.46891823 0.42124182 0.38203725]\n",
-      " [0.59963873 0.51154388 0.28399125 0.60026673 0.49074536 0.32906581\n",
-      "  0.4069157  0.89724282 0.48326853 0.43373107]\n",
-      " [0.28415767 0.95518112 0.68257097 0.59215613 0.64373221 0.81283649\n",
-      "  0.04262217 0.80979265 0.73337355 0.2077068 ]\n",
-      " [0.87696461 0.09529067 0.39540235 0.68352799 0.1598058  0.03648711\n",
-      "  0.73018893 0.60921896 0.33220123 0.50887105]]\n"
+      "[[0.50306335 0.66487438 0.75677341 0.01349244 0.2813829  0.80056922\n",
+      "  0.07974741 0.63046054 0.05098714 0.26479234]\n",
+      " [0.17861414 0.3388112  0.30005983 0.35520169 0.5399913  0.51709529\n",
+      "  0.36339156 0.71582784 0.34568909 0.23164401]\n",
+      " [0.11922031 0.771128   0.20051449 0.07890044 0.96866031 0.34346829\n",
+      "  0.5116375  0.52732966 0.80637385 0.69435454]\n",
+      " [0.94291287 0.29238145 0.84711297 0.22849742 0.56967917 0.1636508\n",
+      "  0.15833751 0.84254917 0.05068486 0.54057582]\n",
+      " [0.17856374 0.71524686 0.66498292 0.00256622 0.72427854 0.50667812\n",
+      "  0.0894015  0.22688898 0.54873252 0.31727523]\n",
+      " [0.73578772 0.81479491 0.45620975 0.2662452  0.47757553 0.64322974\n",
+      "  0.54921401 0.70630967 0.51136852 0.59683811]\n",
+      " [0.37837864 0.71860732 0.35320952 0.67495943 0.16188604 0.41925189\n",
+      "  0.28956161 0.06685171 0.75654448 0.28923   ]\n",
+      " [0.91099021 0.8387133  0.18277213 0.40418675 0.7249499  0.46415522\n",
+      "  0.35018806 0.87148597 0.94141801 0.50911289]\n",
+      " [0.86066395 0.12235593 0.82418352 0.57881465 0.82559478 0.96826039\n",
+      "  0.291056   0.41675053 0.06430789 0.96432396]\n",
+      " [0.91270333 0.64362404 0.18816387 0.81318307 0.47989224 0.20375464\n",
+      "  0.33145794 0.92192012 0.33596404 0.18085537]]\n"
      ]
     }
    ],
@@ -1446,13 +1446,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.03681479262838276\n",
-      "3.936877889972962\n",
-      "-0.43976975777097144\n",
-      "[[ 1.18742521  3.6407249   4.14122256]\n",
-      " [ 3.6407249  12.06804775 12.20775115]\n",
-      " [ 4.14122256 12.20775115 20.86009093]]\n",
-      "[30.46593404  0.0604888   3.58914105]\n"
+      "0.1385510301651882\n",
+      "4.344176915013831\n",
+      "0.13134148011170077\n",
+      "[[ 0.87981676  2.52698542  2.65591748]\n",
+      " [ 2.52698542  8.29861395  7.76790092]\n",
+      " [ 2.65591748  7.76790092 13.4831139 ]]\n",
+      "[19.77845284  0.09028702  2.79280475]\n"
      ]
     }
    ],
@@ -1808,7 +1808,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6690/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m      7\u001b[0m               \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m               \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58840/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m      7\u001b[0m               \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m               \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index 4d27b5af7..7da267c35 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -1533,7 +1533,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9958983289118531\n"
+      "0.9969513794144311\n"
      ]
     }
    ],
@@ -1564,7 +1564,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.010348289064969316\n"
+      "0.007658477904313023\n"
      ]
     }
    ],
@@ -1599,23 +1599,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.01250964 0.00679013 0.06416459 0.02126205 0.01247909 0.00080497\n",
-      " 0.07373479 0.00940611 0.04794409 0.02361665 0.03311432 0.00114401\n",
-      " 0.06392846 0.0813156  0.03578526 0.00691012 0.05978512 0.02928194\n",
-      " 0.00209403 0.02014577 0.13600596 0.0178462  0.02016185 0.00574735\n",
-      " 0.029003   0.04877931 0.03796359 0.02374496 0.06622369 0.02965159\n",
-      " 0.01655384 0.00290613 0.00560098 0.00641423 0.05549529 0.03916813\n",
-      " 0.01288787 0.02472936 0.02564828 0.03365012 0.03642173 0.02102403\n",
-      " 0.00633289 0.02631942 0.02164667 0.04899367 0.00593362 0.03664879\n",
-      " 0.00250787 0.00791263 0.01480256 0.01329403 0.02151521 0.00133638\n",
-      " 0.01717549 0.01681612 0.03358833 0.01003519 0.00659604 0.02026756\n",
-      " 0.01234261 0.058967   0.0126711  0.0039259  0.00254973 0.04315855\n",
-      " 0.02909574 0.02386932 0.02404706 0.02769274 0.05804539 0.00732665\n",
-      " 0.03868372 0.00513051 0.00172865 0.00353938 0.03076019 0.01643795\n",
-      " 0.00458631 0.01817048 0.08667591 0.01706166 0.00637188 0.0930438\n",
-      " 0.01506354 0.02787184 0.0002926  0.09398153 0.00570321 0.03511387\n",
-      " 0.01075018 0.00170809 0.00943959 0.02654585 0.00399972 0.03057995\n",
-      " 0.00211418 0.00685287 0.02330799 0.04191323]\n"
+      "[0.00053122 0.03115122 0.00789262 0.02218076 0.00573727 0.00557893\n",
+      " 0.00099778 0.01234    0.03960002 0.03596557 0.0134113  0.00556946\n",
+      " 0.00136125 0.10719554 0.02754248 0.01409478 0.01760144 0.01032533\n",
+      " 0.01241284 0.01039879 0.00100913 0.02944152 0.00512599 0.00747773\n",
+      " 0.06260611 0.0231353  0.01624447 0.02923006 0.0046544  0.07332248\n",
+      " 0.02338085 0.02920675 0.02286267 0.04353549 0.00569512 0.02664408\n",
+      " 0.01098247 0.02156565 0.03529801 0.00507531 0.00554202 0.05141614\n",
+      " 0.02031987 0.01244297 0.01551724 0.00174738 0.01044475 0.01161645\n",
+      " 0.02622039 0.03285784 0.00522055 0.00687309 0.0195302  0.04101344\n",
+      " 0.00816675 0.0206033  0.04046513 0.02189863 0.06777772 0.04832356\n",
+      " 0.00114855 0.08660891 0.00586355 0.00625051 0.00939407 0.00108471\n",
+      " 0.03948301 0.02527621 0.03205795 0.11042239 0.02594314 0.05176711\n",
+      " 0.03396658 0.00889475 0.02632742 0.02502325 0.01266999 0.00455966\n",
+      " 0.03853313 0.01543076 0.00617221 0.00552462 0.01573062 0.01035006\n",
+      " 0.00162921 0.00974758 0.00812487 0.01881237 0.06690071 0.01499192\n",
+      " 0.04652794 0.04061345 0.04495752 0.00566707 0.01006984 0.00519717\n",
+      " 0.00151416 0.03214829 0.00891702 0.01844822]\n"
      ]
     }
    ],
@@ -1669,15 +1669,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.04641529 -0.77354301  7.7511273  -3.70241548  1.69515531]\n",
+      "[ 1.97243946  0.15593478  4.54011398  0.56342158 -0.19299283]\n",
       "Training R2\n",
-      "0.9958589197366403\n",
+      "0.9948998579029953\n",
       "Training MSE\n",
-      "0.008560581831215528\n",
+      "0.009396472959497925\n",
       "Test R2\n",
-      "0.997252717901263\n",
+      "0.9951059931014423\n",
       "Test MSE\n",
-      "0.00683875021199284\n"
+      "0.010612904886352397\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
index fca6a2395..03a159c41 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
@@ -1671,7 +1671,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.213    14.98        100.211        0.149466\n"
+      " 99.8244  15.0449        99.8227        0.150527\n"
      ]
     }
    ],
@@ -1737,7 +1737,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -2075,13 +2075,7 @@
       "Error: 0.08426840630693411\n",
       "Bias^2: 0.0796891867672603\n",
       "Var: 0.004579219539673834\n",
-      "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n",
       "Polynomial degree: 2\n",
       "Error: 0.10398646080125035\n",
       "Bias^2: 0.1007711427354898\n",
@@ -2112,13 +2106,7 @@
       "Error: 0.037813671417389005\n",
       "Bias^2: 0.033657685071527665\n",
       "Var: 0.00415598634586135\n",
-      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n",
       "Polynomial degree: 7\n",
       "Error: 0.02760977349102253\n",
       "Bias^2: 0.022999498260366312\n",
@@ -2133,13 +2121,7 @@
       "Error: 0.02660572763718093\n",
       "Bias^2: 0.010018312644137363\n",
       "Var: 0.016587414993043573\n",
-      "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n",
       "Polynomial degree: 10\n",
       "Error: 0.021592704588025025\n",
       "Bias^2: 0.010516485576645508\n",
@@ -2151,7 +2133,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Polynomial degree: 11\n",
+      "Polynomial degree:"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 11\n",
       "Error: 0.07160048164233104\n",
       "Bias^2: 0.014436800088904942\n",
       "Var: 0.05716368155342608\n",
@@ -2177,7 +2166,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_6.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_4.png"
       }
      },
      "output_type": "display_data"
@@ -2604,43 +2593,37 @@
       "Mean squared error on test data: 10.50427787\n",
       "Degree of polynomial:   7\n",
       "Mean squared error on training data: 0.47313680\n",
-      "Mean squared error on test data: 1.53738247\n"
+      "Mean squared error on test data: 1.53738247\n",
+      "Degree of polynomial:   8\n",
+      "Mean squared error on training data: 0.04926746\n",
+      "Mean squared error on test data: 0.14629156\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:   8\n",
-      "Mean squared error on training data: 0.04926746\n",
-      "Mean squared error on test data: 0.14629156\n",
       "Degree of polynomial:   9\n",
       "Mean squared error on training data: 0.02546675\n",
       "Mean squared error on test data: 0.11202337\n",
       "Degree of polynomial:  10\n",
       "Mean squared error on training data: 0.02424794\n",
-      "Mean squared error on test data: 0.22467274\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Mean squared error on test data: 0.22467274\n",
       "Degree of polynomial:  11\n",
       "Mean squared error on training data: 0.01594452\n",
       "Mean squared error on test data: 1.07641937\n",
       "Degree of polynomial:  12\n",
       "Mean squared error on training data: 0.00805074\n",
-      "Mean squared error on test data: 0.04295757\n",
-      "Degree of polynomial:  13\n",
-      "Mean squared error on training data: 0.00781918\n",
-      "Mean squared error on test data: 0.56965674\n"
+      "Mean squared error on test data: 0.04295757\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  13\n",
+      "Mean squared error on training data: 0.00781918\n",
+      "Mean squared error on test data: 0.56965674\n",
       "Degree of polynomial:  14\n",
       "Mean squared error on training data: 0.00465099\n",
       "Mean squared error on test data: 0.28443039\n",
@@ -2664,43 +2647,37 @@
       "Mean squared error on test data: 429.25695398\n",
       "Degree of polynomial:  19\n",
       "Mean squared error on training data: 0.00154853\n",
-      "Mean squared error on test data: 239.97065359\n"
+      "Mean squared error on test data: 239.97065359\n",
+      "Degree of polynomial:  20\n",
+      "Mean squared error on training data: 0.00140846\n",
+      "Mean squared error on test data: 1350.24493666\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  20\n",
-      "Mean squared error on training data: 0.00140846\n",
-      "Mean squared error on test data: 1350.24493666\n",
       "Degree of polynomial:  21\n",
       "Mean squared error on training data: 0.00119688\n",
       "Mean squared error on test data: 1840.50530832\n",
       "Degree of polynomial:  22\n",
       "Mean squared error on training data: 0.00092898\n",
-      "Mean squared error on test data: 1184.60929685\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Mean squared error on test data: 1184.60929685\n",
       "Degree of polynomial:  23\n",
       "Mean squared error on training data: 0.00089193\n",
       "Mean squared error on test data: 3892.17483760\n",
       "Degree of polynomial:  24\n",
       "Mean squared error on training data: 0.00083355\n",
-      "Mean squared error on test data: 1332.46736215\n",
-      "Degree of polynomial:  25\n",
-      "Mean squared error on training data: 0.00079904\n",
-      "Mean squared error on test data: 7577.76690383\n"
+      "Mean squared error on test data: 1332.46736215\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  25\n",
+      "Mean squared error on training data: 0.00079904\n",
+      "Mean squared error on test data: 7577.76690383\n",
       "Degree of polynomial:  26\n",
       "Mean squared error on training data: 0.00075590\n",
       "Mean squared error on test data: 1079.36895644\n",
@@ -2725,9 +2702,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2740,7 +2717,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_148_11.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_148_9.png"
       }
      },
      "output_type": "display_data"
@@ -2862,7 +2839,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png b/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
index bfe58af2b..66f7c5bbc 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "54608ce4",
+   "id": "a811ba80",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "702fc25a",
+   "id": "e5014a9c",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "708a3ac3",
+   "id": "023eb6d1",
    "metadata": {
     "editable": true
    },
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca760e72",
+   "id": "e981c015",
    "metadata": {
     "editable": true
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@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9fd5329",
+   "id": "11590c09",
    "metadata": {
     "editable": true
    },
@@ -85,12 +85,14 @@
     "\n",
     "  * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at \n",
     "\n",
-    "  * Work on project 1, in particular resampling methods like cross-validation and bootstrap."
+    "  * Work on project 1, in particular resampling methods like cross-validation and bootstrap.\n",
+    "\n",
+    "  * [Video on cross-validation from exercise session](https://youtu.be/T9jjWsmsd1o)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "63b2922e",
+   "id": "57e011be",
    "metadata": {
     "editable": true
    },
@@ -100,7 +102,7 @@
   },
   {
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-   "id": "d5f9f534",
+   "id": "0896e712",
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     "editable": true
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@@ -122,7 +124,7 @@
   },
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-   "id": "0143d162",
+   "id": "44bb3650",
    "metadata": {
     "editable": true
    },
@@ -148,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef85093d",
+   "id": "921c6771",
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     "editable": true
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@@ -172,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99320f0f",
+   "id": "f80e9666",
    "metadata": {
     "editable": true
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@@ -197,7 +199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44bcf7a3",
+   "id": "952f8119",
    "metadata": {
     "editable": true
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@@ -209,7 +211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "403aa0cd",
+   "id": "9b587b40",
    "metadata": {
     "editable": true
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@@ -227,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7703e42f",
+   "id": "bfb711d7",
    "metadata": {
     "editable": true
    },
@@ -245,7 +247,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "025d7cc2",
+   "id": "0acaaf3c",
    "metadata": {
     "editable": true
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@@ -256,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02656176",
+   "id": "73564ce7",
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     "editable": true
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@@ -283,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "190a0e98",
+   "id": "ef6011fd",
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     "editable": true
    },
@@ -296,7 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d3aa4736",
+   "id": "3444ad7b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -383,7 +385,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b52054ec",
+   "id": "01d01242",
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@@ -396,7 +398,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8c348f42",
+   "id": "143c59fe",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -415,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6df16129",
+   "id": "42136436",
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     "editable": true
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@@ -426,7 +428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a89924c",
+   "id": "e8a7f059",
    "metadata": {
     "editable": true
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@@ -438,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf2297a8",
+   "id": "f1c0bcf8",
    "metadata": {
     "editable": true
    },
@@ -457,7 +459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a865cd76",
+   "id": "e4fd2845",
    "metadata": {
     "editable": true
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@@ -479,7 +481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4137be07",
+   "id": "f4bb77ad",
    "metadata": {
     "editable": true
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@@ -491,7 +493,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42c4a103",
+   "id": "47fc800d",
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     "editable": true
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@@ -501,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b193f51",
+   "id": "0fe9154b",
    "metadata": {
     "editable": true
    },
@@ -514,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "02f9e114",
+   "id": "150c4acd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -579,7 +581,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf81cc5c",
+   "id": "9c1d64b9",
    "metadata": {
     "editable": true
    },
@@ -591,7 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "905fc981",
+   "id": "d1929423",
    "metadata": {
     "editable": true
    },
@@ -606,7 +608,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c734796",
+   "id": "1698b9e7",
    "metadata": {
     "editable": true
    },
@@ -618,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c72d9c5f",
+   "id": "eff2f862",
    "metadata": {
     "editable": true
    },
@@ -630,7 +632,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc0af9ee",
+   "id": "640f9f45",
    "metadata": {
     "editable": true
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@@ -647,7 +649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7362c186",
+   "id": "f94fafba",
    "metadata": {
     "editable": true
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@@ -661,7 +663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "553b6180",
+   "id": "5d457b2e",
    "metadata": {
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@@ -671,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a4286b1",
+   "id": "683657ba",
    "metadata": {
     "editable": true
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@@ -683,7 +685,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5873ef6a",
+   "id": "3d17d95b",
    "metadata": {
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@@ -695,7 +697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97f8406f",
+   "id": "76cd7541",
    "metadata": {
     "editable": true
    },
@@ -707,7 +709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d492a29a",
+   "id": "b88061e7",
    "metadata": {
     "editable": true
    },
@@ -718,7 +720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b384d3ea",
+   "id": "3c95fe37",
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@@ -730,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "257c19a6",
+   "id": "4f573bed",
    "metadata": {
     "editable": true
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@@ -741,7 +743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fcc7b46",
+   "id": "08a700a8",
    "metadata": {
     "editable": true
    },
@@ -757,7 +759,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cccc7482",
+   "id": "9bd6709b",
    "metadata": {
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@@ -769,7 +771,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff305cbe",
+   "id": "98c81b67",
    "metadata": {
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@@ -779,7 +781,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1aa23e",
+   "id": "5540b76a",
    "metadata": {
     "editable": true
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@@ -791,7 +793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6799086b",
+   "id": "0018d823",
    "metadata": {
     "editable": true
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@@ -806,7 +808,7 @@
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   {
    "cell_type": "markdown",
-   "id": "cda3c102",
+   "id": "ee63f4f9",
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@@ -818,7 +820,7 @@
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   {
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+   "id": "413ff641",
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@@ -829,7 +831,7 @@
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-   "id": "28868ab9",
+   "id": "337a2c56",
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@@ -841,7 +843,7 @@
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+   "id": "8c3e92fe",
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@@ -853,7 +855,7 @@
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   {
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+   "id": "ba84fae7",
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@@ -865,7 +867,7 @@
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   {
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-   "id": "9312b78f",
+   "id": "bddd73d3",
    "metadata": {
     "editable": true
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@@ -875,7 +877,7 @@
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-   "id": "edbb0614",
+   "id": "fce6aba6",
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@@ -887,7 +889,7 @@
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   {
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+   "id": "63325aad",
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@@ -901,7 +903,7 @@
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@@ -913,7 +915,7 @@
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@@ -923,7 +925,7 @@
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@@ -935,7 +937,7 @@
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@@ -945,7 +947,7 @@
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@@ -957,7 +959,7 @@
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@@ -968,7 +970,7 @@
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@@ -991,7 +993,7 @@
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@@ -1003,7 +1005,7 @@
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@@ -1013,7 +1015,7 @@
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@@ -1025,7 +1027,7 @@
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@@ -1042,7 +1044,7 @@
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@@ -1061,7 +1063,7 @@
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@@ -1116,7 +1118,7 @@
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@@ -1128,7 +1130,7 @@
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@@ -1143,7 +1145,7 @@
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@@ -1196,7 +1198,7 @@
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@@ -1211,7 +1213,7 @@
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@@ -1231,7 +1233,7 @@
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@@ -1285,7 +1287,7 @@
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@@ -1300,7 +1302,7 @@
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@@ -1327,7 +1329,7 @@
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@@ -1341,7 +1343,7 @@
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@@ -1386,7 +1388,7 @@
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@@ -1411,7 +1413,7 @@
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@@ -1423,7 +1425,7 @@
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@@ -1434,7 +1436,7 @@
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@@ -1446,7 +1448,7 @@
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@@ -1459,7 +1461,7 @@
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@@ -1470,7 +1472,7 @@
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@@ -1513,7 +1515,7 @@
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@@ -1534,7 +1536,7 @@
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@@ -1551,7 +1553,7 @@
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@@ -1566,7 +1568,7 @@
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@@ -1576,7 +1578,7 @@
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@@ -1592,7 +1594,7 @@
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@@ -1604,7 +1606,7 @@
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@@ -1615,7 +1617,7 @@
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@@ -1627,7 +1629,7 @@
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@@ -1637,7 +1639,7 @@
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@@ -1651,7 +1653,7 @@
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@@ -1663,7 +1665,7 @@
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@@ -1673,7 +1675,7 @@
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@@ -1685,7 +1687,7 @@
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@@ -1697,7 +1699,7 @@
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@@ -1717,7 +1719,7 @@
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@@ -1733,7 +1735,7 @@
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@@ -1749,7 +1751,7 @@
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@@ -1760,7 +1762,7 @@
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@@ -1772,7 +1774,7 @@
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@@ -1782,7 +1784,7 @@
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@@ -1794,7 +1796,7 @@
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@@ -1804,7 +1806,7 @@
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@@ -1816,7 +1818,7 @@
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@@ -1838,7 +1840,7 @@
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@@ -1851,7 +1853,7 @@
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@@ -1864,7 +1866,7 @@
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@@ -1874,7 +1876,7 @@
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@@ -1892,7 +1894,7 @@
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@@ -1902,7 +1904,7 @@
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@@ -1917,7 +1919,7 @@
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@@ -1927,7 +1929,7 @@
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@@ -1941,7 +1943,7 @@
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@@ -1951,7 +1953,7 @@
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@@ -1965,7 +1967,7 @@
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@@ -1980,7 +1982,7 @@
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@@ -1997,7 +1999,7 @@
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@@ -2009,7 +2011,7 @@
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@@ -2023,7 +2025,7 @@
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@@ -2038,7 +2040,7 @@
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@@ -2050,7 +2052,7 @@
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@@ -2061,7 +2063,7 @@
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@@ -2089,7 +2091,7 @@
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@@ -2111,7 +2113,7 @@
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@@ -2133,7 +2135,7 @@
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@@ -2145,7 +2147,7 @@
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@@ -2182,7 +2184,7 @@
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@@ -2210,7 +2212,7 @@
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@@ -2240,7 +2242,7 @@
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@@ -2264,7 +2266,7 @@
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@@ -2277,7 +2279,7 @@
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@@ -2288,7 +2290,7 @@
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@@ -2300,7 +2302,7 @@
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@@ -2310,7 +2312,7 @@
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@@ -2322,7 +2324,7 @@
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@@ -2336,7 +2338,7 @@
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@@ -2352,7 +2354,7 @@
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@@ -2362,7 +2364,7 @@
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@@ -2374,7 +2376,7 @@
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@@ -2384,7 +2386,7 @@
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@@ -2396,7 +2398,7 @@
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@@ -2410,7 +2412,7 @@
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@@ -2420,7 +2422,7 @@
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@@ -2431,7 +2433,7 @@
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@@ -2446,7 +2448,7 @@
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@@ -2456,7 +2458,7 @@
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@@ -2468,7 +2470,7 @@
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@@ -2480,7 +2482,7 @@
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@@ -2495,7 +2497,7 @@
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@@ -2508,7 +2510,7 @@
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@@ -2565,7 +2567,7 @@
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@@ -2576,7 +2578,7 @@
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-   "id": "1ba65efc",
+   "id": "d29a0ccf",
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     "editable": true
@@ -2603,7 +2605,7 @@
   },
   {
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-   "id": "845acf36",
+   "id": "7d20e2cc",
    "metadata": {
     "editable": true
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@@ -2615,7 +2617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae354b8b",
+   "id": "52a46927",
    "metadata": {
     "editable": true
    },
@@ -2627,7 +2629,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6349642",
+   "id": "446851f9",
    "metadata": {
     "editable": true
    },
@@ -2637,7 +2639,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4a4a710",
+   "id": "dc10da38",
    "metadata": {
     "editable": true
    },
@@ -2651,7 +2653,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a8c4b43",
+   "id": "e15d77aa",
    "metadata": {
     "editable": true
    },
@@ -2661,7 +2663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4efb50c",
+   "id": "89cd7379",
    "metadata": {
     "editable": true
    },
@@ -2673,7 +2675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d180548c",
+   "id": "20a6a0b6",
    "metadata": {
     "editable": true
    },
@@ -2684,7 +2686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87acd12f",
+   "id": "2bcf31af",
    "metadata": {
     "editable": true
    },
@@ -2699,7 +2701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "869f478b",
+   "id": "3f9a5445",
    "metadata": {
     "editable": true
    },
@@ -2713,7 +2715,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9035cf3a",
+   "id": "003f6d0d",
    "metadata": {
     "editable": true
    },
@@ -2724,7 +2726,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "10e7bb54",
+   "id": "bb679580",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2785,7 +2787,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a349d39",
+   "id": "2050684c",
    "metadata": {
     "editable": true
    },
@@ -2807,7 +2809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33424a86",
+   "id": "6b20b26d",
    "metadata": {
     "editable": true
    },
@@ -2819,7 +2821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "facda6dc",
+   "id": "3570021a",
    "metadata": {
     "editable": true
    },
@@ -2829,7 +2831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71006b8a",
+   "id": "c5f36ff0",
    "metadata": {
     "editable": true
    },
@@ -2854,7 +2856,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a7b6bd2",
+   "id": "a6e47b16",
    "metadata": {
     "editable": true
    },
@@ -2870,7 +2872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01381fbd",
+   "id": "5b7545c5",
    "metadata": {
     "editable": true
    },
@@ -2886,7 +2888,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db6812a1",
+   "id": "fa3a49a2",
    "metadata": {
     "editable": true
    },
@@ -2900,7 +2902,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e98af5ba",
+   "id": "685304e1",
    "metadata": {
     "editable": true
    },
@@ -2928,7 +2930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3fb483e",
+   "id": "9cac2104",
    "metadata": {
     "editable": true
    },
@@ -2941,7 +2943,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "b78643de",
+   "id": "1134c2ed",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.py b/doc/LectureNotes/_build/jupyter_execute/week38.py
index 42b468135..a049a9bb0 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week38.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week38.py
@@ -48,6 +48,8 @@
 #   * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at 
 # 
 #   * Work on project 1, in particular resampling methods like cross-validation and bootstrap.
+# 
+#   * [Video on cross-validation from exercise session](https://youtu.be/T9jjWsmsd1o)
 
 # ## Material for lecture Monday September 16
 
diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf
index 7442e5c7c80428c3d6a21989b51a8498e45ae008..c928b2724e3afcb5042cf602b8c13b09f0d99398 100644
GIT binary patch
delta 29
kcmX@!%y+z*uc3vpg=q`3NFBSmp^2fHp~ZITI_5RZ0G4S8`Tzg`

delta 29
kcmX@!%y+z*uc3vpg=q`3NFBSOfvK^fvH5oCI_5RZ0G0{}@&Et;

diff --git a/doc/LectureNotes/week38.ipynb b/doc/LectureNotes/week38.ipynb
index 343d401bc..68998261e 100644
--- a/doc/LectureNotes/week38.ipynb
+++ b/doc/LectureNotes/week38.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "54608ce4",
+   "id": "a811ba80",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "702fc25a",
+   "id": "e5014a9c",
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@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "708a3ac3",
+   "id": "023eb6d1",
    "metadata": {
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@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca760e72",
+   "id": "e981c015",
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@@ -70,7 +70,7 @@
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   {
    "cell_type": "markdown",
-   "id": "a9fd5329",
+   "id": "11590c09",
    "metadata": {
     "editable": true
    },
@@ -85,12 +85,14 @@
     "\n",
     "  * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at \n",
     "\n",
-    "  * Work on project 1, in particular resampling methods like cross-validation and bootstrap."
+    "  * Work on project 1, in particular resampling methods like cross-validation and bootstrap.\n",
+    "\n",
+    "  * [Video on cross-validation from exercise session](https://youtu.be/T9jjWsmsd1o)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "63b2922e",
+   "id": "57e011be",
    "metadata": {
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@@ -100,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5f9f534",
+   "id": "0896e712",
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@@ -122,7 +124,7 @@
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   {
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-   "id": "0143d162",
+   "id": "44bb3650",
    "metadata": {
     "editable": true
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@@ -148,7 +150,7 @@
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   {
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-   "id": "ef85093d",
+   "id": "921c6771",
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@@ -172,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99320f0f",
+   "id": "f80e9666",
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@@ -197,7 +199,7 @@
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   {
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-   "id": "44bcf7a3",
+   "id": "952f8119",
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@@ -209,7 +211,7 @@
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   {
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-   "id": "403aa0cd",
+   "id": "9b587b40",
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@@ -227,7 +229,7 @@
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   {
    "cell_type": "markdown",
-   "id": "7703e42f",
+   "id": "bfb711d7",
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@@ -245,7 +247,7 @@
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   {
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@@ -256,7 +258,7 @@
   },
   {
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@@ -283,7 +285,7 @@
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   {
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+   "id": "ef6011fd",
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@@ -296,7 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d3aa4736",
+   "id": "3444ad7b",
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     "editable": true
@@ -363,7 +365,7 @@
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   {
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-   "id": "b52054ec",
+   "id": "01d01242",
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@@ -376,7 +378,7 @@
   {
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-   "id": "8c348f42",
+   "id": "143c59fe",
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@@ -395,7 +397,7 @@
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@@ -406,7 +408,7 @@
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+   "id": "e8a7f059",
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@@ -418,7 +420,7 @@
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+   "id": "f1c0bcf8",
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@@ -437,7 +439,7 @@
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   {
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-   "id": "a865cd76",
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@@ -459,7 +461,7 @@
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-   "id": "4137be07",
+   "id": "f4bb77ad",
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@@ -471,7 +473,7 @@
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@@ -481,7 +483,7 @@
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   {
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@@ -494,7 +496,7 @@
   {
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    "execution_count": 3,
-   "id": "02f9e114",
+   "id": "150c4acd",
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@@ -559,7 +561,7 @@
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@@ -571,7 +573,7 @@
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@@ -586,7 +588,7 @@
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+   "id": "1698b9e7",
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@@ -598,7 +600,7 @@
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   {
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+   "id": "eff2f862",
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@@ -610,7 +612,7 @@
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   {
    "cell_type": "markdown",
-   "id": "bc0af9ee",
+   "id": "640f9f45",
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@@ -627,7 +629,7 @@
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   {
    "cell_type": "markdown",
-   "id": "7362c186",
+   "id": "f94fafba",
    "metadata": {
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@@ -641,7 +643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "553b6180",
+   "id": "5d457b2e",
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@@ -651,7 +653,7 @@
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   {
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-   "id": "4a4286b1",
+   "id": "683657ba",
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@@ -663,7 +665,7 @@
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   {
    "cell_type": "markdown",
-   "id": "5873ef6a",
+   "id": "3d17d95b",
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@@ -675,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97f8406f",
+   "id": "76cd7541",
    "metadata": {
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@@ -687,7 +689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d492a29a",
+   "id": "b88061e7",
    "metadata": {
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@@ -698,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b384d3ea",
+   "id": "3c95fe37",
    "metadata": {
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@@ -710,7 +712,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "257c19a6",
+   "id": "4f573bed",
    "metadata": {
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@@ -721,7 +723,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fcc7b46",
+   "id": "08a700a8",
    "metadata": {
     "editable": true
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@@ -737,7 +739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cccc7482",
+   "id": "9bd6709b",
    "metadata": {
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@@ -749,7 +751,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff305cbe",
+   "id": "98c81b67",
    "metadata": {
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@@ -759,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1aa23e",
+   "id": "5540b76a",
    "metadata": {
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@@ -771,7 +773,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6799086b",
+   "id": "0018d823",
    "metadata": {
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@@ -786,7 +788,7 @@
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   {
    "cell_type": "markdown",
-   "id": "cda3c102",
+   "id": "ee63f4f9",
    "metadata": {
     "editable": true
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@@ -798,7 +800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5be8ca2",
+   "id": "413ff641",
    "metadata": {
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@@ -809,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28868ab9",
+   "id": "337a2c56",
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@@ -821,7 +823,7 @@
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   {
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-   "id": "e92418db",
+   "id": "8c3e92fe",
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@@ -833,7 +835,7 @@
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   {
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-   "id": "5533c3f5",
+   "id": "ba84fae7",
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@@ -845,7 +847,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9312b78f",
+   "id": "bddd73d3",
    "metadata": {
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@@ -855,7 +857,7 @@
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   {
    "cell_type": "markdown",
-   "id": "edbb0614",
+   "id": "fce6aba6",
    "metadata": {
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@@ -867,7 +869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "394f6fae",
+   "id": "63325aad",
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@@ -881,7 +883,7 @@
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   {
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-   "id": "883253c4",
+   "id": "1c5878f6",
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@@ -893,7 +895,7 @@
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   {
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+   "id": "2c8a1b85",
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@@ -903,7 +905,7 @@
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   {
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-   "id": "6a4795d0",
+   "id": "cced4ec8",
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@@ -915,7 +917,7 @@
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   {
    "cell_type": "markdown",
-   "id": "deed99f4",
+   "id": "6efd1ce1",
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@@ -925,7 +927,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3ca93d73",
+   "id": "933753b8",
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@@ -937,7 +939,7 @@
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-   "id": "89672774",
+   "id": "ba94450f",
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@@ -948,7 +950,7 @@
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   {
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-   "id": "8a75d099",
+   "id": "8f174f5d",
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@@ -971,7 +973,7 @@
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   {
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-   "id": "780a498d",
+   "id": "9ba36ed7",
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@@ -983,7 +985,7 @@
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   {
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-   "id": "f19ad43b",
+   "id": "b5b5ecc6",
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@@ -993,7 +995,7 @@
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-   "id": "25956ba0",
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@@ -1005,7 +1007,7 @@
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@@ -1022,7 +1024,7 @@
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@@ -1041,7 +1043,7 @@
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    "execution_count": 4,
-   "id": "2cc35ceb",
+   "id": "5dc4fd0e",
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     "editable": true
@@ -1096,7 +1098,7 @@
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   {
    "cell_type": "markdown",
-   "id": "d5c16758",
+   "id": "70944449",
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@@ -1108,7 +1110,7 @@
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-   "id": "67640d94",
+   "id": "1538973c",
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@@ -1123,7 +1125,7 @@
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-   "id": "6aee13c4",
+   "id": "1c1fdba0",
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@@ -1176,7 +1178,7 @@
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+   "id": "dd6f78eb",
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@@ -1191,7 +1193,7 @@
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-   "id": "b32612ca",
+   "id": "20b7afcb",
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@@ -1211,7 +1213,7 @@
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-   "id": "ec7749b1",
+   "id": "4810b670",
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@@ -1265,7 +1267,7 @@
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@@ -1280,7 +1282,7 @@
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@@ -1307,7 +1309,7 @@
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@@ -1321,7 +1323,7 @@
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@@ -1366,7 +1368,7 @@
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@@ -1391,7 +1393,7 @@
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@@ -1403,7 +1405,7 @@
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@@ -1414,7 +1416,7 @@
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@@ -1426,7 +1428,7 @@
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@@ -1439,7 +1441,7 @@
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@@ -1450,7 +1452,7 @@
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@@ -1493,7 +1495,7 @@
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@@ -1514,7 +1516,7 @@
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@@ -1531,7 +1533,7 @@
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@@ -1546,7 +1548,7 @@
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@@ -1556,7 +1558,7 @@
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@@ -1572,7 +1574,7 @@
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@@ -1584,7 +1586,7 @@
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@@ -1595,7 +1597,7 @@
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@@ -1607,7 +1609,7 @@
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@@ -1617,7 +1619,7 @@
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@@ -1631,7 +1633,7 @@
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@@ -1643,7 +1645,7 @@
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@@ -1653,7 +1655,7 @@
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@@ -1665,7 +1667,7 @@
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@@ -1677,7 +1679,7 @@
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@@ -1697,7 +1699,7 @@
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@@ -1713,7 +1715,7 @@
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@@ -1729,7 +1731,7 @@
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@@ -1740,7 +1742,7 @@
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@@ -1752,7 +1754,7 @@
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@@ -1762,7 +1764,7 @@
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@@ -1774,7 +1776,7 @@
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@@ -1784,7 +1786,7 @@
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@@ -1796,7 +1798,7 @@
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@@ -1818,7 +1820,7 @@
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@@ -1831,7 +1833,7 @@
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@@ -1844,7 +1846,7 @@
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@@ -1854,7 +1856,7 @@
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@@ -1872,7 +1874,7 @@
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@@ -1882,7 +1884,7 @@
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@@ -1897,7 +1899,7 @@
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@@ -1907,7 +1909,7 @@
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@@ -1921,7 +1923,7 @@
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@@ -1931,7 +1933,7 @@
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@@ -1945,7 +1947,7 @@
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@@ -1960,7 +1962,7 @@
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@@ -1977,7 +1979,7 @@
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@@ -1989,7 +1991,7 @@
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@@ -2003,7 +2005,7 @@
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@@ -2018,7 +2020,7 @@
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@@ -2030,7 +2032,7 @@
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@@ -2041,7 +2043,7 @@
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@@ -2069,7 +2071,7 @@
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@@ -2091,7 +2093,7 @@
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@@ -2113,7 +2115,7 @@
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@@ -2125,7 +2127,7 @@
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@@ -2162,7 +2164,7 @@
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@@ -2190,7 +2192,7 @@
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@@ -2220,7 +2222,7 @@
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@@ -2244,7 +2246,7 @@
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@@ -2257,7 +2259,7 @@
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@@ -2268,7 +2270,7 @@
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@@ -2280,7 +2282,7 @@
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@@ -2290,7 +2292,7 @@
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@@ -2302,7 +2304,7 @@
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@@ -2316,7 +2318,7 @@
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@@ -2332,7 +2334,7 @@
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@@ -2342,7 +2344,7 @@
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@@ -2354,7 +2356,7 @@
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@@ -2364,7 +2366,7 @@
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@@ -2376,7 +2378,7 @@
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@@ -2390,7 +2392,7 @@
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@@ -2400,7 +2402,7 @@
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@@ -2411,7 +2413,7 @@
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@@ -2426,7 +2428,7 @@
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@@ -2436,7 +2438,7 @@
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@@ -2448,7 +2450,7 @@
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@@ -2460,7 +2462,7 @@
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@@ -2475,7 +2477,7 @@
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@@ -2488,7 +2490,7 @@
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-   "id": "20e6fb89",
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@@ -2545,7 +2547,7 @@
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@@ -2556,7 +2558,7 @@
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-   "id": "1ba65efc",
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@@ -2583,7 +2585,7 @@
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@@ -2595,7 +2597,7 @@
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@@ -2607,7 +2609,7 @@
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@@ -2617,7 +2619,7 @@
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@@ -2631,7 +2633,7 @@
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@@ -2641,7 +2643,7 @@
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@@ -2653,7 +2655,7 @@
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@@ -2664,7 +2666,7 @@
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@@ -2679,7 +2681,7 @@
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@@ -2693,7 +2695,7 @@
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@@ -2704,7 +2706,7 @@
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@@ -2765,7 +2767,7 @@
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@@ -2787,7 +2789,7 @@
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@@ -2799,7 +2801,7 @@
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@@ -2809,7 +2811,7 @@
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@@ -2834,7 +2836,7 @@
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@@ -2850,7 +2852,7 @@
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@@ -2866,7 +2868,7 @@
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@@ -2880,7 +2882,7 @@
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   {
    "cell_type": "markdown",
-   "id": "e98af5ba",
+   "id": "685304e1",
    "metadata": {
     "editable": true
    },
@@ -2908,7 +2910,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3fb483e",
+   "id": "9cac2104",
    "metadata": {
     "editable": true
    },
@@ -2921,7 +2923,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "b78643de",
+   "id": "1134c2ed",
    "metadata": {
     "collapsed": false,
     "editable": true