diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 2a2517518..74919a956 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -6,15 +6,15 @@ 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="worst smoothness <= 0.107\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="concave points error <= 0.016\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; -6 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; +6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 6 ; -7 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; +7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; 5 -> 7 ; 8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; 2 -> 8 ; @@ -22,7 +22,7 @@ edge [fontname="helvetica"] ; 8 -> 9 ; 10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ; 8 -> 10 ; -11 [label="worst texture <= 24.785\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; +11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; 1 -> 11 ; 12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 11 -> 12 ; @@ -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 concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst radius <= 17.74\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="radius error <= 0.251\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="mean texture <= 13.745\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 ; @@ -42,7 +42,7 @@ 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="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; +21 [label="concave points error <= 0.01\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 ; diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index 621604b4a..7666dd4ea 100644 Binary files a/doc/LectureNotes/DataFiles/cancer.png and b/doc/LectureNotes/DataFiles/cancer.png differ diff --git a/doc/LectureNotes/_build/.doctrees/chapter1.doctree b/doc/LectureNotes/_build/.doctrees/chapter1.doctree index f90b9e2b6..6c1688214 100644 Binary files a/doc/LectureNotes/_build/.doctrees/chapter1.doctree and 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a/doc/LectureNotes/_build/html/_sources/week42.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week42.ipynb @@ -2635,7 +2635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/html/_sources/week45.ipynb b/doc/LectureNotes/_build/html/_sources/week45.ipynb index 749491e98..f3cbde629 100644 --- a/doc/LectureNotes/_build/html/_sources/week45.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week45.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "31a10e2b", + "id": "7e9869f2", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "019daa83", + "id": "fca263d7", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "6ecf1c2b", + "id": "8c3d9fea", "metadata": { "editable": true }, @@ -42,6 +42,8 @@ "\n", " * [Video of lab session from week 44](https://youtu.be/EajWMW__k0I)\n", "\n", + " * [Video of lab session from week 45](https://youtu.be/tgkj0KAEtZo)\n", + "\n", " * [See also whiteboard notes from lab session week 44](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/Exercisesweek44.pdf)\n", "\n", " \n", @@ -67,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "d80a4db8", + "id": "d0107e8f", "metadata": { "editable": true }, @@ -77,7 +79,7 @@ }, { "cell_type": "markdown", - "id": "1ded5c92", + "id": "44ed2bb7", "metadata": { "editable": true }, @@ -96,7 +98,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "6dea0513", + "id": "7d202a1c", "metadata": { "collapsed": false, "editable": true @@ -153,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "440bf579", + "id": "0fcc974f", "metadata": { "editable": true }, @@ -165,7 +167,7 @@ }, { "cell_type": "markdown", - "id": "683b1da2", + "id": "693475c8", "metadata": { "editable": true }, @@ -180,7 +182,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "38be79e0", + "id": "cdd8cf73", "metadata": { "collapsed": false, "editable": true @@ -233,7 +235,7 @@ }, { "cell_type": "markdown", - "id": "081fbe5c", + "id": "51ef3976", "metadata": { "editable": true }, @@ -248,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "a9798c07", + "id": "033bda9d", "metadata": { "editable": true }, @@ -268,7 +270,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "0a7e4e2e", + "id": "0a320c52", "metadata": { "collapsed": false, "editable": true @@ -322,7 +324,7 @@ }, { "cell_type": "markdown", - "id": "dcd07cb8", + "id": "bcd8ea70", "metadata": { "editable": true }, @@ -337,7 +339,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "65061d95", + "id": "6fe70002", "metadata": { "collapsed": false, "editable": true @@ -364,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "50492453", + "id": "c4440f93", "metadata": { "editable": true }, @@ -378,7 +380,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "08a2ab17", + "id": "96a74e80", "metadata": { "collapsed": false, "editable": true @@ -423,7 +425,7 @@ }, { "cell_type": "markdown", - "id": "5408b7dd", + "id": "98525677", "metadata": { "editable": true }, @@ -448,7 +450,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "76c3d259", + "id": "d00d3339", "metadata": { "collapsed": false, "editable": true @@ -460,7 +462,7 @@ }, { "cell_type": "markdown", - "id": "83903231", + "id": "39d74647", "metadata": { "editable": true }, @@ -471,7 +473,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "58ccd6c3", + "id": "382459a0", "metadata": { "collapsed": false, "editable": true @@ -483,7 +485,7 @@ }, { "cell_type": "markdown", - "id": "adda54cb", + "id": "06da1caf", "metadata": { "editable": true }, @@ -496,7 +498,7 @@ }, { "cell_type": "markdown", - "id": "8799c034", + "id": "2d846128", "metadata": { "editable": true }, @@ -529,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "dfbe0571", + "id": "23991e3b", "metadata": { "editable": true }, @@ -543,7 +545,7 @@ }, { "cell_type": "markdown", - "id": "65f7c63d", + "id": "4581b004", "metadata": { "editable": true }, @@ -555,7 +557,7 @@ }, { "cell_type": "markdown", - "id": "2e674d10", + "id": "855fc1d5", "metadata": { "editable": true }, @@ -567,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "65577837", + "id": "7b676376", "metadata": { "editable": true }, @@ -579,7 +581,7 @@ }, { "cell_type": "markdown", - "id": "e5e8802d", + "id": "64e4f58f", "metadata": { "editable": true }, @@ -589,7 +591,7 @@ }, { "cell_type": "markdown", - "id": "07c61591", + "id": "6bef46ee", "metadata": { "editable": true }, @@ -601,7 +603,7 @@ }, { "cell_type": "markdown", - "id": "b3f61bb3", + "id": "ed7cfd1e", "metadata": { "editable": true }, @@ -611,7 +613,7 @@ }, { "cell_type": "markdown", - "id": "6544105e", + "id": "307589a6", "metadata": { "editable": true }, @@ -623,7 +625,7 @@ }, { "cell_type": "markdown", - "id": "0b571e85", + "id": "24f17f38", "metadata": { "editable": true }, @@ -634,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "17d6872a", + "id": "e825c026", "metadata": { "editable": true }, @@ -653,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "122f7e6c", + "id": "6dcb34a4", "metadata": { "editable": true }, @@ -665,7 +667,7 @@ }, { "cell_type": "markdown", - "id": "f44a5e78", + "id": "a975cae8", "metadata": { "editable": true }, @@ -675,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "86fa9670", + "id": "d4971baf", "metadata": { "editable": true }, @@ -687,7 +689,7 @@ }, { "cell_type": "markdown", - "id": "a30e358d", + "id": "a4bd001d", "metadata": { "editable": true }, @@ -699,7 +701,7 @@ }, { "cell_type": "markdown", - "id": "3f0de6de", + "id": "a8b1f114", "metadata": { "editable": true }, @@ -711,7 +713,7 @@ }, { "cell_type": "markdown", - "id": "0b4cc93f", + "id": "959cdbff", "metadata": { "editable": true }, @@ -721,7 +723,7 @@ }, { "cell_type": "markdown", - "id": "4866ec0a", + "id": "59ee7f47", "metadata": { "editable": true }, @@ -733,7 +735,7 @@ }, { "cell_type": "markdown", - "id": "9b1c8c45", + "id": "5cdba722", "metadata": { "editable": true }, @@ -761,7 +763,7 @@ }, { "cell_type": "markdown", - "id": "0e651a1f", + "id": "35390ed4", "metadata": { "editable": true }, @@ -773,7 +775,7 @@ }, { "cell_type": "markdown", - "id": "d83efe41", + "id": "b1651ea3", "metadata": { "editable": true }, @@ -798,7 +800,7 @@ }, { "cell_type": "markdown", - "id": "d088215b", + "id": "1bfac852", "metadata": { "editable": true }, @@ -816,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "5c0bcd1f", + "id": "f655bb24", "metadata": { "editable": true }, @@ -827,7 +829,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "2a6f80df", + "id": "31f4ee21", "metadata": { "collapsed": false, "editable": true @@ -870,7 +872,7 @@ }, { "cell_type": "markdown", - "id": "58e5b521", + "id": "098bfaec", "metadata": { "editable": true }, @@ -880,7 +882,7 @@ }, { "cell_type": "markdown", - "id": "4ebe5a91", + "id": "55cc0e20", "metadata": { "editable": true }, @@ -907,7 +909,7 @@ }, { "cell_type": "markdown", - "id": "182f425e", + "id": "8860da9c", "metadata": { "editable": true }, @@ -918,7 +920,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "8b3ca785", + "id": "7252c0b8", "metadata": { "collapsed": false, "editable": true @@ -991,7 +993,7 @@ }, { "cell_type": "markdown", - "id": "b0c12b4c", + "id": "fcbca2f7", "metadata": { "editable": true }, @@ -1011,7 +1013,7 @@ }, { "cell_type": "markdown", - "id": "d81020f6", + "id": "1c5b70a2", "metadata": { "editable": true }, @@ -1027,7 +1029,7 @@ }, { "cell_type": "markdown", - "id": "2d7453c9", + "id": "29c129fe", "metadata": { "editable": true }, @@ -1049,7 +1051,7 @@ }, { "cell_type": "markdown", - "id": "fc4a082e", + "id": "c2bc1aa3", "metadata": { "editable": true }, @@ -1067,7 +1069,7 @@ }, { "cell_type": "markdown", - "id": "c1106ad9", + "id": "01ef7e17", "metadata": { "editable": true }, @@ -1113,7 +1115,7 @@ }, { "cell_type": "markdown", - "id": "b139ef7b", + "id": "8dd26bf6", "metadata": { "editable": true }, @@ -1134,7 +1136,7 @@ }, { "cell_type": "markdown", - "id": "70c95078", + "id": "3b42d5ef", "metadata": { "editable": true }, @@ -1195,7 +1197,7 @@ }, { "cell_type": "markdown", - "id": "2ca24031", + "id": "2f8de715", "metadata": { "editable": true }, @@ -1220,7 +1222,7 @@ }, { "cell_type": "markdown", - "id": "93648979", + "id": "9998cb2c", "metadata": { "editable": true }, @@ -1239,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "b8806dcd", + "id": "45dfbeff", "metadata": { "editable": true }, @@ -1263,7 +1265,7 @@ }, { "cell_type": "markdown", - "id": "b0edad40", + "id": "141b8f50", "metadata": { "editable": true }, @@ -1345,7 +1347,7 @@ }, { "cell_type": "markdown", - "id": "58fd18f9", + "id": "fbadc1e0", "metadata": { "editable": true }, @@ -1361,7 +1363,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "a604caa5", + "id": "9365f92f", "metadata": { "collapsed": false, "editable": true @@ -1400,7 +1402,7 @@ }, { "cell_type": "markdown", - "id": "545336c9", + "id": "55c2aa23", "metadata": { "editable": true }, @@ -1444,7 +1446,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "3ef3ed13", + "id": "b57a3099", "metadata": { "collapsed": false, "editable": true @@ -1527,7 +1529,7 @@ }, { "cell_type": "markdown", - "id": "8a8b29b8", + "id": "0b519f6c", "metadata": { "editable": true }, @@ -1538,7 +1540,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "d840beea", + "id": "82db8594", "metadata": { "collapsed": false, "editable": true @@ -1640,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "2e5b339f", + "id": "4711b0d4", "metadata": { "editable": true }, @@ -1662,7 +1664,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "370e799d", + "id": "22123a0f", "metadata": { "collapsed": false, "editable": true @@ -1759,7 +1761,7 @@ }, { "cell_type": "markdown", - "id": "d8f62fc4", + "id": "50cd4d20", "metadata": { "editable": true }, @@ -1785,7 +1787,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "4ea36ea9", + "id": "5c3d5a53", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index 44221cffb..5093e2dc8 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output" Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations +
  • + + Week 44, Convolutional Neural Networks (CNN) + +
    • +
  • + + Week 45, Recurrent Neural Networks + +

    • @@ -1076,13 +1086,13 @@ example of the functionality of Scikit-Learn.

    The intercept alpha: 
- [1.95815651]
+ [1.79503422]
 Coefficient beta : 
- [[5.03219974]]
-Mean squared error: 0.26
-Variance score: 0.90
+ [[5.33918941]]
+Mean squared error: 0.21
+Variance score: 0.92
 Mean squared log error: 0.01
-Mean absolute error: 0.41
+Mean absolute error: 0.37
    _images/chapter1_19_1.png @@ -1182,7 +1192,7 @@ a linear \(x\)-dependence we s
    _images/chapter1_33_0.png -
    0.004999999999999996
+
    0.005
    diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 88afb4026..949eaabb5 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output" Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations +
  • + + Week 44, Convolutional Neural Networks (CNN) + +
    • +
  • + + Week 45, Recurrent Neural Networks + +

    • @@ -1393,7 +1403,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1727,7 +1737,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1736,7 +1746,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1745,7 +1755,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1754,7 +1764,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1763,7 +1773,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1772,7 +1782,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1781,7 +1791,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1790,11 +1800,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1803,11 +1813,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1816,11 +1826,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1829,11 +1839,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1842,11 +1852,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1855,7 +1865,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1864,11 +1874,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1877,11 +1887,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1890,11 +1900,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1903,11 +1913,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1916,11 +1926,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1929,11 +1939,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1942,11 +1952,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1955,38 +1965,17 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.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)
-Input In [8], in <cell line: 7>()
-      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)
-
-Input In [6], in NeuralNetwork.train(self)
-     95 self.X_data = self.X_data_full[chosen_datapoints]
-     96 self.Y_data = self.Y_data_full[chosen_datapoints]
----> 98 self.feed_forward()
-     99 self.backpropagation()
-
-Input In [6], in NeuralNetwork.feed_forward(self)
-     36 def feed_forward(self):
-     37     # feed-forward for training
----> 38     self.z_h = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias
-     39     self.a_h = sigmoid(self.z_h)
-     41     self.z_o = np.matmul(self.a_h, self.output_weights) + self.output_bias
-
-KeyboardInterrupt: 
+
    Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
@@ -2032,6 +2021,22 @@ Accuracy score on test set:  0.07777777777777778
    +
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
    +
    +_images/chapter10_59_1.png +_images/chapter10_59_2.png +
    @@ -2067,6 +2072,332 @@ performance overall.

    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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
+
    +
    +
    @@ -2110,6 +2441,10 @@ performance overall.

    +
    +_images/chapter10_63_0.png +_images/chapter10_63_1.png +
    @@ -2148,6 +2483,14 @@ and/or if you use anaconda, just write (or install from the gra
    +
    +
      Input In [12]
+    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 7fd994b10..11567163b 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output" Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations +
  • + + Week 44, Convolutional Neural Networks (CNN) + +
    • +
  • + + Week 45, Recurrent Neural Networks + +

    • @@ -2647,20 +2657,140 @@ 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:29, in grad(fun, x) - 26 if not vspace(ans).size == 1: - 27 raise TypeError("Grad only applies to real scalar-output functions. " - 28 "Try jacobian, elementwise_grad or holomorphic_grad.") ----> 29 return vjp(vspace(ans).ones()) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25, in grad(fun, x) + 18 @unary_to_nary + 19 def grad(fun, x): + 20 """ + 21 Returns a function which computes the gradient of `fun` with respect to + 22 positional argument number `argnum`. The returned function takes the same + 23 arguments as `fun`, but returns the gradient instead. The function `fun` + 24 should be scalar-valued. The gradient has the same type as the argument.""" +---> 25 vjp, ans = _make_vjp(fun, x) + 26 if not vspace(ans).size == 1: + 27 raise TypeError("Grad only applies to real scalar-output functions. " + 28 "Try jacobian, elementwise_grad or holomorphic_grad.") -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/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/core.py:23, in backward_pass(g, end_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) - 24 return outgrad[0] +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) + +Input In [9], in cost_function(P, x, t) + 78 g_t = g_trial(point,P) + 79 g_t_jacobian = g_t_jacobian_func(point,P) +---> 80 g_t_hessian = g_t_hessian_func(point,P) + 82 g_t_dt = g_t_jacobian[1] + 83 g_t_d2x = g_t_hessian[0][0] + +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/differential_operators.py:78, in hessian(fun, x) + 75 @unary_to_nary + 76 def hessian(fun, x): + 77 "Returns a function that computes the exact Hessian." +---> 78 return jacobian(jacobian(fun))(x) + +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/differential_operators.py:57, in jacobian(fun, x) + 47 @unary_to_nary + 48 def jacobian(fun, x): + 49 """ + 50 Returns a function which computes the Jacobian of `fun` with respect to + 51 positional argument number `argnum`, which must be a scalar or array. Unlike + (...) + 55 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...). + 56 """ +---> 57 vjp, ans = _make_vjp(fun, x) + 58 ans_vspace = vspace(ans) + 59 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: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/differential_operators.py:61, in jacobian(fun, x) + 59 jacobian_shape = ans_vspace.shape + vspace(x).shape + 60 grads = map(vjp, ans_vspace.standard_basis()) +---> 61 return np.reshape(np.stack(grads), jacobian_shape) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:103, in stack(arrays, axis) + 100 axis += result_ndim + 102 sl = (slice(None),) * axis + (None,) +--> 103 return concatenate([arr[sl] for arr in arrays], axis=axis) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:103, in <listcomp>(.0) + 100 axis += result_ndim + 102 sl = (slice(None),) * axis + (None,) +--> 103 return concatenate([arr[sl] for arr in arrays], axis=axis) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44, in primitive.<locals>.f_wrapped(*args, **kwargs) + 42 parents = tuple(box._node for _ , box in boxed_args) + 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) + +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:56, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs) + 53 argnums = kwargs.get('argnums', count()) + 54 vjps_dict = {argnum : translate_vjp(vjpmaker, fun, argnum) + 55 for argnum, vjpmaker in zip(argnums, vjpmakers)} +---> 56 def vjp_argnums(argnums, ans, args, kwargs): + 57 L = len(argnums) + 58 # These first two cases are just optimizations KeyboardInterrupt:
    diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index 43ed02538..ffdb7e34a 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output" Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations +
  • + + Week 44, Convolutional Neural Networks (CNN) + +
    • +
  • + + Week 45, Recurrent Neural Networks + +

    • @@ -1307,7 +1317,7 @@ labels = (n_inputs) = (1797,) 

    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
   super(SGD, self).__init__(name, **kwargs)
-2023-10-25 15:31:33.965944: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+2023-11-08 15:24:42.293245: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 
    ---------------------------------------------------------------------------
diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html
index 86ce44183..089d96f2a 100644
--- a/doc/LectureNotes/_build/html/chapter13.html
+++ b/doc/LectureNotes/_build/html/chapter13.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -710,316 +720,316 @@ systems such as automatic translation and speech-to-text.
    
 
    Epoch 1/100
 
    
 
    
-
    2023-10-25 15:32:11.077734: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+
    2023-11-08 15:25:18.982829: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 
    
 
    
-
    50/50 - 3s - loss: 1.4222 - 3s/epoch - 66ms/step
+
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    Epoch 2/100
 
    
 
    
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    Epoch 3/100
 
    
 
    
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    50/50 - 0s - loss: 0.3992 - 459ms/epoch - 9ms/step
+
    50/50 - 1s - loss: 0.3814 - 527ms/epoch - 11ms/step
 
    
 
    
 
    Epoch 42/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3814 - 464ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 43/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3812 - 453ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 44/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3975 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3779 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 45/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3972 - 457ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3797 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 46/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3948 - 460ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3808 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 47/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3954 - 458ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3788 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 48/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3931 - 462ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3792 - 455ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 49/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3946 - 461ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3765 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 50/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3940 - 458ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3775 - 454ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 51/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3935 - 457ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3777 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 52/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3932 - 458ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3770 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 53/100
diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html
index c5833b87a..8a93e1c74 100644
--- a/doc/LectureNotes/_build/html/chapter2.html
+++ b/doc/LectureNotes/_build/html/chapter2.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1325,10 +1335,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.023888460698069384
-4.161573669199933
-[[0.708589   2.01323615]
- [2.01323615 6.75406265]]
+
    0.07752620206774397
+4.3068687590657415
+[[1.01031184 2.85759522]
+ [2.85759522 9.0542566 ]]
 
    
 
    
 
    
@@ -1365,10 +1375,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.08737007811453563
-1.792898603630095
-[[1.         0.65673455]
- [0.65673455 1.        ]]
+
    0.07472152457534222
+1.4560786541572335
+[[1.         0.61219726]
+ [0.61219726 1.        ]]
 
    
 
    
 
    
@@ -1398,30 +1408,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[ 0.1036544   0.17432695]
- [ 0.51758232  1.25700419]
- [-1.37236385 -4.22805937]
- [-0.46766277 -1.52501047]
- [ 0.99175336  3.59187177]
- [-2.34718587 -5.3512747 ]
- [ 1.94980712  6.99450177]
- [ 0.24849282 -0.56424167]
- [-0.35408251 -2.3930301 ]
- [ 0.73000497  2.04391163]]
+
    [[-0.59265811 -0.57943748]
+ [ 0.08641073  0.06656566]
+ [-0.68596176 -2.59060904]
+ [ 0.47558206  1.64482901]
+ [ 0.21597684 -0.67723533]
+ [ 1.22935165  5.02293408]
+ [-0.51249881 -3.38478181]
+ [ 0.29220202  1.35149796]
+ [ 0.10955639  0.86177342]
+ [-0.61796102 -1.71553646]]
           0         1
-0  0.103654  0.174327
-1  0.517582  1.257004
-2 -1.372364 -4.228059
-3 -0.467663 -1.525010
-4  0.991753  3.591872
-5 -2.347186 -5.351275
-6  1.949807  6.994502
-7  0.248493 -0.564242
-8 -0.354083 -2.393030
-9  0.730005  2.043912
+0 -0.592658 -0.579437
+1  0.086411  0.066566
+2 -0.685962 -2.590609
+3  0.475582  1.644829
+4  0.215977 -0.677235
+5  1.229352  5.022934
+6 -0.512499 -3.384782
+7  0.292202  1.351498
+8  0.109556  0.861773
+9 -0.617961 -1.715536
           0         1
-0  1.000000  0.966337
-1  0.966337  1.000000
+0  1.000000  0.921567
+1  0.921567  1.000000
 
    
 
    
 
    
@@ -1478,37 +1488,37 @@ 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  0.084489  0.079202  0.083269  0.079700  0.076331  0.074355  0.071456   
-2   0.0  0.079202  0.075352  0.079849  0.077042  0.074328  0.072527  0.070086   
-3   0.0  0.083269  0.079849  0.087761  0.084946  0.082203  0.081779  0.079170   
-4   0.0  0.079700  0.077042  0.084946  0.082590  0.080248  0.079820  0.077517   
-5   0.0  0.076331  0.074328  0.082203  0.080248  0.078258  0.077833  0.075804   
-6   0.0  0.074355  0.072527  0.081779  0.079820  0.077833  0.078423  0.076337   
-7   0.0  0.071456  0.070086  0.079170  0.077517  0.075804  0.076337  0.074477   
-8   0.0  0.068743  0.067765  0.076678  0.075294  0.073824  0.074307  0.072650   
-9   0.0  0.066200  0.065559  0.074301  0.073154  0.071901  0.072338  0.070865   
-10  0.0  0.065631  0.064814  0.074306  0.072976  0.071564  0.072718  0.071080   
-11  0.0  0.063225  0.062696  0.071960  0.070845  0.069629  0.070705  0.069239   
-12  0.0  0.060971  0.060691  0.069733  0.068809  0.067769  0.068771  0.067462   
-13  0.0  0.058856  0.058793  0.067619  0.066865  0.065984  0.066919  0.065753   
-14  0.0  0.056870  0.056996  0.065613  0.065012  0.064275  0.065147  0.064113   
+1   0.0  0.079330  0.084471  0.080541  0.081753  0.082306  0.073011  0.072990   
+2   0.0  0.084471  0.092487  0.087280  0.089513  0.090777  0.079391  0.079777   
+3   0.0  0.080541  0.087280  0.087211  0.088900  0.089752  0.082231  0.082329   
+4   0.0  0.081753  0.089513  0.088900  0.091023  0.092206  0.083848  0.084164   
+5   0.0  0.082306  0.090777  0.089752  0.092206  0.093657  0.084672  0.085172   
+6   0.0  0.073011  0.079391  0.082231  0.083848  0.084672  0.079611  0.079715   
+7   0.0  0.072990  0.079777  0.082329  0.084164  0.085172  0.079715  0.079958   
+8   0.0  0.072802  0.079892  0.082196  0.084212  0.085382  0.079597  0.079964   
+9   0.0  0.072527  0.079854  0.081937  0.084110  0.085425  0.079353  0.079836   
+10  0.0  0.064985  0.070571  0.075171  0.076586  0.077304  0.074181  0.074265   
+11  0.0  0.064627  0.070400  0.074809  0.076355  0.077194  0.073842  0.074026   
+12  0.0  0.064245  0.070170  0.074403  0.076066  0.077017  0.073458  0.073736   
+13  0.0  0.063864  0.069919  0.073987  0.075758  0.076814  0.073063  0.073431   
+14  0.0  0.063500  0.069672  0.073582  0.075454  0.076612  0.072676  0.073131   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.068743  0.066200  0.065631  0.063225  0.060971  0.058856  0.056870  
-2   0.067765  0.065559  0.064814  0.062696  0.060691  0.058793  0.056996  
-3   0.076678  0.074301  0.074306  0.071960  0.069733  0.067619  0.065613  
-4   0.075294  0.073154  0.072976  0.070845  0.068809  0.066865  0.065012  
-5   0.073824  0.071901  0.071564  0.069629  0.067769  0.065984  0.064275  
-6   0.074307  0.072338  0.072718  0.070705  0.068771  0.066919  0.065147  
-7   0.072650  0.070865  0.071080  0.069239  0.067462  0.065753  0.064113  
-8   0.071008  0.069391  0.069456  0.067774  0.066143  0.064568  0.063051  
-9   0.069391  0.067929  0.067859  0.066323  0.064827  0.063378  0.061977  
-10  0.069456  0.067859  0.068437  0.066752  0.065119  0.063542  0.062023  
-11  0.067774  0.066323  0.066752  0.065207  0.063705  0.062250  0.060845  
-12  0.066143  0.064827  0.065119  0.063705  0.062325  0.060983  0.059685  
-13  0.064568  0.063378  0.063542  0.062250  0.060983  0.059749  0.058550  
-14  0.063051  0.061977  0.062023  0.060845  0.059685  0.058550  0.057446  
+1   0.072802  0.072527  0.064985  0.064627  0.064245  0.063864  0.063500  
+2   0.079892  0.079854  0.070571  0.070400  0.070170  0.069919  0.069672  
+3   0.082196  0.081937  0.075171  0.074809  0.074403  0.073987  0.073582  
+4   0.084212  0.084110  0.076586  0.076355  0.076066  0.075758  0.075454  
+5   0.085382  0.085425  0.077304  0.077194  0.077017  0.076814  0.076612  
+6   0.079597  0.079353  0.074181  0.073842  0.073458  0.073063  0.072676  
+7   0.079964  0.079836  0.074265  0.074026  0.073736  0.073431  0.073131  
+8   0.080086  0.080069  0.074152  0.074008  0.073810  0.073592  0.073378  
+9   0.080069  0.080157  0.073929  0.073876  0.073766  0.073635  0.073504  
+10  0.074152  0.073929  0.070129  0.069821  0.069475  0.069119  0.068771  
+11  0.074008  0.073876  0.069821  0.069595  0.069327  0.069048  0.068774  
+12  0.073810  0.073766  0.069475  0.069327  0.069136  0.068931  0.068731  
+13  0.073592  0.073635  0.069119  0.069048  0.068931  0.068800  0.068671  
+14  0.073378  0.073504  0.068771  0.068774  0.068731  0.068671  0.068612  
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html
index 33b58b349..fe1b77db3 100644
--- a/doc/LectureNotes/_build/html/chapter3.html
+++ b/doc/LectureNotes/_build/html/chapter3.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -879,10 +889,10 @@ number \(i\) is left out. Usin
 
    
 
    
 
    
-
    Runtime: 0.134565 sec
+
    Runtime: 0.155664 sec
 Jackknife Statistics :
 original           bias      std. error
-   100.2         100.19        0.146591
+ 99.9688        99.9588         0.15043
 
    
 
    
 
    
@@ -1101,7 +1111,7 @@ theorem.
    
 
    
 
    Bootstrap Statistics :
 original           bias      std. error
- 99.9919  15.0954        99.9924        0.150989
+ 100.132  14.8115        100.132        0.147896
 
    
 
    
 
    
@@ -1345,14 +1355,14 @@ Error: 0.017355848195593312
 Bias^2: 0.010331721306655165
 Var: 0.007024126888938144
 0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331
-Polynomial degree: 9
+
    
+
    
+
    Polynomial degree: 9
 Error: 0.026605727637184558
 Bias^2: 0.010018312644139219
 Var: 0.016587414993045335
 0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554
-
    
-
    
-
    Polynomial degree: 10
+Polynomial degree: 10
 Error: 0.021592704588021178
 Bias^2: 0.010516485576646504
 Var: 0.01107621901137467
@@ -1670,12 +1680,12 @@ Mean squared error on test data: 128664.31650694
 Degree of polynomial:  26
 Mean squared error on training data: 0.00076905
 Mean squared error on test data: 19003.94822514
-
    
-
    
-
    Degree of polynomial:  27
+Degree of polynomial:  27
 Mean squared error on training data: 0.00068946
 Mean squared error on test data: 2379.66219404
-Degree of polynomial:  28
+
    
+
    
+
    Degree of polynomial:  28
 Mean squared error on training data: 0.00062595
 Mean squared error on test data: 4082.19983530
 Degree of polynomial:  29
@@ -1683,9 +1693,9 @@ Mean squared error on training data: 0.00060705
 Mean squared error on test data: 3250.17647619
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -1919,7 +1929,7 @@ cross-validation (LOOCV).
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
@@ -2808,9 +2818,9 @@ linear system as an equation would reduce this down to
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2954,9 +2964,9 @@ with the form utilized in linear regression, viz.
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2996,9 +3006,9 @@ cost function is given by
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3033,9 +3043,9 @@ cost function is given by
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3088,43 +3098,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
 
    
 
    
 
    
-
      0%|                                                                                                                                                         | 0/10 [00:00<?, ?it/s]
+
      0%|                                                                                                                                                                   | 0/10 [00:00<?, ?it/s]
 
    
 
    
 
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: 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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@@ -3271,9 +3281,9 @@ which polynomial fits the data best.
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection='3d')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   fig.colorbar(surf, shrink=0.5, aspect=5)
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index 140df44e6..c013569de 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html
index 222df26c1..23dddd0e7 100644
--- a/doc/LectureNotes/_build/html/chapter5.html
+++ b/doc/LectureNotes/_build/html/chapter5.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index ca0987d0a..8f053107e 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -807,9 +817,9 @@ predicting the target features of query instances is as follows:
    
 
    
 
    
 
    2nd degree coefficients:
-zero power:  2.731441119315968
-first power:  -0.07208896238192342
-second power:  0.0005051756404139333
+zero power:  0.9887034589972739
+first power:  -0.10518426027535331
+second power:  0.0005840075008020406
 
    
 
    
 _images/chapter6_1_1.png
diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html
index 3f0c4ee90..cb57e52f3 100644
--- a/doc/LectureNotes/_build/html/chapter7.html
+++ b/doc/LectureNotes/_build/html/chapter7.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 20466c93c..2ed547447 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -761,10 +771,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.04570437990371566
-4.420442688206847
-[[ 1.01597952  3.06059304]
- [ 3.06059304 10.1387933 ]]
+
    -0.11743722141098414
+3.5452708224046345
+[[ 1.27880068  3.85600299]
+ [ 3.85600299 12.61955303]]
 
    
 
    
 
    
@@ -804,10 +814,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.07663067400487368
-1.9423652864980914
-[[1.         0.72782592]
- [0.72782592 1.        ]]
+
    0.07178264457746288
+1.6714298027296224
+[[1.         0.59987612]
+ [0.59987612 1.        ]]
 
    
 
    
 
    
@@ -836,30 +846,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[-0.51761523 -1.42486342]
- [ 1.91816586  6.87585634]
- [-0.34694145 -1.09920915]
- [ 0.31244861  1.08282867]
- [ 1.12441319  3.24411906]
- [-0.51892347 -1.08417181]
- [-0.54509921 -2.1148557 ]
- [ 0.26084008  0.60846694]
- [ 0.01029574  0.21049575]
- [-1.69758412 -6.29866668]]
+
    [[-1.05589275 -2.32845846]
+ [-1.43650129 -5.0020496 ]
+ [ 0.30685269 -0.25002882]
+ [ 1.1511986   3.95940231]
+ [-0.84931504 -2.84538739]
+ [-0.63401971 -1.90876452]
+ [ 0.39256409  1.76775004]
+ [ 1.07828283  3.52988562]
+ [-0.18753987  0.14133772]
+ [ 1.23437046  2.9363131 ]]
           0         1
-0 -0.517615 -1.424863
-1  1.918166  6.875856
-2 -0.346941 -1.099209
-3  0.312449  1.082829
-4  1.124413  3.244119
-5 -0.518923 -1.084172
-6 -0.545099 -2.114856
-7  0.260840  0.608467
-8  0.010296  0.210496
-9 -1.697584 -6.298667
+0 -1.055893 -2.328458
+1 -1.436501 -5.002050
+2  0.306853 -0.250029
+3  1.151199  3.959402
+4 -0.849315 -2.845387
+5 -0.634020 -1.908765
+6  0.392564  1.767750
+7  1.078283  3.529886
+8 -0.187540  0.141338
+9  1.234370  2.936313
           0         1
-0  1.000000  0.993148
-1  0.993148  1.000000
+0  1.000000  0.972745
+1  0.972745  1.000000
 
    
 
    
 
    
@@ -916,37 +926,37 @@ 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  0.079243  0.085251  0.080541  0.083416  0.086394  0.074010  0.075867   
-2   0.0  0.085251  0.093570  0.084995  0.089008  0.093218  0.076658  0.079150   
-3   0.0  0.080541  0.084995  0.088240  0.090361  0.092452  0.084878  0.086337   
-4   0.0  0.083416  0.089008  0.090361  0.093080  0.095821  0.086090  0.087899   
-5   0.0  0.086394  0.093218  0.092452  0.095821  0.099275  0.087175  0.089365   
-6   0.0  0.074010  0.076658  0.084878  0.086090  0.087175  0.084042  0.084968   
-7   0.0  0.075867  0.079150  0.086337  0.087899  0.089365  0.084968  0.086108   
-8   0.0  0.077847  0.081832  0.087845  0.089793  0.091685  0.085877  0.087254   
-9   0.0  0.079975  0.084740  0.089414  0.091796  0.094163  0.086774  0.088416   
-10  0.0  0.067272  0.068624  0.079437  0.079971  0.080322  0.080193  0.080702   
-11  0.0  0.068609  0.070338  0.080572  0.081322  0.081908  0.081000  0.081647   
-12  0.0  0.070043  0.072194  0.081762  0.082754  0.083604  0.081821  0.082621   
-13  0.0  0.071587  0.074211  0.083015  0.084278  0.085427  0.082657  0.083630   
-14  0.0  0.073256  0.076413  0.084340  0.085908  0.087393  0.083511  0.084678   
+1   0.0  0.093993  0.092566  0.095420  0.093559  0.091714  0.087802  0.086054   
+2   0.0  0.092566  0.091571  0.094206  0.092560  0.090919  0.086830  0.085223   
+3   0.0  0.095420  0.094206  0.103273  0.101409  0.099552  0.098879  0.097049   
+4   0.0  0.093559  0.092560  0.101409  0.099701  0.097995  0.097238  0.095528   
+5   0.0  0.091714  0.090919  0.099552  0.097995  0.096434  0.095596  0.094001   
+6   0.0  0.087802  0.086830  0.098879  0.097238  0.095596  0.097294  0.095624   
+7   0.0  0.086054  0.085223  0.097049  0.095528  0.094001  0.095624  0.094050   
+8   0.0  0.084365  0.083669  0.095273  0.093866  0.092450  0.093996  0.092516   
+9   0.0  0.082734  0.082168  0.093551  0.092254  0.090945  0.092412  0.091021   
+10  0.0  0.079914  0.079165  0.092493  0.091090  0.089678  0.092852  0.091372   
+11  0.0  0.078377  0.077731  0.090832  0.089523  0.088202  0.091293  0.089893   
+12  0.0  0.076897  0.076349  0.089227  0.088007  0.086773  0.089781  0.088456   
+13  0.0  0.075471  0.075017  0.087674  0.086540  0.085390  0.088314  0.087062   
+14  0.0  0.074096  0.073734  0.086172  0.085121  0.084051  0.086891  0.085709   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.077847  0.079975  0.067272  0.068609  0.070043  0.071587  0.073256  
-2   0.081832  0.084740  0.068624  0.070338  0.072194  0.074211  0.076413  
-3   0.087845  0.089414  0.079437  0.080572  0.081762  0.083015  0.084340  
-4   0.089793  0.091796  0.079971  0.081322  0.082754  0.084278  0.085908  
-5   0.091685  0.094163  0.080322  0.081908  0.083604  0.085427  0.087393  
-6   0.085877  0.086774  0.080193  0.081000  0.081821  0.082657  0.083511  
-7   0.087254  0.088416  0.080702  0.081647  0.082621  0.083630  0.084678  
-8   0.088665  0.090123  0.081150  0.082248  0.083393  0.084594  0.085858  
-9   0.090123  0.091913  0.081538  0.082805  0.084141  0.085557  0.087063  
-10  0.081150  0.081538  0.077571  0.078106  0.078624  0.079125  0.079606  
-11  0.082248  0.082805  0.078106  0.078732  0.079353  0.079969  0.080577  
-12  0.083393  0.084141  0.078624  0.079353  0.080089  0.080832  0.081584  
-13  0.084594  0.085557  0.079125  0.079969  0.080832  0.081718  0.082632  
-14  0.085858  0.087063  0.079606  0.080577  0.081584  0.082632  0.083726  
+1   0.084365  0.082734  0.079914  0.078377  0.076897  0.075471  0.074096  
+2   0.083669  0.082168  0.079165  0.077731  0.076349  0.075017  0.073734  
+3   0.095273  0.093551  0.092493  0.090832  0.089227  0.087674  0.086172  
+4   0.093866  0.092254  0.091090  0.089523  0.088007  0.086540  0.085121  
+5   0.092450  0.090945  0.089678  0.088202  0.086773  0.085390  0.084051  
+6   0.093996  0.092412  0.092852  0.091293  0.089781  0.088314  0.086891  
+7   0.092516  0.091021  0.091372  0.089893  0.088456  0.087062  0.085709  
+8   0.091072  0.089664  0.089925  0.088521  0.087159  0.085835  0.084550  
+9   0.089664  0.088339  0.088510  0.087180  0.085888  0.084633  0.083414  
+10  0.089925  0.088510  0.089979  0.088563  0.087184  0.085842  0.084536  
+11  0.088521  0.087180  0.088563  0.087212  0.085898  0.084617  0.083371  
+12  0.087159  0.085888  0.087184  0.085898  0.084644  0.083423  0.082234  
+13  0.085835  0.084633  0.085842  0.084617  0.083423  0.082260  0.081126  
+14  0.084550  0.083414  0.084536  0.083371  0.082234  0.081126  0.080045  
 
    
 
    
 
    
@@ -1135,10 +1145,10 @@ We can write our own code or simply use either the functionaly of numpy<
 
    
 
    
 
              0         1
-0  3.987648  2.034723
-1  2.034723  2.038727
-[[3.98764765 2.03472297]
- [2.03472297 2.03872663]]
+0  4.068439  2.030371
+1  2.030371  2.006046
+[[4.06843936 2.03037095]
+ [2.03037095 2.00604596]]
 
    
 
    
 
    
@@ -1165,8 +1175,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 
    
 
    
 
    Centered covariance using own code
-[[3.98764765 2.03472297]
- [2.03472297 2.03872663]]
+[[4.06843936 2.03037095]
+ [2.03037095 2.00604596]]
 
    
 
    
 _images/chapter8_65_1.png
@@ -1226,16 +1236,16 @@ questions.
    
 
    
 
    
 
    Eigenvalues of Covariance matrix
-5.269217029290255
-0.7571572558830478
+5.314471861842257
+0.7600134536106469
 First eigenvector
-[0.84614892 0.53294653]
+[0.8522997  0.52305374]
 Second eigenvector
-[-0.53294653  0.84614892]
+[-0.52305374  0.8522997 ]
 
    
 
    
 
    Eigenvector of largest eigenvalue
-[-0.84614892 -0.53294653]
+[0.8522997  0.52305374]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html
index 09852dd6e..3526d1ea2 100644
--- a/doc/LectureNotes/_build/html/chapter9.html
+++ b/doc/LectureNotes/_build/html/chapter9.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html
index 7a957deb6..a9c376442 100644
--- a/doc/LectureNotes/_build/html/chapteroptimization.html
+++ b/doc/LectureNotes/_build/html/chapteroptimization.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1057,11 +1067,11 @@ which equals
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11016/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8738/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 
    
 
    
-
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x122d2e790>
+
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x12b1c2700>
 
    
 
    
 _images/chapteroptimization_61_2.png
@@ -1119,7 +1129,7 @@ which equals
    
 
    
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x12334c310>]
+
    [<matplotlib.lines.Line2D at 0x12e9e6280>]
 
    
 
    
 _images/chapteroptimization_69_1.png
@@ -1376,11 +1386,11 @@ when \(||\nabla_\beta C(\beta_k) || \
 
    
 
    
 
    
-
    [0.27637358 4.69167569]
-[[4.08692465]
- [2.84462849]]
-[[4.08692465]
- [2.84462849]]
+
    [0.33015882 4.55906894]
+[[4.11836068]
+ [2.85949635]]
+[[4.11836068]
+ [2.85949635]]
 
    
 
    
 _images/chapteroptimization_123_1.png
@@ -1409,9 +1419,9 @@ when \(||\nabla_\beta C(\beta_k) || \
 
    
 
    
 
    
-
    [[4.17086577]
- [2.92317667]]
-[4.14538257] [2.90670236]
+
    [[4.13451895]
+ [2.8383548 ]]
+[4.19783086] [2.93535577]
 
    
 
    
 
    
@@ -1482,10 +1492,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
 
    
 
    
 
    
-
    [[4.11027723]
- [2.92805329]]
-[[4.04931542]
- [2.97504526]]
+
    [[4.12575322]
+ [2.87918262]]
+[[4.08876865]
+ [2.90510842]]
 
    
 
    
 _images/chapteroptimization_132_1.png
@@ -1735,15 +1745,15 @@ function.
    
 
    
 
    
 
    Own inversion
-[[3.22532324]
- [3.44210664]]
-Eigenvalues of Hessian Matrix:[0.30012384 4.62464344]
+[[3.78515112]
+ [3.19029687]]
+Eigenvalues of Hessian Matrix:[0.30739146 4.15768662]
 theta from own gd
-[[3.22532324]
- [3.44210664]]
+[[3.78515112]
+ [3.19029687]]
 theta from own sdg
-[[3.17736035]
- [3.48289037]]
+[[3.81425332]
+ [3.25285802]]
 
    
 
    
 _images/chapteroptimization_148_1.png
diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html
index 508961051..f12eae21b 100644
--- a/doc/LectureNotes/_build/html/clustering.html
+++ b/doc/LectureNotes/_build/html/clustering.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html
index 0da71c964..4c7a11b17 100644
--- a/doc/LectureNotes/_build/html/exercisesweek34.html
+++ b/doc/LectureNotes/_build/html/exercisesweek34.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek35.html b/doc/LectureNotes/_build/html/exercisesweek35.html
index f3eed704e..77af0ea9f 100644
--- a/doc/LectureNotes/_build/html/exercisesweek35.html
+++ b/doc/LectureNotes/_build/html/exercisesweek35.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek36.html b/doc/LectureNotes/_build/html/exercisesweek36.html
index cb5be9132..3d4875d2d 100644
--- a/doc/LectureNotes/_build/html/exercisesweek36.html
+++ b/doc/LectureNotes/_build/html/exercisesweek36.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek37.html b/doc/LectureNotes/_build/html/exercisesweek37.html
index dc866b9fd..b3e443f89 100644
--- a/doc/LectureNotes/_build/html/exercisesweek37.html
+++ b/doc/LectureNotes/_build/html/exercisesweek37.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek38.html b/doc/LectureNotes/_build/html/exercisesweek38.html
index 8aecc16b5..a79427044 100644
--- a/doc/LectureNotes/_build/html/exercisesweek38.html
+++ b/doc/LectureNotes/_build/html/exercisesweek38.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek39.html b/doc/LectureNotes/_build/html/exercisesweek39.html
index 417b35c92..27ee7bf03 100644
--- a/doc/LectureNotes/_build/html/exercisesweek39.html
+++ b/doc/LectureNotes/_build/html/exercisesweek39.html
@@ -341,6 +341,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek41.html b/doc/LectureNotes/_build/html/exercisesweek41.html
index 34c086bfd..5bfd5df19 100644
--- a/doc/LectureNotes/_build/html/exercisesweek41.html
+++ b/doc/LectureNotes/_build/html/exercisesweek41.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -814,15 +824,15 @@ regression.
    
 
    
 
    
 
    Own inversion
-[[3.97739698]
- [2.9189726 ]]
-Eigenvalues of Hessian Matrix:[0.27987128 4.51549827]
+[[3.69887085]
+ [3.34977681]]
+Eigenvalues of Hessian Matrix:[0.25923926 4.67193435]
 theta from own gd
-[[3.97739698]
- [2.9189726 ]]
+[[3.69887085]
+ [3.34977681]]
 theta from own sdg
-[[3.9577228 ]
- [2.91473433]]
+[[3.63685221]
+ [3.37369014]]
 
    
 
    
 _images/exercisesweek41_5_1.png
@@ -944,14 +954,14 @@ first example shows results with ordinary leats squares.
    
 
    
 
    
 
    Own inversion
-[[4.32133765]
- [2.59905073]]
-Eigenvalues of Hessian Matrix:[0.29426584 4.28858038]
+[[3.64342603]
+ [3.30485583]]
+Eigenvalues of Hessian Matrix:[0.29030069 4.6608358 ]
 
    
 
    
 
    theta from own gd
-[[4.32133765]
- [2.59905073]]
+[[3.64342603]
+ [3.30485583]]
 
    
 
    
 _images/exercisesweek41_16_2.png
@@ -1022,73 +1032,73 @@ Eigenvalues of Hessian Matrix:[0.29426584 4.28858038]
 
    Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.30311767 4.03556032]
-0 [-14.28624958] [-15.54071847]
-1 [-0.04900086] [0.04473913]
-2 [-0.04532032] [0.0413787]
-3 [-0.04191624] [0.03827068]
-4 [-0.03876784] [0.0353961]
-5 [-0.03585592] [0.03273744]
-6 [-0.03316272] [0.03027848]
-7 [-0.03067182] [0.02800421]
-8 [-0.02836801] [0.02590077]
-9 [-0.02623724] [0.02395532]
-10 [-0.02426651] [0.022156]
-11 [-0.02244382] [0.02049182]
-12 [-0.02075802] [0.01895265]
-13 [-0.01919885] [0.01752908]
-14 [-0.0177568] [0.01621244]
-15 [-0.01642305] [0.0149947]
-16 [-0.01518949] [0.01386842]
-17 [-0.01404858] [0.01282674]
-18 [-0.01299337] [0.0118633]
-19 [-0.01201742] [0.01097223]
-20 [-0.01111477] [0.01014809]
-21 [-0.01027992] [0.00938585]
-22 [-0.00950778] [0.00868086]
-23 [-0.00879363] [0.00802883]
-24 [-0.00813313] [0.00742577]
-25 [-0.00752224] [0.00686801]
-26 [-0.00695723] [0.00635214]
-27 [-0.00643466] [0.00587502]
-28 [-0.00595134] [0.00543374]
-29 [-0.00550433] [0.0050256]
+Eigenvalues of Hessian Matrix:[0.27335131 4.46000649]
+0 [-13.27099835] [-15.86570776]
+1 [0.01163425] [-0.00974702]
+2 [0.01092119] [-0.00914964]
+3 [0.01025184] [-0.00858886]
+4 [0.00962351] [-0.00806245]
+5 [0.00903369] [-0.00756831]
+6 [0.00848002] [-0.00710445]
+7 [0.00796028] [-0.00666902]
+8 [0.0074724] [-0.00626028]
+9 [0.00701442] [-0.00587659]
+10 [0.00658451] [-0.00551642]
+11 [0.00618095] [-0.00517832]
+12 [0.00580212] [-0.00486095]
+13 [0.00544651] [-0.00456302]
+14 [0.0051127] [-0.00428336]
+15 [0.00479935] [-0.00402083]
+16 [0.0045052] [-0.0037744]
+17 [0.00422908] [-0.00354307]
+18 [0.00396988] [-0.00332591]
+19 [0.00372657] [-0.00312207]
+20 [0.00349817] [-0.00293072]
+21 [0.00328377] [-0.0027511]
+22 [0.00308251] [-0.00258249]
+23 [0.00289358] [-0.00242421]
+24 [0.00271624] [-0.00227563]
+25 [0.00254976] [-0.00213616]
+26 [0.00239349] [-0.00200523]
+27 [0.00224679] [-0.00188233]
+28 [0.00210909] [-0.00176697]
+29 [0.00197982] [-0.00165867]
 theta from own gd
-[[3.98320492]
- [3.01533437]]
-0 [-0.00509089] [0.00464812]
-1 [-0.0047085] [0.00429899]
-2 [-0.00424012] [0.00387135]
-3 [-0.00378113] [0.00345227]
-4 [-0.00335942] [0.00306724]
-5 [-0.00298058] [0.00272135]
-6 [-0.00264305] [0.00241318]
-7 [-0.00234327] [0.00213947]
-8 [-0.00207732] [0.00189665]
-9 [-0.00184151] [0.00168135]
-10 [-0.00163245] [0.00149047]
-11 [-0.00144711] [0.00132125]
-12 [-0.00128282] [0.00117125]
-13 [-0.00113717] [0.00103827]
-14 [-0.00100807] [0.00092039]
-15 [-0.00089362] [0.0008159]
-16 [-0.00079216] [0.00072326]
-17 [-0.00070222] [0.00064115]
-18 [-0.0006225] [0.00056836]
-19 [-0.00055182] [0.00050383]
-20 [-0.00048917] [0.00044663]
-21 [-0.00043363] [0.00039592]
-22 [-0.0003844] [0.00035097]
-23 [-0.00034076] [0.00031112]
-24 [-0.00030207] [0.0002758]
-25 [-0.00026778] [0.00024449]
-26 [-0.00023737] [0.00021673]
-27 [-0.00021042] [0.00019212]
-28 [-0.00018653] [0.00017031]
-29 [-0.00016536] [0.00015097]
+[[4.00679887]
+ [2.994304  ]]
+0 [0.00185848] [-0.00155701]
+1 [0.00174457] [-0.00146158]
+2 [0.00160348] [-0.00134337]
+3 [0.00146287] [-0.00122558]
+4 [0.00133103] [-0.00111512]
+5 [0.0012099] [-0.00101364]
+6 [0.00109941] [-0.00092107]
+7 [0.00099888] [-0.00083685]
+8 [0.0009075] [-0.00076029]
+9 [0.00082447] [-0.00069073]
+10 [0.00074902] [-0.00062752]
+11 [0.00068048] [-0.0005701]
+12 [0.00061822] [-0.00051793]
+13 [0.00056165] [-0.00047054]
+14 [0.00051025] [-0.00042748]
+15 [0.00046356] [-0.00038836]
+16 [0.00042114] [-0.00035283]
+17 [0.0003826] [-0.00032054]
+18 [0.00034759] [-0.00029121]
+19 [0.00031579] [-0.00026456]
+20 [0.00028689] [-0.00024035]
+21 [0.00026064] [-0.00021836]
+22 [0.00023679] [-0.00019838]
+23 [0.00021512] [-0.00018022]
+24 [0.00019544] [-0.00016373]
+25 [0.00017755] [-0.00014875]
+26 [0.0001613] [-0.00013514]
+27 [0.00014654] [-0.00012277]
+28 [0.00013313] [-0.00011154]
+29 [0.00012095] [-0.00010133]
 theta from own gd wth momentum
-[[3.99951642]
- [3.00044152]]
+[[4.00040199]
+ [2.99966322]]
 
    
 
    
 
    
@@ -1141,17 +1151,17 @@ theta from own gd wth momentum
 
    
 
    
 
    Own inversion
-[[3.66959644]
- [3.26513904]]
-Eigenvalues of Hessian Matrix:[0.33285444 4.11450263]
-0 [-12.48534921] [-14.7906583]
-1 [-1.09712586e-14] [-5.19623863e-15]
-2 [-1.27068495e-16] [-2.9424428e-16]
-3 [5.91540705e-16] [7.2837521e-16]
-4 [-1.27068495e-16] [-2.9424428e-16]
+[[3.71369789]
+ [3.2314999 ]]
+Eigenvalues of Hessian Matrix:[0.30237154 4.4642383 ]
+0 [-17.75091492] [-21.33108943]
+1 [-4.60742555e-15] [5.64228618e-16]
+2 [-5.34294831e-16] [-5.79981535e-16]
+3 [-5.34294831e-16] [-5.79981535e-16]
+4 [-5.34294831e-16] [-5.79981535e-16]
 beta from own Newton code
-[[3.66959644]
- [3.26513904]]
+[[3.71369789]
+ [3.2314999 ]]
 
    
 
    
 
    
@@ -1240,18 +1250,20 @@ beta from own Newton code
 
    
 
    
 
    Own inversion
-[[3.68184997]
- [3.32507975]]
-Eigenvalues of Hessian Matrix:[0.26370919 4.62518501]
-theta from own gd
-[[3.68184997]
- [3.32507975]]
+[[4.39917327]
+ [2.69542733]]
+Eigenvalues of Hessian Matrix:[0.29765192 4.0375827 ]
 
    
 
    
-_images/exercisesweek41_22_1.png
+
    theta from own gd
+[[4.39917327]
+ [2.69542733]]
+
    
+
    
+_images/exercisesweek41_22_2.png
 
    theta from own sdg
-[[3.68809785]
- [3.32032017]]
+[[4.32234998]
+ [2.64530585]]
 
    
 
    
 
    
@@ -1333,15 +1345,17 @@ theta from own gd
 
    
 
    
 
    Own inversion
-[[3.55555773]
- [3.41891092]]
-Eigenvalues of Hessian Matrix:[0.30326262 4.34133193]
+[[3.7635689 ]
+ [3.10080981]]
+Eigenvalues of Hessian Matrix:[0.31633433 3.9824638 ]
 theta from own gd
-[[3.55511609]
- [3.41928689]]
-theta from own sdg with momentum
-[[3.58207928]
- [3.37895549]]
+[[3.76366462]
+ [3.1007216 ]]
+
    
+
    
+
    theta from own sdg with momentum
+[[3.75707243]
+ [3.12422141]]
 
    
 
    
 
    
@@ -1416,9 +1430,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own AdaGrad
-[[2.00039962]
- [2.99772199]
- [4.00233436]]
+[[1.99999956]
+ [3.00000215]
+ [3.99999797]]
 
    
 
    
 
    
@@ -1500,9 +1514,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own RMSprop
-[[1.99858411]
- [2.9981377 ]
- [3.99861427]]
+[[1.99907985]
+ [2.99897733]
+ [3.99754609]]
 
    
 
    
 
    
@@ -1588,9 +1602,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own ADAM
-[[2.00002678]
- [2.99985103]
- [4.00014662]]
+[[2.00003617]
+ [2.99986253]
+ [4.00012569]]
 
    
 
    
 
    
@@ -1663,7 +1677,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
   return asarray(x, dtype=self.dtype)
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x1262b9a90>]
+
    [<matplotlib.lines.Line2D at 0x11cb23a60>]
 
    
 
    
 _images/exercisesweek41_39_2.png
@@ -1702,7 +1716,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
   return asarray(x, dtype=self.dtype)
 
    
 
    
-
    <matplotlib.collections.PathCollection at 0x126323b50>
+
    <matplotlib.collections.PathCollection at 0x11cb23fd0>
 
    
 
    
 _images/exercisesweek41_41_2.png
diff --git a/doc/LectureNotes/_build/html/exercisesweek42.html b/doc/LectureNotes/_build/html/exercisesweek42.html
index cbcf79ed3..87083cf43 100644
--- a/doc/LectureNotes/_build/html/exercisesweek42.html
+++ b/doc/LectureNotes/_build/html/exercisesweek42.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek43.html b/doc/LectureNotes/_build/html/exercisesweek43.html
index b1030881b..4d4eb3514 100644
--- a/doc/LectureNotes/_build/html/exercisesweek43.html
+++ b/doc/LectureNotes/_build/html/exercisesweek43.html
@@ -931,8 +931,9 @@ Accuracy score on data set:  0.5
 Learning rate  =  1e-05
 Lambda =  1.0
 Accuracy score on data set:  0.5
-
-Learning rate  =  1e-05
+
    
+
    
+
    Learning rate  =  1e-05
 Lambda =  10.0
 Accuracy score on data set:  0.5
 
@@ -1035,6 +1036,75 @@ Accuracy score on data set:  1.0
 Learning rate  =  0.1
 Lambda =  0.01
 Accuracy score on data set:  1.0
+
+Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on data set:  0.5
+
+Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on data set:  0.75
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+Learning rate  =  1.0
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+Accuracy score on data set:  0.75
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+Learning rate  =  1.0
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+Accuracy score on data set:  0.75
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+Learning rate  =  1.0
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+Accuracy score on data set:  0.5
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+Learning rate  =  1.0
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+Accuracy score on data set:  0.5
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+Learning rate  =  1.0
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+Accuracy score on data set:  0.5
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+Learning rate  =  1.0
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+Accuracy score on data set:  0.5
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+Learning rate  =  10.0
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+Accuracy score on data set:  0.5
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+Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on data set:  0.5
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on data set:  0.5
 
    
 
    
 
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
@@ -1059,76 +1129,7 @@ Accuracy score on data set:  1.0
   warnings.warn(
 
    
 
    
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-Accuracy score on data set:  0.5
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-Learning rate  =  1.0
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-Accuracy score on data set:  0.5
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-Learning rate  =  10.0
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-Accuracy score on data set:  0.5
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-Learning rate  =  10.0
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-Accuracy score on data set:  0.5
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-Learning rate  =  10.0
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-Accuracy score on data set:  0.5
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-Learning rate  =  10.0
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-Learning rate  =  10.0
-Lambda =  10.0
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-
    
-_images/exercisesweek43_26_3.png
+_images/exercisesweek43_26_4.png
 
    
 
    
 
    
@@ -5433,8 +5434,9 @@ case.
    
 
    
 
    
 
    Adam: Eta=0.001, Lambda=0
-
-  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
+
    
+
    
+
      [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
 
    
 
    
 
      [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 01c2390fa..973fd087a 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -663,9 +673,8 @@ matrices and vectors.
    
 
    
 
    
 
    
-
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@@ -975,13 +974,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
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diff --git a/doc/LectureNotes/_build/html/project1.html b/doc/LectureNotes/_build/html/project1.html
index 8a063cbf5..b21f81330 100644
--- a/doc/LectureNotes/_build/html/project1.html
+++ b/doc/LectureNotes/_build/html/project1.html
@@ -56,7 +56,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -710,7 +720,7 @@ which polynomial fits the data best.
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11057/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8779/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection='3d')
 
    
 
    
@@ -1069,11 +1079,11 @@ of code developers and contributors keeps increasing.
    
             
                 
 
     
-    
+    
         
         
    
             
    previous
    
-            
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
    
+            
    Week 45,  Recurrent Neural Networks
    
         
    
         
     
diff --git a/doc/LectureNotes/_build/html/project2.html b/doc/LectureNotes/_build/html/project2.html
index 29936c86c..e2dd10093 100644
--- a/doc/LectureNotes/_build/html/project2.html
+++ b/doc/LectureNotes/_build/html/project2.html
@@ -342,6 +342,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
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index d8806a39c..5301df535 100644
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@@ -341,6 +341,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
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36,37,38,39,40,41,43],zeros_lik:4,zeroth:33,zfill:4,zip:[2,4,6,13,38],zm_h:[0,32],zmq:[],zn:[0,33],zone:[0,33],zoom:32,zx:[25,32],zy:[25,32],zz:[25,32]},titles:["3. 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","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","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: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural 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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","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","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: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural 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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 8fdb80c31..00c6a991d 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1035,27 +1045,27 @@ uncorrelated.
    
 
    
 
    
 
    
-
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- [ 5.28962017  3.69303559  8.24339513  4.83283826  4.18604968  3.90164278
-   2.1790289   5.43713337  5.14036907  7.8932215 ]
- [13.19870992  9.21489709 20.56899695 12.0589434  10.44507049  9.7354157
-   5.43713337 13.56678624 12.82629718 19.69524041]
- [12.47831084  8.71193859 19.44632008 11.40075395  9.87496787  9.20404676
-   5.14036907 12.82629718 12.12622478 18.62025406]
- [19.16089488 13.37749489 29.86052354 17.50626752 15.16336815 14.13314468
-   7.8932215  19.69524041 18.62025406 28.59206948]]
+
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+  14.58098325  8.09978307 10.32858131  7.04416475]
+ [22.38035637 20.23288045  5.83471014 20.81233249 20.95513615 12.47116132
+  13.18188532  7.32257967  9.33751667  6.36825174]
+ [ 6.45399416  5.83471014  1.68259989  6.00181115  6.04299253  3.59640396
+   3.80136087  2.11166818  2.69273094  1.83646135]
+ [23.02131025 20.81233249  6.00181115 21.40837954 21.55527296 12.8283245
+  13.55940301  7.53229196  9.60493501  6.55063291]
+ [23.17927079 20.95513615  6.04299253 21.55527296 21.7031743  12.91634595
+  13.65244075  7.58397472  9.67083919  6.59558002]
+ [13.79482449 12.47116132  3.59640396 12.8283245  12.91634595  7.6869858
+   8.12506251  4.51349834  5.75546705  3.92526882]
+ [14.58098325 13.18188532  3.80136087 13.55940301 13.65244075  8.12506251
+   8.58810494  4.7707199   6.08346766  4.14896753]
+ [ 8.09978307  7.32257967  2.11166818  7.53229196  7.58397472  4.51349834
+   4.7707199   2.65015024  3.37938584  2.3047648 ]
+ [10.32858131  9.33751667  2.69273094  9.60493501  9.67083919  5.75546705
+   6.08346766  3.37938584  4.30928349  2.9389615 ]
+ [ 7.04416475  6.36825174  1.83646135  6.55063291  6.59558002  3.92526882
+   4.14896753  2.3047648   2.9389615   2.00439232]]
 
    
 
    
 
    
@@ -1323,15 +1333,15 @@ more practically oriented methods like the blocking technique.
    
 
    
 
    
 
    
-
    0.035513525941656535
-4.0516821246279795
--0.00822879725131466
-1.0340060287164625 10.447659635275407 10.449001126081919
-3.1285896350792584 2.5721905504656455 7.8028954343792245
-[[ 1.03400603  3.12858964  2.57219055]
- [ 3.12858964 10.44765964  7.80289543]
- [ 2.57219055  7.80289543 10.44900113]]
-[19.14871402  0.07942491  2.70252786]
+
    0.07023654656164897
+4.208190393562401
+-0.023810076900619058
+1.0331134070762626 10.076560707521647 14.512204707520711
+3.055706923889776 3.0768224464930487 8.922010623244745
+[[ 1.03311341  3.05570692  3.07682245]
+ [ 3.05570692 10.07656071  8.92201062]
+ [ 3.07682245  8.92201062 14.51220471]]
+[22.35796655  0.08015655  3.18375572]
 
    
 
    
 
    
@@ -1661,7 +1671,7 @@ assumption for approximating \(\sigma
 
    
 
    
 
    
-
    -0.024318244280276506 1.0399587275832265
+
    -0.006719367598355617 1.0020717457079393
 
    
 
    
 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html
index e6f1a125d..59454a901 100644
--- a/doc/LectureNotes/_build/html/teachers.html
+++ b/doc/LectureNotes/_build/html/teachers.html
@@ -341,6 +341,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html
index 9503ec8cc..bce2f5a5f 100644
--- a/doc/LectureNotes/_build/html/textbooks.html
+++ b/doc/LectureNotes/_build/html/textbooks.html
@@ -341,6 +341,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index c722c1557..4660c5828 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1825,8 +1835,8 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 
    
 
    
 
    
-
    [ 0.98502634 -1.56822376 -0.45668633  1.26063304 -1.43801947 -0.72264336
-  0.12428533 -0.64472123 -0.99439119  0.84257054]
+
    [ 1.303107    1.38160211  0.19790229  1.36099915 -2.08992459 -0.86156954
+  2.9012691  -0.18410452 -1.06886644  0.01011906]
 
    
 
    
 
    
@@ -2051,26 +2061,26 @@ lowercase letters for vectors and uppercase letters for matrices)
    
 
    
 
    
 
    
-
    [[0.36681298 0.62199022 0.32023229 0.08231145 0.09917246 0.29025302
-  0.85115237 0.79516409 0.833774   0.85910255]
- [0.08990571 0.56425249 0.34440086 0.37540613 0.30085673 0.93901621
-  0.00790262 0.92604308 0.7742213  0.58486384]
- [0.57811941 0.84268865 0.11339075 0.57329374 0.78094722 0.46156624
-  0.2545724  0.1095957  0.6559956  0.22291364]
- [0.12049203 0.50172141 0.38493367 0.62633359 0.13880371 0.37092452
-  0.21913628 0.78478186 0.12625715 0.89142357]
- [0.69018454 0.02200532 0.63281889 0.28622606 0.84900747 0.44440345
-  0.5302517  0.15957051 0.10154612 0.7025846 ]
- [0.26518597 0.48577692 0.68736603 0.10401756 0.88473534 0.31949465
-  0.00434364 0.20240089 0.46285399 0.64432571]
- [0.39229856 0.66289428 0.2591811  0.68871199 0.37021881 0.32041353
-  0.93049763 0.30690504 0.63212587 0.56397327]
- [0.31212802 0.82402448 0.94610136 0.19407473 0.28535441 0.99933329
-  0.55331574 0.96439516 0.4809676  0.21947455]
- [0.66489687 0.7613959  0.33285905 0.20726939 0.74587018 0.97375628
-  0.62642303 0.95762994 0.22169909 0.82717721]
- [0.8470287  0.16761991 0.74042291 0.42143986 0.70262543 0.8964059
-  0.69629255 0.05916189 0.89940861 0.19010909]]
+
    [[0.27204759 0.8509716  0.98844754 0.6522099  0.81868633 0.74934715
+  0.53632379 0.2257879  0.88228452 0.33213799]
+ [0.82550815 0.97032289 0.54852248 0.45000312 0.45227801 0.44437409
+  0.66608227 0.37871763 0.10320791 0.55812916]
+ [0.10455569 0.18044829 0.92543216 0.81873184 0.05924492 0.00620039
+  0.03106988 0.61631038 0.56830193 0.46580623]
+ [0.97991527 0.62460522 0.51635486 0.08776426 0.80418542 0.94035843
+  0.63870745 0.64347512 0.03019138 0.45811552]
+ [0.82050873 0.4757488  0.95696156 0.2970942  0.56217428 0.16487517
+  0.2522939  0.44886896 0.66951925 0.56135704]
+ [0.82178144 0.58639705 0.0944958  0.69585594 0.08191117 0.42487977
+  0.29009852 0.08368077 0.6691852  0.48444949]
+ [0.55790428 0.58519863 0.35795044 0.65925306 0.53506617 0.60090208
+  0.22830615 0.30506642 0.79142002 0.68029581]
+ [0.12867125 0.34373214 0.89558707 0.05629549 0.54342461 0.10888134
+  0.27485633 0.97326759 0.10477501 0.80621648]
+ [0.56756375 0.18276764 0.83519995 0.23044077 0.43559429 0.0934955
+  0.5712104  0.92054587 0.10120164 0.69666941]
+ [0.17456211 0.42323635 0.03955811 0.54969188 0.5793788  0.49423098
+  0.78834469 0.80312429 0.94756925 0.83793923]]
 
    
 
    
 
    
@@ -2125,13 +2135,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    -0.1804736801658276
-3.577421319924605
--0.13894606338836166
-[[ 0.93900613  3.09173024  2.71615146]
- [ 3.09173024 11.22689573  9.13261905]
- [ 2.71615146  9.13261905 12.06931309]]
-[21.60398689  0.07438088  2.55684718]
+
    -0.14482255345953607
+3.467427755242117
+-0.7005538702846336
+[[ 0.82032378  2.41772265  2.45808919]
+ [ 2.41772265  8.23849741  7.23077531]
+ [ 2.45808919  7.23077531 13.06343533]]
+[18.91591367  0.08817972  3.11816312]
 
    
 
    
 
    
@@ -2354,7 +2364,7 @@ Name: Aragorn, dtype: object
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11068/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8790/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
   data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index cb75d3083..3886be914 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1706,7 +1716,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.9955273625597437
+
    0.996738628265756
 
    
 
    
 
    
@@ -1723,7 +1733,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.008900933315885705
+
    0.00846262916105675
 
    
 
    
 
    
@@ -1738,23 +1748,31 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    [0.00643899 0.04246989 0.0607062  0.02997344 0.0011878  0.00123457
- 0.00324986 0.03285652 0.01028728 0.01571866 0.03940381 0.0814985
- 0.038844   0.01828593 0.03967758 0.00303693 0.02227466 0.02702328
- 0.00026861 0.00883798 0.02876697 0.00472251 0.03141454 0.02911162
- 0.02387339 0.00585113 0.00110716 0.00587564 0.00693821 0.00604105
- 0.00804985 0.02058094 0.01151984 0.01782721 0.0286851  0.08874631
- 0.01678538 0.0065912  0.03611471 0.02706508 0.00313125 0.04977093
- 0.00415289 0.02760079 0.00518122 0.00628874 0.00739489 0.01619456
- 0.00996972 0.02210753 0.02030107 0.02100763 0.04699527 0.01512934
- 0.00717079 0.01784714 0.01095703 0.01281486 0.0231703  0.04482932
- 0.00287871 0.0565419  0.04028659 0.03102525 0.01617722 0.0271761
- 0.01736502 0.03394827 0.00328494 0.05750876 0.0059888  0.01915888
- 0.01423609 0.01024227 0.03660869 0.01012951 0.00534938 0.03375068
- 0.02699539 0.04083439 0.04965227 0.00565625 0.02250553 0.00893027
- 0.02244755 0.00741987 0.00189101 0.02042476 0.02036545 0.06362348
- 0.03145163 0.02987833 0.07393685 0.0033575  0.02791218 0.00214832
- 0.0111154  0.01344581 0.00368581 0.01436601]
+
    [2.18321314e-02 4.63790586e-02 1.86599003e-02 2.57537966e-02
+ 7.75301638e-03 2.41708096e-03 2.05388549e-02 2.24797183e-02
+ 3.80669838e-02 1.77124395e-02 5.74437617e-02 8.91992985e-03
+ 3.79831624e-02 1.33444711e-02 2.46753261e-02 2.04450975e-02
+ 8.98180203e-02 1.21522960e-02 3.12747234e-03 1.31395784e-03
+ 1.84447599e-03 2.96525482e-03 6.28909679e-03 1.52006777e-02
+ 2.87135280e-03 2.40513177e-02 3.35417405e-02 5.92991719e-03
+ 3.54379087e-02 7.18303628e-03 1.54601264e-02 2.15148810e-02
+ 2.79754897e-03 4.12602928e-03 4.22227163e-02 3.09676156e-02
+ 1.25713219e-02 2.32108713e-02 2.44657526e-02 1.05066388e-02
+ 6.68324974e-02 2.97565845e-02 2.42484290e-02 1.89707309e-02
+ 2.19461919e-02 1.41644629e-02 1.41226929e-02 5.23396766e-03
+ 3.21530495e-03 3.66036618e-03 7.91408373e-03 3.18065689e-02
+ 5.10582403e-02 6.76220793e-03 3.09797549e-02 1.01612033e-02
+ 4.64257697e-02 1.98270777e-02 2.88088818e-02 5.94948363e-03
+ 8.94501598e-03 4.64365518e-03 4.55438359e-02 3.34347894e-03
+ 4.97761429e-03 2.71875845e-02 1.57316402e-02 4.15628391e-02
+ 4.75979803e-02 8.77079389e-03 3.22623101e-03 2.53596681e-03
+ 4.02206965e-02 3.06020683e-02 3.07080407e-02 9.75525377e-03
+ 6.45380691e-02 2.66174067e-02 1.94727053e-03 4.82766482e-03
+ 3.39313789e-03 5.00126617e-02 3.25794223e-02 3.97663980e-02
+ 3.51267283e-02 4.43226747e-02 4.45976616e-03 2.86750237e-02
+ 2.33197004e-02 9.78449688e-05 4.38688646e-02 2.86830766e-02
+ 2.90763970e-02 9.24053124e-03 1.36970119e-02 6.97177697e-02
+ 2.34728094e-02 2.31728952e-02 3.08484802e-03 6.25254477e-02]
 
    
 
    
 
    
@@ -1823,15 +1841,15 @@ but now splitting the data into a training set and a test set.
    
 
    
 
    
 
    
-
    [ 2.09851217 -1.48209629 10.27096183 -6.79998516  2.87206824]
+
    [ 1.97864285  0.28134042  4.70594499 -0.58368727  0.70917314]
 Training R2
-0.9957273060382023
+0.993658072083743
 Training MSE
-0.010053880703541525
+0.012874822204495243
 Test R2
-0.9888005551376943
+0.9945729062189713
 Test MSE
-0.008043926731954223
+0.007472516848671787
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index 681ce6e4a..44504217b 100644
--- a/doc/LectureNotes/_build/html/week36.html
+++ b/doc/LectureNotes/_build/html/week36.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index e822ee4a0..a743bc8c3 100644
--- a/doc/LectureNotes/_build/html/week37.html
+++ b/doc/LectureNotes/_build/html/week37.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1827,7 +1837,7 @@ theorem.
    
 
    
 
    Bootstrap Statistics :
 original           bias      std. error
- 99.9889  15.1336        99.9892        0.150749
+ 100.038  14.9172        100.039        0.150218
 
    
 
    
 
    
@@ -2089,14 +2099,14 @@ Error: 0.026605727637184558
 Bias^2: 0.010018312644139219
 Var: 0.016587414993045335
 0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554
-
    
-
    
-
    Polynomial degree: 10
+Polynomial degree: 10
 Error: 0.021592704588021178
 Bias^2: 0.010516485576646504
 Var: 0.01107621901137467
 0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174
-Polynomial degree: 11
+
    
+
    
+
    Polynomial degree: 11
 Error: 0.07160048164232538
 Bias^2: 0.014436800088896381
 Var: 0.05716368155342902
@@ -2106,14 +2116,16 @@ Error: 0.11547777218876518
 Bias^2: 0.016285782696017142
 Var: 0.09919198949274803
 0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518
-Polynomial degree: 13
+
    
+
    
+
    Polynomial degree: 13
 Error: 0.2284246870217162
 Bias^2: 0.01975416527168255
 Var: 0.20867052175003364
 0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162
 
    
 
    
-_images/week37_162_3.png
+_images/week37_162_4.png
 
    
 
    
 
    
@@ -2510,12 +2522,12 @@ 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
-Mean squared error on training data: 0.00465099
-Mean squared error on test data: 0.28443039
 
    
 
    
-
    Degree of polynomial:  15
+
    Degree of polynomial:  14
+Mean squared error on training data: 0.00465099
+Mean squared error on test data: 0.28443039
+Degree of polynomial:  15
 Mean squared error on training data: 0.00420072
 Mean squared error on test data: 568.47202442
 Degree of polynomial:  16
@@ -2530,12 +2542,12 @@ Mean squared error on test data: 429.23643365
 Degree of polynomial:  19
 Mean squared error on training data: 0.00154860
 Mean squared error on test data: 238.16356503
-Degree of polynomial:  20
-Mean squared error on training data: 0.00140849
-Mean squared error on test data: 1345.68592431
 
    
 
    
-
    Degree of polynomial:  21
+
    Degree of polynomial:  20
+Mean squared error on training data: 0.00140849
+Mean squared error on test data: 1345.68592431
+Degree of polynomial:  21
 Mean squared error on training data: 0.00119699
 Mean squared error on test data: 1836.21110005
 Degree of polynomial:  22
@@ -2550,12 +2562,12 @@ Mean squared error on test data: 1346.92651068
 Degree of polynomial:  25
 Mean squared error on training data: 0.00079910
 Mean squared error on test data: 7697.35412147
-Degree of polynomial:  26
-Mean squared error on training data: 0.00075597
-Mean squared error on test data: 1078.81597834
 
    
 
    
-
    Degree of polynomial:  27
+
    Degree of polynomial:  26
+Mean squared error on training data: 0.00075597
+Mean squared error on test data: 1078.81597834
+Degree of polynomial:  27
 Mean squared error on training data: 0.00068088
 Mean squared error on test data: 3189.20355156
 Degree of polynomial:  28
@@ -2566,9 +2578,9 @@ Mean squared error on training data: 0.00063862
 Mean squared error on test data: 3073.63180447
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8815/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8815/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -2653,7 +2665,7 @@ Mean squared error on test data: 3073.63180447
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8815/3817475779.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 0fc241504..a149f4be5 100644
--- a/doc/LectureNotes/_build/html/week38.html
+++ b/doc/LectureNotes/_build/html/week38.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -2044,7 +2054,7 @@ case under study.
    
 
    
 
    
 
    RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1045a7eb0>})
+                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x280a35220>})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
diff --git a/doc/LectureNotes/_build/html/week39.html b/doc/LectureNotes/_build/html/week39.html
index 456abb5de..6ebf028b7 100644
--- a/doc/LectureNotes/_build/html/week39.html
+++ b/doc/LectureNotes/_build/html/week39.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1808,11 +1818,11 @@ which equals
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11106/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8843/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 
    
 
    
-
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x136af27c0>
+
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1194ee790>
 
    
 
    
 _images/week39_80_2.png
@@ -1870,7 +1880,7 @@ which equals
    
 
    
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x13792dfa0>]
+
    [<matplotlib.lines.Line2D at 0x128f6eee0>]
 
    
 
    
 _images/week39_88_1.png
diff --git a/doc/LectureNotes/_build/html/week40.html b/doc/LectureNotes/_build/html/week40.html
index 76f5d4541..e7d9f8b03 100644
--- a/doc/LectureNotes/_build/html/week40.html
+++ b/doc/LectureNotes/_build/html/week40.html
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1456,15 +1466,15 @@ function.
    
 
    
 
    
 
    Own inversion
-[[3.88391015]
- [3.15024162]]
-Eigenvalues of Hessian Matrix:[0.29734306 4.63081005]
+[[4.34459931]
+ [2.77172582]]
+Eigenvalues of Hessian Matrix:[0.28795864 4.47391428]
 theta from own gd
-[[3.88391015]
- [3.15024162]]
+[[4.34459931]
+ [2.77172582]]
 theta from own sdg
-[[3.92822216]
- [3.17648722]]
+[[4.35401107]
+ [2.71654553]]
 
    
 
    
 _images/week40_25_1.png
diff --git a/doc/LectureNotes/_build/html/week41.html b/doc/LectureNotes/_build/html/week41.html
index 71198fcf4..86da2c3f4 100644
--- a/doc/LectureNotes/_build/html/week41.html
+++ b/doc/LectureNotes/_build/html/week41.html
@@ -2817,7 +2817,7 @@ the Hadamard product, meaning element-wise multiplication.
    
 
    Old accuracy on training data: 0.1440501043841336
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3153,7 +3153,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3162,7 +3162,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3171,7 +3171,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3180,7 +3180,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3189,7 +3189,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3198,7 +3198,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3207,7 +3207,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3216,11 +3216,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3229,11 +3229,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3242,11 +3242,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3255,11 +3255,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3268,11 +3268,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3281,7 +3281,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3290,11 +3290,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3303,11 +3303,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3316,11 +3316,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3329,11 +3329,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3342,11 +3342,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3355,11 +3355,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3368,11 +3368,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3381,11 +3381,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3438,15 +3438,15 @@ Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3776,13 +3776,13 @@ 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 =  1e-05
-Accuracy score on test set:  0.17222222222222222
-
-Learning rate  =  10.0
 Lambda =  0.0001
 Accuracy score on test set:  0.11666666666666667
 
@@ -3793,9 +3793,8 @@ 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
+
+Learning rate  =  10.0
 Lambda =  0.1
 Accuracy score on test set:  0.11388888888888889
 
    
diff --git a/doc/LectureNotes/_build/html/week42.html b/doc/LectureNotes/_build/html/week42.html
index d6e1a66ed..bb4bafbcd 100644
--- a/doc/LectureNotes/_build/html/week42.html
+++ b/doc/LectureNotes/_build/html/week42.html
@@ -1697,7 +1697,7 @@ the Hadamard product, meaning element-wise multiplication.
    
 
    Old accuracy on training data: 0.1440501043841336
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2033,7 +2033,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2042,7 +2042,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2051,7 +2051,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2060,7 +2060,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2069,7 +2069,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2078,7 +2078,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2087,7 +2087,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2096,11 +2096,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2109,11 +2109,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2122,11 +2122,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2135,11 +2135,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2148,11 +2148,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2161,7 +2161,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2170,11 +2170,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2183,11 +2183,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2196,11 +2196,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2209,11 +2209,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2222,11 +2222,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2235,11 +2235,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2248,11 +2248,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2261,11 +2261,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2318,15 +2318,15 @@ Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2617,22 +2617,22 @@ 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 =  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 =  1e-05
-Accuracy score on test set:  0.08611111111111111
-
-Learning rate  =  1.0
 Lambda =  0.0001
 Accuracy score on test set:  0.10555555555555556
 
    
@@ -2644,13 +2644,13 @@ 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 =  0.1
+Accuracy score on test set:  0.08333333333333333
+
+Learning rate  =  1.0
 Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
 
@@ -3116,8 +3116,9 @@ Accuracy score on data set:  0.5
 Learning rate  =  10.0
 Lambda =  1e-05
 Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
+
    
+
    
+
    Learning rate  =  10.0
 Lambda =  0.0001
 Accuracy score on data set:  0.5
 
@@ -3164,7 +3165,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
 
    
 
    
-_images/week42_88_2.png
+_images/week42_88_3.png
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week43.html b/doc/LectureNotes/_build/html/week43.html
index 5a4b18c24..595b830ac 100644
--- a/doc/LectureNotes/_build/html/week43.html
+++ b/doc/LectureNotes/_build/html/week43.html
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
   
  
+ 
  • 
+  
+   Week 44,  Convolutional Neural Networks (CNN)
+  
+ 
    • 
+ 
  • 
+  
+   Week 45,  Recurrent Neural Networks
+  
+ 
    • 
 
 
    
  
@@ -1862,9 +1872,8 @@ Accuracy score on data set:  0.5
 Learning rate  =  1e-05
 Lambda =  1.0
 Accuracy score on data set:  0.5
-
    
-
    
-
    Learning rate  =  1e-05
+
+Learning rate  =  1e-05
 Lambda =  10.0
 Accuracy score on data set:  0.5
 
@@ -1891,8 +1900,9 @@ Accuracy score on data set:  0.5
 Learning rate  =  0.0001
 Lambda =  1.0
 Accuracy score on data set:  0.5
-
-Learning rate  =  0.0001
+
    
+
    
+
    Learning rate  =  0.0001
 Lambda =  10.0
 Accuracy score on data set:  0.5
 
@@ -6364,9 +6374,8 @@ case.
    
 
    
 
    
 
    Adam: Eta=0.001, Lambda=0
-
    
-
    
-
      [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
+
+  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
 
    
 
    
 
      [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
@@ -18834,12 +18843,9 @@ This is then passed through the activation:
    
  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,)
-
    
-
    
-
    prediction for image 0: 8
+
+predictions = (n_inputs) = (1437,)
+prediction for image 0: 8
 correct label for image 0: 6
 
    
 
    
@@ -22318,10 +22324,10 @@ g(x,t) = \sin(\pi x)\cos(\pi t) - \sin(\pi x)\sin(\pi t)
             
    Exercises weeks 43 and 44
    
         
    
     
-    
+    
     
    
         
    next
    
-        
    Project 1 on Machine Learning, deadline October 9 (midnight), 2023
    
+        
    Week 44,  Convolutional Neural Networks (CNN)
    
     
    
     
         
diff --git a/doc/LectureNotes/_build/html/week44.html b/doc/LectureNotes/_build/html/week44.html
index 90f12a1b8..9300af2a0 100644
--- a/doc/LectureNotes/_build/html/week44.html
+++ b/doc/LectureNotes/_build/html/week44.html
@@ -2235,7 +2235,7 @@ labels = (n_inputs) = (1797,)
 
    
 
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
   super(SGD, self).__init__(name, **kwargs)
-2023-11-06 06:34:51.825607: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+2023-11-08 15:30:42.160913: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 
    
 
    
 
    ---------------------------------------------------------------------------
diff --git a/doc/LectureNotes/_build/html/week45.html b/doc/LectureNotes/_build/html/week45.html
index fdaf51645..eed96be58 100644
--- a/doc/LectureNotes/_build/html/week45.html
+++ b/doc/LectureNotes/_build/html/week45.html
@@ -857,6 +857,7 @@ doconce format html week45.do.txt --no_mako -->
 
  • Discussion of project 2
    • 
 
  • Video of lab session from week 43
    • 
 
  • Video of lab session from week 44
    • 
+
  • Video of lab session from week 45
    • 
 
  • See also whiteboard notes from lab session week 44
    • 
 
 
    Material for the lecture on Thursday November 9, 2023.
    
@@ -1075,7 +1076,7 @@ case under study.
    
 
    
 
    
 
    RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13d4a1640>})
+                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x107a08b50>})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
@@ -1619,303 +1620,327 @@ systems such as automatic translation and speech-to-text.
    
 
    Epoch 1/100
 
    
 
    
-
    2023-11-08 06:51:57.259901: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+
    2023-11-08 15:31:22.964588: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 
    
 
    
-
    50/50 - 3s - loss: 2.5680 - 3s/epoch - 69ms/step
+
    50/50 - 3s - loss: 1.3549 - 3s/epoch - 66ms/step
 
    
 
    
 
    Epoch 2/100
 
    
 
    
-
    50/50 - 1s - loss: 1.8934 - 563ms/epoch - 11ms/step
+
    50/50 - 0s - loss: 0.4772 - 473ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 3/100
 
    
 
    
-
    50/50 - 1s - loss: 1.4588 - 512ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.4055 - 471ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 4/100
 
    
 
    
-
    50/50 - 0s - loss: 0.8688 - 466ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3987 - 467ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 5/100
 
    
 
    
-
    50/50 - 0s - loss: 0.4871 - 457ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3954 - 475ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 6/100
 
    
 
    
-
    50/50 - 0s - loss: 0.4162 - 456ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3923 - 468ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 7/100
 
    
 
    
-
    50/50 - 0s - loss: 0.4068 - 451ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3914 - 468ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 8/100
 
    
 
    
-
    50/50 - 0s - loss: 0.4032 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3887 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 9/100
 
    
 
    
-
    50/50 - 0s - loss: 0.4009 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 10/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3978 - 457ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3882 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 11/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3964 - 451ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3868 - 463ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 12/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3936 - 455ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3862 - 466ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 13/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3933 - 450ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3862 - 484ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 14/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3938 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3854 - 464ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 15/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3924 - 452ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3847 - 476ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 16/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3924 - 456ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3839 - 473ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 17/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3919 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3843 - 474ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 18/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3907 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3837 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 19/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3906 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3829 - 461ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 20/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3907 - 452ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3819 - 494ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 21/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3888 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3812 - 480ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 22/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3898 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3816 - 491ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 23/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3889 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3818 - 459ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 24/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3883 - 452ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3812 - 470ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 25/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3885 - 456ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3811 - 452ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 26/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3886 - 452ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3809 - 455ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 27/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3885 - 457ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3798 - 453ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 28/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3872 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3809 - 454ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 29/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3885 - 452ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3802 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 30/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3798 - 463ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 31/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3869 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3789 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 32/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3868 - 455ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3781 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 33/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3790 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 34/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3872 - 455ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3775 - 455ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 35/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3856 - 481ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3785 - 459ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 36/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3853 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3777 - 454ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 37/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3859 - 483ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3782 - 453ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 38/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3862 - 482ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3780 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 39/100
 
    
 
    
-
    50/50 - 1s - loss: 0.3850 - 508ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3764 - 479ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 40/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3841 - 494ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3773 - 498ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 41/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3848 - 473ms/epoch - 9ms/step
+
    50/50 - 1s - loss: 0.3749 - 505ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 42/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3850 - 479ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3769 - 489ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 43/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3841 - 473ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3766 - 462ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 44/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3847 - 479ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3752 - 459ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 45/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3810 - 463ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3758 - 469ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 46/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3843 - 483ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3734 - 477ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 47/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3830 - 467ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3753 - 475ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 48/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3827 - 485ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3756 - 469ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 49/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3820 - 479ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.3748 - 487ms/epoch - 10ms/step
 
    
 
    
 
    Epoch 50/100
 
    
 
    
+
    50/50 - 0s - loss: 0.3741 - 478ms/epoch - 10ms/step
+
    
+
    
+
    Epoch 51/100
+
    
+
    
+
    50/50 - 0s - loss: 0.3744 - 478ms/epoch - 10ms/step
+
    
+
    
+
    Epoch 52/100
+
    
+
    
+
    50/50 - 0s - loss: 0.3756 - 458ms/epoch - 9ms/step
+
    
+
    
+
    Epoch 53/100
+
    
+
    
+
    50/50 - 0s - loss: 0.3725 - 464ms/epoch - 9ms/step
+
    
+
    
+
    Epoch 54/100
+
    
+
    
 
    ---------------------------------------------------------------------------
 KeyboardInterrupt                         Traceback (most recent call last)
 Input In [9], in <cell line: 53>()
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index 007e9499d..f26016e01 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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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -515,7 +515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -583,18 +583,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.95815651]\n",
+      " [1.79503422]\n",
       "Coefficient beta : \n",
-      " [[5.03219974]]\n",
-      "Mean squared error: 0.26\n",
-      "Variance score: 0.90\n",
+      " [[5.33918941]]\n",
+      "Mean squared error: 0.21\n",
+      "Variance score: 0.92\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.41\n"
+      "Mean absolute error: 0.37\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -822,7 +822,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -838,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.004999999999999996\n"
+      "0.005\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 7fefc49e8..dde41f240 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -1077,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1655,7 +1655,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1673,7 +1673,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1691,7 +1691,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1709,7 +1709,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1727,7 +1727,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1745,7 +1745,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1763,7 +1763,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1781,11 +1781,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1803,11 +1803,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1825,11 +1825,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1847,11 +1847,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1869,11 +1869,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1891,7 +1891,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1909,11 +1909,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1931,11 +1931,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1953,11 +1953,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1975,11 +1975,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1997,11 +1997,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2019,11 +2019,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2041,11 +2041,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2063,25 +2063,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.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)",
-      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\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",
-      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data_full[chosen_datapoints]\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[0;32m---> 98\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackpropagation()\n",
-      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36mNeuralNetwork.feed_forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;66;03m# feed-forward for training\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h \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[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_weights\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h \u001b[38;5;241m=\u001b[39m sigmoid(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h)\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_o \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;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
      ]
     }
    ],
@@ -2126,7 +2123,52 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.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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\n",
+      "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",
@@ -2193,7 +2235,620 @@
     "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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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:692: 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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9027777777777778\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8583333333333333\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9055555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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",
@@ -2231,7 +2886,36 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_0.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "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",
@@ -2321,7 +3005,16 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2259440937.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Input \u001b[0;32mIn [12]\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/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
index 1fd483e8f..fccb4a3ab 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
@@ -3021,9 +3021,26 @@
       "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m    140\u001b[0m num_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m250\u001b[39m\n\u001b[1;32m    141\u001b[0m lmb \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m\n\u001b[0;32m--> 143\u001b[0m P \u001b[38;5;241m=\u001b[39m \u001b[43msolve_pde_deep_neural_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_hidden_neurons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlmb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;66;03m## Store the results\u001b[39;00m\n\u001b[1;32m    146\u001b[0m g_dnn_ag \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((Nx, Nt))\n",
       "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36msolve_pde_deep_neural_network\u001b[0;34m(x, t, num_neurons, num_iter, lmb)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;66;03m# Let the update be done num_iter times\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_iter):\n\u001b[0;32m--> 120\u001b[0m     cost_grad \u001b[38;5;241m=\u001b[39m  \u001b[43mcost_function_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_hidden\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    123\u001b[0m         P[l] \u001b[38;5;241m=\u001b[39m P[l] \u001b[38;5;241m-\u001b[39m lmb \u001b[38;5;241m*\u001b[39m cost_grad[l]\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:29\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     28\u001b[0m                     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvspace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\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:23\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvjp(outgrad[\u001b[38;5;241m0\u001b[39m])\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[0;32m---> 23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(\u001b[43moutgrads\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent\u001b[49m\u001b[43m)\u001b[49m, ingrad)\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outgrad[\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m     20\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;124;03m    arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;124;03m    should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\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     26\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     28\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\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",
+      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36mcost_function\u001b[0;34m(P, x, t)\u001b[0m\n\u001b[1;32m     78\u001b[0m g_t \u001b[38;5;241m=\u001b[39m g_trial(point,P)\n\u001b[1;32m     79\u001b[0m g_t_jacobian \u001b[38;5;241m=\u001b[39m g_t_jacobian_func(point,P)\n\u001b[0;32m---> 80\u001b[0m g_t_hessian \u001b[38;5;241m=\u001b[39m \u001b[43mg_t_hessian_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43mP\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m g_t_dt \u001b[38;5;241m=\u001b[39m g_t_jacobian[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     83\u001b[0m g_t_d2x \u001b[38;5;241m=\u001b[39m g_t_hessian[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\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:78\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m     77\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---> 78\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:57\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m     49\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     50\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     51\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     55\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     56\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 57\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     58\u001b[0m     ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m     59\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:61\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     59\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     60\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---> 61\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:103\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m    100\u001b[0m     axis \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m result_ndim\n\u001b[1;32m    102\u001b[0m sl \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m),) \u001b[38;5;241m*\u001b[39m axis \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28;01mNone\u001b[39;00m,)\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concatenate([arr[sl] \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays], axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:103\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    100\u001b[0m     axis \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m result_ndim\n\u001b[1;32m    102\u001b[0m sl \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m),) \u001b[38;5;241m*\u001b[39m axis \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28;01mNone\u001b[39;00m,)\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concatenate([\u001b[43marr\u001b[49m\u001b[43m[\u001b[49m\u001b[43msl\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays], axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m parents \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(box\u001b[38;5;241m.\u001b[39m_node \u001b[38;5;28;01mfor\u001b[39;00m _     , box \u001b[38;5;129;01min\u001b[39;00m boxed_args)\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[0;32m---> 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m \u001b[43mf_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margvals\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     45\u001b[0m node \u001b[38;5;241m=\u001b[39m node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\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:56\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m argnums \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124margnums\u001b[39m\u001b[38;5;124m'\u001b[39m, count())\n\u001b[1;32m     54\u001b[0m vjps_dict \u001b[38;5;241m=\u001b[39m {argnum : translate_vjp(vjpmaker, fun, argnum)\n\u001b[1;32m     55\u001b[0m              \u001b[38;5;28;01mfor\u001b[39;00m argnum, vjpmaker \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(argnums, vjpmakers)}\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp_argnums\u001b[39m(argnums, ans, args, kwargs):\n\u001b[1;32m     57\u001b[0m     L \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(argnums)\n\u001b[1;32m     58\u001b[0m     \u001b[38;5;66;03m# These first two cases are just optimizations\u001b[39;00m\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
index 8e8b20081..b3f74b5c5 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
@@ -1382,7 +1382,7 @@
      "text": [
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
       "  super(SGD, self).__init__(name, **kwargs)\n",
-      "2023-10-25 15:31:33.965944: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-11-08 15:24:42.293245: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
index ca55534bf..113668bb3 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
@@ -61,7 +61,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -196,14 +196,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-10-25 15:32:11.077734: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-11-08 15:25:18.982829: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 3s - loss: 1.4222 - 3s/epoch - 66ms/step\n"
+      "50/50 - 3s - loss: 1.7073 - 3s/epoch - 65ms/step\n"
      ]
     },
     {
@@ -217,7 +217,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.5274 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.5091 - 450ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -231,7 +231,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4426 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4349 - 447ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -245,7 +245,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4375 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4192 - 447ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -259,7 +259,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4336 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4095 - 448ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -273,7 +273,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4310 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4056 - 450ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -287,7 +287,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4287 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4055 - 451ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -301,7 +301,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4277 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4025 - 449ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -315,7 +315,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4266 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3975 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -329,7 +329,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4253 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3994 - 451ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -343,7 +343,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4236 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3976 - 449ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -357,7 +357,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4221 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3973 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -371,7 +371,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4208 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3951 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -385,7 +385,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4199 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3957 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -399,7 +399,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4203 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3953 - 472ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -413,7 +413,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4181 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3940 - 479ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -427,7 +427,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4177 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3939 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -441,7 +441,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4162 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3897 - 483ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -455,7 +455,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4148 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3936 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -469,7 +469,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4146 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3929 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -483,7 +483,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4137 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3913 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -497,7 +497,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4128 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3900 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -511,7 +511,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4130 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3921 - 516ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -525,7 +525,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4106 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3903 - 485ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -539,7 +539,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4099 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3902 - 471ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -553,7 +553,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4100 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3887 - 472ms/epoch - 9ms/step\n"
      ]
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     {
@@ -567,7 +567,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4086 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3895 - 519ms/epoch - 10ms/step\n"
      ]
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     {
@@ -581,7 +581,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4088 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3873 - 477ms/epoch - 10ms/step\n"
      ]
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     {
@@ -595,7 +595,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4078 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3879 - 500ms/epoch - 10ms/step\n"
      ]
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     {
@@ -609,7 +609,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4063 - 465ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3876 - 485ms/epoch - 10ms/step\n"
      ]
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     {
@@ -623,7 +623,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4054 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3865 - 472ms/epoch - 9ms/step\n"
      ]
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     {
@@ -637,7 +637,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4054 - 464ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3856 - 459ms/epoch - 9ms/step\n"
      ]
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     {
@@ -651,7 +651,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4048 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3856 - 502ms/epoch - 10ms/step\n"
      ]
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     {
@@ -665,7 +665,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4051 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3839 - 479ms/epoch - 10ms/step\n"
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@@ -679,7 +679,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4027 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3853 - 459ms/epoch - 9ms/step\n"
      ]
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@@ -693,7 +693,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4008 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3827 - 457ms/epoch - 9ms/step\n"
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     {
@@ -707,7 +707,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4011 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3824 - 453ms/epoch - 9ms/step\n"
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     {
@@ -721,7 +721,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4024 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3824 - 455ms/epoch - 9ms/step\n"
      ]
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     {
@@ -735,7 +735,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4014 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3812 - 455ms/epoch - 9ms/step\n"
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     {
@@ -749,7 +749,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4001 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3823 - 469ms/epoch - 9ms/step\n"
      ]
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     {
@@ -763,7 +763,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3992 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3814 - 527ms/epoch - 11ms/step\n"
      ]
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     {
@@ -777,7 +777,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3814 - 464ms/epoch - 9ms/step\n"
      ]
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     {
@@ -791,7 +791,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3812 - 453ms/epoch - 9ms/step\n"
      ]
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     {
@@ -805,7 +805,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3975 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3779 - 456ms/epoch - 9ms/step\n"
      ]
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     {
@@ -819,7 +819,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3972 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3797 - 457ms/epoch - 9ms/step\n"
      ]
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     {
@@ -833,7 +833,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3948 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3808 - 460ms/epoch - 9ms/step\n"
      ]
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     {
@@ -847,7 +847,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3954 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3788 - 460ms/epoch - 9ms/step\n"
      ]
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     {
@@ -861,7 +861,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3931 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3792 - 455ms/epoch - 9ms/step\n"
      ]
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     {
@@ -875,7 +875,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3946 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3765 - 457ms/epoch - 9ms/step\n"
      ]
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     {
@@ -889,7 +889,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3940 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3775 - 454ms/epoch - 9ms/step\n"
      ]
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     {
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3935 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3777 - 458ms/epoch - 9ms/step\n"
      ]
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     {
@@ -917,7 +917,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3932 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3770 - 458ms/epoch - 9ms/step\n"
      ]
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     {
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
index 198ca1479..78196d19c 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": [
-      "0.023888460698069384\n",
-      "4.161573669199933\n",
-      "[[0.708589   2.01323615]\n",
-      " [2.01323615 6.75406265]]\n"
+      "0.07752620206774397\n",
+      "4.3068687590657415\n",
+      "[[1.01031184 2.85759522]\n",
+      " [2.85759522 9.0542566 ]]\n"
      ]
     }
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@@ -1845,10 +1845,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.08737007811453563\n",
-      "1.792898603630095\n",
-      "[[1.         0.65673455]\n",
-      " [0.65673455 1.        ]]\n"
+      "0.07472152457534222\n",
+      "1.4560786541572335\n",
+      "[[1.         0.61219726]\n",
+      " [0.61219726 1.        ]]\n"
      ]
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@@ -1905,30 +1905,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.1036544   0.17432695]\n",
-      " [ 0.51758232  1.25700419]\n",
-      " [-1.37236385 -4.22805937]\n",
-      " [-0.46766277 -1.52501047]\n",
-      " [ 0.99175336  3.59187177]\n",
-      " [-2.34718587 -5.3512747 ]\n",
-      " [ 1.94980712  6.99450177]\n",
-      " [ 0.24849282 -0.56424167]\n",
-      " [-0.35408251 -2.3930301 ]\n",
-      " [ 0.73000497  2.04391163]]\n",
+      "[[-0.59265811 -0.57943748]\n",
+      " [ 0.08641073  0.06656566]\n",
+      " [-0.68596176 -2.59060904]\n",
+      " [ 0.47558206  1.64482901]\n",
+      " [ 0.21597684 -0.67723533]\n",
+      " [ 1.22935165  5.02293408]\n",
+      " [-0.51249881 -3.38478181]\n",
+      " [ 0.29220202  1.35149796]\n",
+      " [ 0.10955639  0.86177342]\n",
+      " [-0.61796102 -1.71553646]]\n",
       "          0         1\n",
-      "0  0.103654  0.174327\n",
-      "1  0.517582  1.257004\n",
-      "2 -1.372364 -4.228059\n",
-      "3 -0.467663 -1.525010\n",
-      "4  0.991753  3.591872\n",
-      "5 -2.347186 -5.351275\n",
-      "6  1.949807  6.994502\n",
-      "7  0.248493 -0.564242\n",
-      "8 -0.354083 -2.393030\n",
-      "9  0.730005  2.043912\n",
+      "0 -0.592658 -0.579437\n",
+      "1  0.086411  0.066566\n",
+      "2 -0.685962 -2.590609\n",
+      "3  0.475582  1.644829\n",
+      "4  0.215977 -0.677235\n",
+      "5  1.229352  5.022934\n",
+      "6 -0.512499 -3.384782\n",
+      "7  0.292202  1.351498\n",
+      "8  0.109556  0.861773\n",
+      "9 -0.617961 -1.715536\n",
       "          0         1\n",
-      "0  1.000000  0.966337\n",
-      "1  0.966337  1.000000\n"
+      "0  1.000000  0.921567\n",
+      "1  0.921567  1.000000\n"
      ]
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    ],
@@ -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  0.084489  0.079202  0.083269  0.079700  0.076331  0.074355  0.071456   \n",
-      "2   0.0  0.079202  0.075352  0.079849  0.077042  0.074328  0.072527  0.070086   \n",
-      "3   0.0  0.083269  0.079849  0.087761  0.084946  0.082203  0.081779  0.079170   \n",
-      "4   0.0  0.079700  0.077042  0.084946  0.082590  0.080248  0.079820  0.077517   \n",
-      "5   0.0  0.076331  0.074328  0.082203  0.080248  0.078258  0.077833  0.075804   \n",
-      "6   0.0  0.074355  0.072527  0.081779  0.079820  0.077833  0.078423  0.076337   \n",
-      "7   0.0  0.071456  0.070086  0.079170  0.077517  0.075804  0.076337  0.074477   \n",
-      "8   0.0  0.068743  0.067765  0.076678  0.075294  0.073824  0.074307  0.072650   \n",
-      "9   0.0  0.066200  0.065559  0.074301  0.073154  0.071901  0.072338  0.070865   \n",
-      "10  0.0  0.065631  0.064814  0.074306  0.072976  0.071564  0.072718  0.071080   \n",
-      "11  0.0  0.063225  0.062696  0.071960  0.070845  0.069629  0.070705  0.069239   \n",
-      "12  0.0  0.060971  0.060691  0.069733  0.068809  0.067769  0.068771  0.067462   \n",
-      "13  0.0  0.058856  0.058793  0.067619  0.066865  0.065984  0.066919  0.065753   \n",
-      "14  0.0  0.056870  0.056996  0.065613  0.065012  0.064275  0.065147  0.064113   \n",
+      "1   0.0  0.079330  0.084471  0.080541  0.081753  0.082306  0.073011  0.072990   \n",
+      "2   0.0  0.084471  0.092487  0.087280  0.089513  0.090777  0.079391  0.079777   \n",
+      "3   0.0  0.080541  0.087280  0.087211  0.088900  0.089752  0.082231  0.082329   \n",
+      "4   0.0  0.081753  0.089513  0.088900  0.091023  0.092206  0.083848  0.084164   \n",
+      "5   0.0  0.082306  0.090777  0.089752  0.092206  0.093657  0.084672  0.085172   \n",
+      "6   0.0  0.073011  0.079391  0.082231  0.083848  0.084672  0.079611  0.079715   \n",
+      "7   0.0  0.072990  0.079777  0.082329  0.084164  0.085172  0.079715  0.079958   \n",
+      "8   0.0  0.072802  0.079892  0.082196  0.084212  0.085382  0.079597  0.079964   \n",
+      "9   0.0  0.072527  0.079854  0.081937  0.084110  0.085425  0.079353  0.079836   \n",
+      "10  0.0  0.064985  0.070571  0.075171  0.076586  0.077304  0.074181  0.074265   \n",
+      "11  0.0  0.064627  0.070400  0.074809  0.076355  0.077194  0.073842  0.074026   \n",
+      "12  0.0  0.064245  0.070170  0.074403  0.076066  0.077017  0.073458  0.073736   \n",
+      "13  0.0  0.063864  0.069919  0.073987  0.075758  0.076814  0.073063  0.073431   \n",
+      "14  0.0  0.063500  0.069672  0.073582  0.075454  0.076612  0.072676  0.073131   \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   0.068743  0.066200  0.065631  0.063225  0.060971  0.058856  0.056870  \n",
-      "2   0.067765  0.065559  0.064814  0.062696  0.060691  0.058793  0.056996  \n",
-      "3   0.076678  0.074301  0.074306  0.071960  0.069733  0.067619  0.065613  \n",
-      "4   0.075294  0.073154  0.072976  0.070845  0.068809  0.066865  0.065012  \n",
-      "5   0.073824  0.071901  0.071564  0.069629  0.067769  0.065984  0.064275  \n",
-      "6   0.074307  0.072338  0.072718  0.070705  0.068771  0.066919  0.065147  \n",
-      "7   0.072650  0.070865  0.071080  0.069239  0.067462  0.065753  0.064113  \n",
-      "8   0.071008  0.069391  0.069456  0.067774  0.066143  0.064568  0.063051  \n",
-      "9   0.069391  0.067929  0.067859  0.066323  0.064827  0.063378  0.061977  \n",
-      "10  0.069456  0.067859  0.068437  0.066752  0.065119  0.063542  0.062023  \n",
-      "11  0.067774  0.066323  0.066752  0.065207  0.063705  0.062250  0.060845  \n",
-      "12  0.066143  0.064827  0.065119  0.063705  0.062325  0.060983  0.059685  \n",
-      "13  0.064568  0.063378  0.063542  0.062250  0.060983  0.059749  0.058550  \n",
-      "14  0.063051  0.061977  0.062023  0.060845  0.059685  0.058550  0.057446  \n"
+      "1   0.072802  0.072527  0.064985  0.064627  0.064245  0.063864  0.063500  \n",
+      "2   0.079892  0.079854  0.070571  0.070400  0.070170  0.069919  0.069672  \n",
+      "3   0.082196  0.081937  0.075171  0.074809  0.074403  0.073987  0.073582  \n",
+      "4   0.084212  0.084110  0.076586  0.076355  0.076066  0.075758  0.075454  \n",
+      "5   0.085382  0.085425  0.077304  0.077194  0.077017  0.076814  0.076612  \n",
+      "6   0.079597  0.079353  0.074181  0.073842  0.073458  0.073063  0.072676  \n",
+      "7   0.079964  0.079836  0.074265  0.074026  0.073736  0.073431  0.073131  \n",
+      "8   0.080086  0.080069  0.074152  0.074008  0.073810  0.073592  0.073378  \n",
+      "9   0.080069  0.080157  0.073929  0.073876  0.073766  0.073635  0.073504  \n",
+      "10  0.074152  0.073929  0.070129  0.069821  0.069475  0.069119  0.068771  \n",
+      "11  0.074008  0.073876  0.069821  0.069595  0.069327  0.069048  0.068774  \n",
+      "12  0.073810  0.073766  0.069475  0.069327  0.069136  0.068931  0.068731  \n",
+      "13  0.073592  0.073635  0.069119  0.069048  0.068931  0.068800  0.068671  \n",
+      "14  0.073378  0.073504  0.068771  0.068774  0.068731  0.068671  0.068612  \n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index 7fd4fa0a7..7913b8ce8 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: 0.134565 sec\n",
+      "Runtime: 0.155664 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      "   100.2         100.19        0.146591\n"
+      " 99.9688        99.9588         0.15043\n"
      ]
     }
    ],
@@ -917,7 +917,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 99.9919  15.0954        99.9924        0.150989\n"
+      " 100.132  14.8115        100.132        0.147896\n"
      ]
     }
    ],
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -1318,18 +1318,18 @@
       "Error: 0.017355848195593312\n",
       "Bias^2: 0.010331721306655165\n",
       "Var: 0.007024126888938144\n",
-      "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n",
-      "Polynomial degree: 9\n",
-      "Error: 0.026605727637184558\n",
-      "Bias^2: 0.010018312644139219\n",
-      "Var: 0.016587414993045335\n",
-      "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n"
+      "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Polynomial degree: 9\n",
+      "Error: 0.026605727637184558\n",
+      "Bias^2: 0.010018312644139219\n",
+      "Var: 0.016587414993045335\n",
+      "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n",
       "Polynomial degree: 10\n",
       "Error: 0.021592704588021178\n",
       "Bias^2: 0.010516485576646504\n",
@@ -1685,16 +1685,16 @@
       "Mean squared error on test data: 128664.31650694\n",
       "Degree of polynomial:  26\n",
       "Mean squared error on training data: 0.00076905\n",
-      "Mean squared error on test data: 19003.94822514\n"
+      "Mean squared error on test data: 19003.94822514\n",
+      "Degree of polynomial:  27\n",
+      "Mean squared error on training data: 0.00068946\n",
+      "Mean squared error on test data: 2379.66219404\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  27\n",
-      "Mean squared error on training data: 0.00068946\n",
-      "Mean squared error on test data: 2379.66219404\n",
       "Degree of polynomial:  28\n",
       "Mean squared error on training data: 0.00062595\n",
       "Mean squared error on test data: 4082.19983530\n",
@@ -1707,9 +1707,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/626635268.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_10962/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2051,7 +2051,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
@@ -3725,9 +3725,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4037,9 +4037,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4116,9 +4116,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4203,9 +4203,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4266,7 +4266,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  0%|                                                                                                                                                         | 0/10 [00:00"
       ]
@@ -107,7 +107,7 @@
     },
     {
      "data": {
-      "image/png": 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Z0aNHGQD29ddf8+V0V5Pdu3fP6P7/7//+j3Xt2pV5enoyd3d3FhERwcaOHctf5Xbp0iU2evRoFhERwdzd3ZlEImEPP/wwi4+P57fx559/sri4ONakSRP+/frEE0+wQ4cO8WWMXU3GGGOHDh1i/fr14/ffrVs39scffwjKmHoNda/3vn37TJx5QiyPRqAmhBAb9ssvv2DMmDH4999/0b17d2tXhxCHRMEQIYTYiHXr1uH27dto164dRCIRjh49ioULF6JTp071fnsPQhoS6jNECCE2wtvbG+vXr8cnn3yC/Px8yOVyjB8/Hp988om1q0aIQ6PMECGEEEIaNBp0kRBCCCENGgVDhBBCCGnQKBgihBBCSINGHairoNFocOfOHXh7e9NgYYQQQoidYIwhNzcXQUFBVQ5aSsFQFe7cuYPg4GBrV4MQQgghNXDz5s0qb2ZNwVAVvL29AWhPpo+Pj5VrQwghhJDqyMnJQXBwMP89XhkKhqqgaxrz8fGhYIgQQgixM9Xp4kIdqAkhhBDSoFEwRAghhJAGjYIhQgghhDRo1GfIQtRqNUpLS61dDWLHXFxcqrz8kxBCiOVRMFRLjDGkpaUhKyvL2lUhdk4kEiEsLAwuLi7WrgohhDQoFAzVki4QCggIgIeHBw3MSGpEN7inUqlEs2bN6H1ECCH1iIKhWlCr1Xwg5O/vb+3qEDvXuHFj3LlzB2VlZXB2drZ2dQghpMGgDgq1oOsj5OHhYeWaEEegax5Tq9VWrgkhhDQsFAxZADVpEEug9xEhhFgHBUOEEEIIadAoGCK8Pn36YOrUqdauBiGEEFKvKBgiNbJ//35wHEdDChBCCLF7FAwRQgghdkalUmHPnj1QqVTWropDoGCogcrPz8fYsWPh5eUFuVyOL774QrB8zZo16Ny5M7y9vSGTyfDss88iPT0dAHD9+nX07dsXACCVSsFxHMaPHw8A2LFjBx555BH4+vrC398fAwcORFJSUr0eGyGEODKVSoXIyEj0798fkZGRFBBZAAVDDdTbb7+Nffv2YevWrdi1axf279+PhIQEfnlJSQk+/vhjnDlzBtu2bUNKSgof8AQHB2Pz5s0AgMuXL0OpVOLrr78GoA2ypk+fjhMnTuCff/6BSCTCU089BY1GU+/HSAghjighIQFKpRIAoFQqoVAorFwj+0eDLtoIlUqFhIQEREdHQyqV1um+8vLysGLFCqxevRr9+/cHAKxatQpNmzbly0yYMIF/Hh4ejiVLluDhhx9GXl4evLy84OfnBwAICAiAr68vX/bpp58W7GvFihUICAjAhQsX0LZt2zo8KkIIaRiio6Mhl8uhVCoRFBSEqKgoa1fJ7lFmyAbUd8ozKSkJJSUliImJ4ef5+fmhZcuW/PSpU6cwZMgQhISEwNvbG3369AEApKamVrntZ599FuHh4fDx8UFYWFi11iOEEFI9UqkUiYmJ2LNnD86fP1/nP6AbAgqGbEB9pzwZY5Uuz8/Px2OPPQYvLy+sWbMGJ06cwNatWwFom88qM2jQIGRkZOCnn37CsWPHcOzYsWqtRwghpPqkUiliY2MpELIQmwmGDh48iEGDBiEoKAgcx2Hbtm2C5RzHGX0sXLjQ5Dbj4+ONrlNUVFTHR2MeXcoTQL2kPJs3bw5nZ2ccPXqUn6dSqXDlyhUAwKVLl3D//n189tln6NmzJ1q1asV3ntYxduuIjIwMXLx4ETNnzkRsbCxat25NHfsIIYTYPJsJhvLz89GhQwd8++23RpcrlUrB4//+7//AcZxBH5WKfHx8DNZ1c3Ori0OosfpOeXp5eeHFF1/E22+/jX/++Qfnz5/H+PHjIRJp3w7NmjWDi4sLvvnmGyQnJ+P333/Hxx9/LNhGSEgIOI7Dn3/+iXv37iEvLw9SqRT+/v5YtmwZrl27hr1792L69Ol1eiyEEEJIbdlMB+q4uDjExcWZXC6TyQTTv/32G/r27Yvw8PBKt8txnMG6tkiX8qwvCxcuRF5eHgYPHgxvb2+8+eabyM7OBqC9e3p8fDzef/99LFmyBFFRUVi0aBEGDx7Mr9+kSRPMmTMH7733Hl544QWMHTsW8fHxWL9+Pd544w20bdsWLVu2xJIlS/j+RoQQQogt4lhVHUisgOM4bN26FUOHDjW6/O7du2jatClWrVqFZ5991uR24uPj8dJLL6FJkyZQq9Xo2LEjPv74Y3Tq1MnkOsXFxSguLuanc3JyEBwcjOzsbPj4+AjKFhUVISUlBWFhYTaXbSL2h95PhBBiOTk5OZBIJEa/vyuymWYyc6xatQre3t4YNmxYpeVatWqF+Ph4/P7771i3bh3c3NzQo0cPXL161eQ68+fPh0Qi4R/BwcGWrj4hhBBCbIhdBkP/93//hzFjxlT567lbt2547rnn0KFDB/Ts2RMbN27EQw89hG+++cbkOjNmzEB2djb/uHnzpqWrTwghhBAbYjN9hqrr0KFDuHz5MjZs2GD2uiKRCF26dKk0M+Tq6gpXV9faVJEQQgghdsTuMkMrVqxAdHQ0OnToYPa6jDGcPn2av4ydEEIIIcRmMkN5eXm4du0aP52SkoLTp0/Dz88PzZo1A6DtDLVp0yaDm4rqjB07Fk2aNMH8+fMBAHPmzEG3bt3QokUL5OTkYMmSJTh9+jS+++67uj8gQgghhNgFmwmGTp48yd8JHQA/Ps24ceMQHx8PAFi/fj0YYxg9erTRbaSmpvJj5QBAVlYWXn75ZaSlpUEikaBTp044ePAgHn744bo7EEIIIYTYFZu8tN6WVHZpHl0KTSyJ3k+EEGI5Dn9pPSGEEGLPVCoV9uzZg5SUFOzZs6fKWxeZKq+bT7c+qh2baSYjhBBCGgKVSoXIyEgolUqIRCJoNBrI5XIkJiYavR2TqfL//vsvevToAaVSWen6pGqUGSI2Kz4+Hr6+vvWyr/Hjx5sc8ZwQQiwpISEBSqUSAKDRaABo77+pUCjMKr9p0yZ+fmXrk6pRZog0KNevX0dYWBhOnTqFjh071ul+Pv74Y+zduxdpaWkICgrCc889hw8++AAuLi51tl9CiO2Ljo6GXC4XZHqCgoIQFRVltHzLgpZY4bwCbqXlfQmdxE4I+jYIG8QbUKYug5PYCe4vuuOo6ChcglzQ4tsW8O7oXV+HZPcoGCKkDly6dAkajQY//vgjmjdvjvPnz2PixInIz8/HokWLrF09QogVSaVSJCYmQqFQIDw8HMnJyYiKijLZxJWxOAPhpRVuSq4Gym6WIQAB/HTJjRIAQFFKEW59dQutV7Wuy8NwKNRM1kD16dMHr7/+OqZOnQqpVIrAwEAsW7YM+fn5eOGFF+Dt7Y2IiAhs374dAKBWq/Hiiy8iLCwM7u7uaNmyJb7++mt+e0VFRYiMjMTLL7/Mz0tJSYFEIsFPP/1UrTrFx8ejWbNm8PDwwFNPPYWMjAyDMn/88Qeio6Ph5uaG8PBwzJkzB2VlZfxyjuPw/fffIy4uDu7u7ggLC8OmTZv45WFhYQCATp06geM49OnTR7D9RYsWQS6Xw9/fH1OmTEFpaWm16l7RgAEDsHLlSjz22GMIDw/H4MGD8dZbb2HLli012h4hxLFIpVLExsYiLCwMsbGxlfb1KVOVf8Y5BzibfDj5l+c3yrLLjG2KmMJIpbKzsxkAlp2dbbCssLCQXbhwgRUWFlqhZrXTu3dv5u3tzT7++GN25coV9vHHHzORSMTi4uLYsmXL2JUrV9irr77K/P39WX5+PispKWEfffQRO378OEtOTmZr1qxhHh4ebMOGDfw2T506xVxcXNjWrVtZWVkZ69GjBxsyZEi16nP06FHGcRybP38+u3z5Mvv666+Zr68vk0gkfJkdO3YwHx8fFh8fz5KSktiuXbtYaGgomz17Nl8GAPP392c//fQTu3z5Mps5cyYTi8XswoULjDHGjh8/zgCwPXv2MKVSyTIyMhhjjI0bN475+PiwSZMmsYsXL7I//viDeXh4sGXLlvHbfuWVV5inp2eljxs3bpg8xg8++IBFR0ebXG7P7ydCSN053u4424d97ID7gUrLFacVs33Yx/ZhHzs7+Gw91c52Vfb9XRGNM1SFGo0z1LkzkJZWvxWVyYCTJ6tdvE+fPlCr1Th06BAAbeZHIpFg2LBhWL16NQAgLS0NcrkcR44cQbdu3Qy2MWXKFNy9exe//vorP2/hwoVYsGABRo8ejU2bNuHcuXNo1KhRlfV59tlnoVKp+EwUADzzzDPYsWMHsrKyAAC9evVCXFwcZsyYwZdZs2YN3nnnHdy5cweANjM0adIkfP/993yZbt26ISoqCkuXLjXZZ2j8+PHYv38/kpKSIBaLAQAjR46ESCTC+vXrAQDp6enIycmp9DhCQ0Ph5GTY+pyUlISoqCh88cUXeOmll4yuS+MMEUKMOR55HAUXCiD2EqNnbk+T5UrSS/Bf4H8AAP9B/mj3e7v6qqJNMmecIeozVBfS0oDbt61diyq1b9+efy4Wi+Hv74927cr/eAIDAwFogwAA+OGHH7B8+XLcuHEDhYWFKCkpMeiE/Oabb+K3337DN998g+3bt1crEAKAixcv4qmnnhLMi4mJwY4dO/jphIQEnDhxAp9++ik/T61Wo6ioCAUFBfDw8ODXq7id06dPV1mHyMhIPhACALlcjnPnzvHTAQEBCAgIqNbx6Ltz5w4GDBiAESNGmAyECCHEFFamzVlwTlzlBfUWMw3lOcxBwVBdkMnsYp/Ozs6CaY7jBPM4TvuXpdFosHHjRkybNg1ffPEFYmJi4O3tjYULF+LYsWOCbaSnp+Py5csQi8W4evUqBgwYUK26VCdBqdFoMGfOHAwbNsxgWVWZFN2xVMbY+dBdxgoAkyZNwpo1ayrdxoULF/h76QHaQKhv376IiYnBsmXLqqwDIYRUxNQPPh/FlZfTD4ZAsZBZKBiqC2Y0V9mLQ4cOoXv37pg8eTI/LykpyaDchAkT0LZtW0ycOBEvvvgiYmNj0aZNmyq336ZNGxw9elQwr+J0VFQULl++jObNm1e6raNHj2Ls2LGC6U6dOgEAf1m7Wq2usk4VzZ07F2+99ValZYKCgvjnt2/fRt++fREdHY2VK1cK7ptHCGk4Eu4k4OezP6NEXVKj9Z/IewJe8EKeOg+T/5psspxLrguGYigAoKSsZvtqqCgYItXSvHlzrF69Gjt37kRYWBh+/vlnnDhxgr86CwC+++47HDlyBGfPnkVwcDC2b9+OMWPG4NixY1WOrfPGG2+ge/fuWLBgAYYOHYpdu3YJmsgA4KOPPsLAgQMRHByMESNGQCQS4ezZszh37hw++eQTvtymTZvQuXNnPPLII1i7di2OHz+OFStWANA2dbm7u2PHjh1o2rQp3NzcIJFIqnUOzGkmu3PnDvr06YNmzZph0aJFuHfvHr9MZo3MISHEKjRMgyHrh+B2bs27TvQu7A0veCFXnYvvT35vspxXoRcfDCVnJqMzOtd4nw0N/VQl1TJp0iQMGzYMo0aNQteuXZGRkSHIEl26dAlvv/02li5diuDgYADa4CgrKwsffvhhldvv1q0bli9fjm+++QYdO3bErl27MHPmTEGZxx9/HH/++Sd2796NLl26oFu3bvjyyy8REhIiKDdnzhysX78e7du3x6pVq7B27Vo+O+Xk5IQlS5bgxx9/RFBQEIYMGVLbU2PUrl27cO3aNezduxdNmzaFXC7nH4SQhqNEXSIIhDwLPeFV6GXWw0mjzVtoOI2p3QAAmF7bWEFJQd0ckIOiq8mqQHetty8cx2Hr1q12eWsNej8R4niKyorg/qk7RBoRfvrlJ4RfC696JRO4YA4+x0xfFZWZnglxR23Houvtr2P8mfE13pcjoKvJCCGEEBugyzc0VzavVSAEAN7B3ugk72RyeRrScAmXHuy4VrtqcCgYIvUiLi6OH9Ooovfffx/vv/9+PdeIEELqnq7pykVd3m/SLdQNHq09zNqO2EeM4LeCKy0jEpf3fOE0VV9BS8pRMETqxfLly1FYWGh0mZ+fn8X2Q62+hBBbwn8m6X00NR7eGBELIyy+L8EVq/RRaBYKhki9aNKkibWrQAgh9U6XGRKx8kDlRuoN+Kn8+PuRqVQqJCQkICIiAklJSSb/j46ONnkPM5VKhZ07d6IJmuh2LFiWkJAgWN/UPivbhyOjYIgQQgipI7rMEKc3IuL6jevx3KHnkJiYCEA7+r1SqYRIJIJGozH5v1wuR2JiokGwolKp0Lp1a21AhJ2C/apUKn77uvUr26epfTg6urSeEEIIqSO6zBDHOME8pVIJhUKBhIQEKJVKAOBHvDf1v26dihISEnD37l1BN4Gy0jJ+mW771dmnqX04OgqGCCGEkDrCZ4YqBENBQUGIiopCdHQ0P/6Yrs+Pqf9161QUHR2tvZekXtOYk9iJX6bbfnX2aWofjo6ayQghhJA6Yiwz9Oyzz2Lhtwv5pqjExEQoFAqEh4cjOTnZ5P9RUVFGm6+kUikuXryIXbt3AaO083TNclKplN++bv3so9nY3ns7Mu9kwtPTE/n5+fz/vr6+SJuWBvFrYvh0rnxsHkdCwRAhhBBSx/Q7UIdFhAmCGqlUitjYWO2yB7c4MvW/KVKpFIMHD8YxaG+erR986W8fAC6Nu4TCK4XgwKEABYL/sx/8yz+Tj86nGs7tPKiZjNis+Ph4+Pr61su+xo8fb5ejVhNCbJuxDtR19c3LcXr7qOTS+uLU4iq3VXyr6jKOhIIh0qBcv34dHMfh9OnTdb6v0NBQcBwneLz33nt1vl9CiO0w1kwmCFosqLrjDDG1dqFnW090u9lN8HANcdWWaWBjtlEzGSF1aO7cuZg4cSI/7eXlZcXaEELqm7EO1HWWGRJVLzPEyh6MfeQugltT4X0QRS6iKtd3RJQZaqD69OmD119/HVOnToVUKkVgYCCWLVuG/Px8vPDCC/D29kZERAS2b98OAFCr1XjxxRcRFhYGd3d3tGzZEl9//TW/vaKiIkRGRuLll1/m56WkpEAikeCnn36qVp3i4+PRrFkzeHh44KmnnkJGRoZBmT/++APR0dFwc3NDeHg45syZg7KyMn45x3H4/vvvERcXB3d3d4SFhWHTpk38cl27e6dOncBxHPr06SPY/qJFiyCXy+Hv748pU6agtLS0WnU3xdvbGzKZjH9QMERIw2IsM4Q6ulOGWCSuuj4axgc6nJORiuhmUTBEGopVq1ahUaNGOH78OF5//XW8+uqrGDFiBLp37w6FQoHHH38czz//PAoKCqDRaNC0aVNs3LgRFy5cwEcffYT3338fGzduBAC4ublh7dq1WLVqFbZt2wa1Wo3nn38effv2FWRGTDl27BgmTJiAyZMn4/Tp0+jbty8++eQTQZmdO3fiueeewxtvvIELFy7gxx9/RHx8PD799FNBuQ8//BBPP/00zpw5g+eeew6jR4/GxYsXAQDHjx8HAOzZswdKpRJbtmzh19u3bx+SkpKwb98+rFq1CvHx8YiPj+eXT5o0CV5eXpU+UlNTBXX5/PPP4e/vj44dO+LTTz9FSUlJ9V8gQojdM9ZnSJDBsSAOHDTQjhckCL7066Muj3I4MQVDOhxraA2DZsrJyYFEIkF2djZ8fISXGRYVFSElJQVhYWFwcytPNXZe1hlpeWn1Wk+ZlwwnXz5Z7fJ9+vSBWq3mb56qVqshkUgwbNgwrF69GgCQlpYGuVyOI0eOoFu3bgbbmDJlCu7evYtff/2Vn7dw4UIsWLAAo0ePxqZNm3Du3Dk0atSoyvo8++yzUKlUfCYKAJ555hns2LEDWVlZAIBevXohLi4OM2bM4MusWbMG77zzDu7cuQNAmxmaNGkSvv/+e75Mt27dEBUVhaVLl+L69esICwvDqVOn0LFjR77M+PHjsX//fiQlJUEs1v66GjlyJEQiEdavXw8ASE9PR05OTqXHERoaCicnbevzV199xV/Kevz4ccyYMQNDhgzB8uXLja5r6v1ECLFf6fnpCFwUiB6XeuCT9dofeGHzwhAyI6RO9veP6B+ImRg3Q27i+evPGyxXF6lxyF37ue/bxxcd93UULD/e+jgKLhVALBGjZ1bPOqljfans+7si6jNUB9Ly0nA797a1q1Gl9u3b88/FYjH8/f3Rrl07fl5gYCAAbRAAAD/88AOWL1+OGzduoLCwECUlJYKAAgDefPNN/Pbbb/jmm2+wffv2agVCAHDx4kU89dRTgnkxMTHYsWMHP52QkIATJ04IMkFqtRpFRUUoKCiAh4cHv17F7VSnw3RkZCQfCAGAXC7HuXPn+OmAgAAEBARU63gAYNq0afzz9u3bQyqVYvjw4Xy2iBDi+Iz1GaqrzJB24wBYhavX9OtTppf/MNaqpltNY+mK2TYKhuqAzEtmF/t0dnYWTHMcJ5inu+JBo9Fg48aNmDZtGr744gvExMTA29sbCxcuxLFjxwTbSE9Px+XLlyEWi3H16lUMGDCgWnWpToJSo9Fgzpw5GDZsmMGyqjIp1bl6w9j50A1RD2ibydasWVPpNi5cuIBmzZoZXabLrl27do2CIUIaCKN9huqwg4puf145Xrj1zS2D5ZrC8s806jNUzmaCoYMHD2LhwoX8PVO2bt0qGPdl/PjxWLVqlWCdrl274ujRo5Vud/Pmzfjwww/5u/J++umnBhkISzOnucpeHDp0CN27d8fkyZP5eUlJSQblJkyYgLZt22LixIl48cUXERsbizZt2lS5/TZt2hi8lhWno6KicPnyZTRv3rzSbR09ehRjx44VTHfq1AkA4OLiAkCbUTLX3Llz8dZbb1VaJigoyOSyU6dOAQA/DD4hxPEZvZqsDhNDGk4b7PiqfHHtjWuVlt1/cz+e+lr4fTg3cy6aoinyS/IR/nW4YFnPkJ5YMXgFnEQ2EzpYjM0cUX5+Pjp06IAXXngBTz/9tNEyAwYMwMqVK/lp3RebKUeOHMGoUaPw8ccf46mnnsLWrVsxcuRIHD58GF27drVo/R1d8+bNsXr1auzcuRNhYWH4+eefceLECcGoqN999x2OHDmCs2fPIjg4GNu3b8eYMWNw7NixKl+rN954A927d8eCBQswdOhQ7Nq1S9BEBgAfffQRBg4ciODgYIwYMQIikQhnz57FuXPnBJ2tN23ahM6dO+ORRx7B2rVrcfz4caxYsQKAtqnL3d0dO3bsQNOmTeHm5gaJRFKtc2BOM9mRI0dw9OhR9O3bFxKJBCdOnMC0adMwePBgk5kjQojjqo8O1ABwJeQK2ia3rVbZE7ITSMlKEcwr0Wgv8mCMGSxLyUrBc+2eQ/+I/paprA2xmWAoLi4OcXFxlZZxdXWFTFb95qDFixejf//+fIfbGTNm4MCBA1i8eDHWrVtXq/o2NJMmTcLp06cxatQocByH0aNHY/LkyXyH50uXLuHtt9/GihUrEBwcDEAbHHXo0AEffvghPv/880q3361bNyxfvhyzZs3C7Nmz8eijj2LmzJn4+OOP+TKPP/44/vzzT8ydOxcLFiyAs7MzWrVqhZdeekmwrTlz5mD9+vWYPHkyZDIZ1q5dy2ennJycsGTJEsydOxcfffQRevbsif3791vwTGm5urpiw4YNmDNnDoqLixESEoKJEyfinXfesfi+CCG2g6kZ7qfex5lzZxDdMxrM6cEAh8We5YXqMDPkssoFX6/4GuoCNUQiETQaDcQiMTgRB6ZhfFY8V5qLixEXESAS/sDTXZ4vggiN3RujtKwUpShFfmk+ACC7OLvuKm9FNnk1GcdxRpvJtm3bBhcXF/j6+qJ379749NNPK/2l3qxZM0ybNk3QkfWrr77C4sWLcePGDaPrFBcXo7i4fBjynJwcBAcHm3U1GbEeY+8de0HvJ0LsW9GtIiR0T0DpzVJooMFuj9147tJz+OnpnzD0xFC+XNC8IDw046E6qYNKpUJkZCSUSiUfDMnlcvz777+IiYnB3bt3AQAymQwXLlwQ3CNNpVLh94DfEVIWghKUYJxsHNLS0uD9mDdyu+cCADYO34gRkSPqpO6WZs7VZHYzzlBcXBzWrl2LvXv34osvvsCJEyfQr18/QeBSUVpaGn9FlE5gYCDS0kxf9j5//nxIJBL+octyEEIIIZXJ+C0DpTe1A7WKIMKjBY/i3MlzGHRykKDcrQLDjs2Wout3C4C/AESpVGLTpk18IARovx8VCoXBuqVlpYIyAJCbm8vPYw7as9pugqFRo0bhySefRNu2bTFo0CBs374dV65cwV9//VXpehWvImKMVXpl0YwZM5Cdnc0/bt68aZH6N3RxcXEmByqcN2+etatHCCG1pikWXo8uhhitm7eGmJVfw/6b12/oMKVDndUhOjqav0hDd6+yoKAgjBgxQpAckMlkiIqKMlhXN04aB47vlqKfVbHBxiSLsJk+Q+aSy+UICQnB1atXTZaRyWQGWaD09HSDbJE+V1dXuLq6WqyeRGv58uUoLCw0uszPz89i+3HUP1RCiO1jGsPPHx9PH6igAgAktUzCR0c+EjRNWZpUKkViYiIUCgXCw8ORnJzMD/568eJFHDhwAADQu3dvg3pIpVK0btMahWcL4ezkjAsXLkChUOAYdwwfHPqgzupsC+w2GMrIyMDNmzcrvUw5JiYGu3fvFvQZ2rVrF7p3714fVSR6mjRpYu0qEEJI3TIyUKGmtHymi4tLnQZCOlKpFLGxsQAguOJXKpVW2Z9S7PQgi8XKt3P+6Hl+uaM2k9lMMJSXl4dr18rHREhJScHp06fh5+cHPz8/zJ49G08//TTkcjmuX7+O999/H40aNRKMGTR27Fg0adIE8+fPBwD873//Q69evfD5559jyJAh+O2337Bnzx4cPny43o+PEEKIYzOWGRKM+GwHHVP4y/71qq3ftcRRs+82EwydPHkSffv25aenT58OABg3bhy+//57nDt3DqtXr0ZWVhbkcjn69u2LDRs2wNvbm18nNTWVbyMFgO7du2P9+vWYOXMmPvzwQ0RERGDDhg00xhAhhBDLM5IZ0g+GGGcHgYSREaj1x0iizFAd69OnT6UR586dO6vchrHxYoYPH47hw4fXpmqEEEJIlarKDDGRHQQSxoIhygwRQgghpFqMZYbUghSL7dOr48VxF6Ep0CA4Mxiz0mbhXLNzYEMpGCKEEEKICY6QGdLPAt1drR2XSAIJ+qAP+lzog7vP3gXqbmQAq7GD7lykoYqPj4evr2+97Gv8+PF2OWo1IcSGGLuaTK03084yQ8aI7jpm2OCYR0WICdevXwfHcTh9+nSd7mf//v3gOM7o48SJE3W6b0KIdRjNDJXaV2aoYjDk0coD9168Vz7DDg6hJigYIqQOdO/eHUqlUvB46aWXEBoais6dO1u7eoQQC1MoFTiaetRg/tbzW8sn7DAzJPIQQeNTnt1y1A7UFAw1UH369MHrr7+OqVOnQiqVIjAwEMuWLUN+fj5eeOEFeHt7IyIigr8rvVqtxosvvoiwsDC4u7ujZcuW+Prrr/ntFRUVITIyEi+//DI/LyUlBRKJBD/99FO16hQfH49mzZrBw8MDTz31FDIyMgzK/PHHH4iOjoabmxvCw8MxZ84clJWV8cs5jsP333+PuLg4uLu7IywsDJs2beKX6wYg69SpEziOQ58+fQTbX7RoEeRyOfz9/TFlyhSUlpaiJlxcXCCTyfiHv78/fv/9d0yYMKHS28EQQuzP3by76La8G/al7DNYtv7M+vIJO/jGrfj5xDlxgkvrjTUFOgLqQN2ArVq1Cu+88w6OHz+ODRs24NVXX8W2bdvw1FNP4f3338dXX32F559/HqmpqXB2dkbTpk2xceNGNGrUCP/99x9efvllyOVyjBw5Em5ubli7di26du2KJ554AoMGDcLzzz+Pvn37YuLEiVXW5dixY5gwYQLmzZuHYcOGYceOHZg1a5agzM6dO/Hcc89hyZIl6NmzJ5KSkvjgS7/shx9+iM8++wxff/01fv75Z4wePRpt27ZF69atcfz4cTz88MPYs2cPIiMj4eLiwq+3b98+yOVy7Nu3D9euXcOoUaPQsWNHvv6TJk3CmjVrKj2OCxcuoFmzZgbzf//9d9y/fx/jx4+v8lwQQuyASgVs2gRkZ+OSOgmlmlKImGG0I9aU35escW4ZsHBhfdbSfLdbASgfv4+7qwSuXgQQoJ1x+jSw8Ibl9yuVAi+9ZPntVhPHHDXnZSE5OTmQSCTIzs4W3KwO0GZDUlJSEBYWBjc3N37+yc4nUZJWUq/1dJG5oPPJ6je/9OnTB2q1GocOHQKgzfxIJBIMGzYMq1evBqC9Y7FcLseRI0fQrVs3g21MmTIFd+/exa+//srPW7hwIRYsWIDRo0dj06ZNOHfuHBo1alRlfZ599lmoVCo+EwUAzzzzDHbs2IGsrCwAQK9evRAXF4cZM2bwZdasWYN33nkHd+7cAaD9VTNp0iR8//33fJlu3bohKioKS5cuxfXr1xEWFoZTp06hY8eOfJnx48dj//79SEpKglis/fAaOXIkRCIR1q/X/rJLT09HTk5OpccRGhrK3+hQ3xNPPAEA+Pvvv02ua+r9RAixQcOHA5s3AwD2hwJ9xwOv7HoFz/z3jKBYXvB0eN38EgAQgH/QBp/Uc0XNcwqLka13uZgEZ3C16QEE3HoDAHA35BOMuvGP5XfcogVw5YpFN1nZ93dFlBmqAyVpJSi5Xb/BUE20b9+efy4Wi+Hv74927drx83Q3tE1PTwcA/PDDD1i+fDlu3LiBwsJClJSUCAIKAHjzzTfx22+/4ZtvvsH27durFQgBwMWLFwW3VgG095bbsWMHP52QkIATJ07g008/5eep1WoUFRWhoKAAHh4e/HoVt1OdDtORkZF8IARobwZ87tw5fjogIAABAQHVOh59t27dws6dO7Fx40az1yWE2KizZ/mnDIBHkQc6Jxn+IG13sxVS+Cl7aGMS5kc4qMEJ5jlmMz8FQ3XAReZSdSEb2Kezs7NgmuM4wTxd27FGo8HGjRsxbdo0fPHFF4iJiYG3tzcWLlyIY8eOCbaRnp6Oy5cvQywW4+rVqxgwYEC16lKdBKVGo8GcOXMwbNgwg2VVZVKq00/H2PnQaMo/vGraTLZy5Ur4+/tj8ODBVdaBEGJnPD3BZryLX6a2h6RQYrA4BeX9KNXduwNvbq7P2plN84EHcKl8urTVQzhachSDdNO9ewNDXrX8jr28LL9NM1AwVAfMaa6yF4cOHUL37t0xefJkfl5SUpJBuQkTJqBt27aYOHEiXnzxRcTGxqJNmzZVbr9NmzY4elR4JUbF6aioKFy+fBnNmzevdFtHjx7F2LFjBdOdOnUCAL6PkFqtrrJOFc2dOxdvvfVWpWWCgoIE04wxrFy5EmPHjjUItgghdkz3A87VFcynOySF4srLA7jXyh0Y9mQdV6x2suP3QqQXDCX55+HajWJ+Ot3dAzDyg9TeUTBEqqV58+ZYvXo1du7cibCwMPz88884ceIEf3UWAHz33Xc4cuQIzp49i+DgYGzfvh1jxozBsWPHBB2VjXnjjTfQvXt3LFiwAEOHDsWuXbsETWQA8NFHH2HgwIEIDg7GiBEjIBKJcPbsWZw7dw6ffFLeDr9p0yZ07twZjzzyCNauXYvjx49jxYoVALRNXe7u7tixYweaNm0KNzc3SCSGv+aMqUkz2d69e5GSkoIXX3zRrPUIIXakQuvXPP95WPTmIiz/dDny8vMAAMU+xVgyd4kVKmee9kva460Db0GUI4JGosGH332IrSPKhwfw9/O3Yu3qjh1c6EdswaRJkzBs2DCMGjUKXbt2RUZGhiBLdOnSJbz99ttYunQpgoODAWiDo6ysLHz44YdVbr9bt25Yvnw5vvnmG3Ts2BG7du3CzJkzBWUef/xx/Pnnn9i9eze6dOmCbt264csvv0RISIig3Jw5c7B+/Xq0b98eq1atwtq1a/nslJOTE5YsWYIff/wRQUFBGDJkSG1PTaVWrFiB7t27o3Xr1nW6H0JIPdNlhjhOMNji6SdPY8PVDWg/oz3m3JyDwVsHY/DWwfj2+rfwb2L7gUSj0Eb46vpXGLNnDL5K+QoRHSIweEh5E7+LU/13A6kPdDVZFWpyNRmxHo7jsHXrVru8tQa9nwixI82bA0lJgL8/dn+5Ds7jtM3gKc+l4IWfX7By5Szr149+RaOPtRfDKD9UYvTc0VauUfWYczUZZYYIIYSQWhDcmd4Bv1UFgy6a393SLjjgy0ZsUVxcHLy8vIw+5s2bZ+3qEUKIeUw0kznkt6reMTEHvTkZdaAm9WL58uUoLCw0uszPz89i+6FWX0JIfRMEQw44DI9gaBJ7GCqpBigYIvWiSZMm1q4CIYRYjl5mSD9AcMh7D+pnuxz096YjJvQIIYSQeuPozWSCAI+CIWKK/ijFhNQUNfERYoc4ThggOOC3KuPKD1AQ+DkQaiarBRcXF4hEIty5cweNGzeGi4uLY6ZISZ1jjOHevXsGt0QhhNgoZiJAcMBgiBM5/vcaBUO1IBKJEBYWBqVSyd81nZCa4jgOTZs2FdwslhBi+xy9A7XgmBy0IYSCoVpycXFBs2bNUFZWVqP7XRGi4+zsTIEQIfbCRAdqh8wM6bV4OGpzPgVDFqBr2qDmDUIIaXj0M0MO2aSkH+A5aGbIAWNYQgghpJ5wnOOPQE1XkxFCCCHEwIPmosKyRnD5P72blzrgt6p+MOSx0gNH9xy1Ym3qhgO+bIQQQkjdY+BwRjUTrpdc+XklZSVWrFEd0YsU/HL9cHfQXdy+edt69akDFAwRQggh5mIMarihSB3Iz9JAg2sB16xYqbrRIa4DSsWl/LSkSIIDuw5YsUaWR8EQIYQQUiPCr9Ax/xsD7xhvK9Wl7oS0CoHsiAyFzuX3l2wR3sKKNbI8CoYIIYQQczHh/duPNT+GNGkavDy9rFalutSuSzukdkvlpz09PK1YG8ujYIgQQgipkfKvUN0tKziHHHVRS+xUPg6ao92GymaCoYMHD2LQoEEICgoCx3HYtm0bv6y0tBTvvvsu2rVrB09PTwQFBWHs2LFVjvocHx8PjuMMHkVFRXV8NIQQQhwd0wt8NJw2OHDoWzLpRQyOdo8ymwmG8vPz0aFDB3z77bcGywoKCqBQKPDhhx9CoVBgy5YtuHLlCgYPHlzldn18fKBUKgUPNze3ujgEQgghDQVjaGiZIf1D06gdKzNkMyNQx8XFIS4uzugyiUSC3bt3C+Z98803ePjhh5GamopmzZqZ3C7HcZDJZBatKyGEEFLgxAFl2ucNLTNEzWQ2Ijs7GxzHwdfXt9JyeXl5CAkJQdOmTTFw4ECcOnWq0vLFxcXIyckRPAghhBB937TJRespDSwzpB8MOVhmyC6DoaKiIrz33nt49tln4ePjY7Jcq1atEB8fj99//x3r1q2Dm5sbevTogatXr5pcZ/78+ZBIJPwjODi4Lg6BEEKIHfuhdQHKRIZ9huTecmtVqe7p9xlSU58hqyotLcUzzzwDjUaDpUuXVlq2W7dueO6559ChQwf07NkTGzduxEMPPYRvvvnG5DozZsxAdnY2/7h586alD4EQQoidKxEziFj5V6jMW4YPen6AgQ8NtGKt6pgDN5PZTJ+h6igtLcXIkSORkpKCvXv3VpoVMkYkEqFLly6VZoZcXV3h6upqcjkhhBCiAcCx8sxQr7BeaNOvjfUqVB+omcz6dIHQ1atXsWfPHvj7+5u9DcYYTp8+DbncgdOYhBBC6hzjIMgMOXJXIWMc7dJ6m8kM5eXl4dq18nu6pKSk4PTp0/Dz80NQUBCGDx8OhUKBP//8E2q1GmlpaQAAPz8/uLho7xg8duxYNGnSBPPnzwcAzJkzB926dUOLFi2Qk5ODJUuW4PTp0/juu+/q/wAJIYQ4DAZhZsh+Ugs1x+n3kXKwzJDNBEMnT55E3759+enp06cDAMaNG4fZs2fj999/BwB07NhRsN6+ffvQp08fAEBqaipEovJ3ZFZWFl5++WWkpaVBIpGgU6dOOHjwIB5++OG6PRhCCCEOjXFMcOWYfqDgsBy4mcxmgqE+ffqAMdNpt8qW6ezfv18w/dVXX+Grr76qbdUIIYQQAQ0qNJM1gMyQI49AbTPBECGEEGIvmtwPxZi90/jphpAZ0j/Gy+9dxplPz8DZ2RllZWVwecgFI74dAWljqRVrWHMUDBFCCCFmem7va4i82ZafLtGUWLE29YNzLg+GWiS3EC48DWxrvA0vfPtC/VbKQhpCYo8QQgixKP/cxvzzfOTjXrt7VqxN/Wg3th1y3E3flSHralb9VcbCKDNECCGEmInT6y/0muw1KCYorFib+tH5sc64ffk2+j3SD5mZmQCAVr6t8PGtjwEAnu6e1qxerVAwRAghhJhJdyVZtocKigsKSKX22VfGXE2Cm+Do6aM4cOCAdkYGgJe0T51E9htS2G/NCSGEECvRZYYYp2kwgZCOVCrF0KFDAQD//vEvSlEKwL6vMKM+Q4QQQoiZdAMu6u5W31Dpj+3Haez3ijoKhgghhBAz8cEQHGvwQXNxYr0AyI5PBQVDhBBCiJl0fYYaemZIEEXY8amgYIgQQggxU3mfITuOACxAMNgkZYYIIYSQhqO8z5AdRwAWIBLr37DMevWoLQqGCCGEEDOJKDMEoEJmyI5PBQVDhBBCiJkoM6RFzWSEEEJIA8X3GbLndIgF6DeT0aX1hBBCSAMionGGAFAzGSGEENKAUTMZQM1khBBCSIOlyww1dIKryew4M0T3JiOEEEKqcO/OPfw++Hd4p3iDMYbA0kAAlBniuPKgMOxQGE6PPI32a9sj/2w+Lr56Efk38+ER5IFWS1pB0kMiWDf3dC4uTrqI/Bv58G7hjc4HO9d39XmUGSKEEEKq8M+3/yAiIQIBmQEIVAXy84vF+VCpVFasmXWJPEXQ6LWPZW3Kws2tN5E0PwkFJwrApXEoVBQi6eMkg3WTP0tGwTFtmdR/U616HikYIoQQQqpQklXCP89zzUOWRyaU0htwEf8ChUJhxZpZl9hfjDW91gjmJZ1JQubNTMG87DvZButmpGbwzws1hVY9j9RMRgghhFRFrzVsfqP5+Ov2f/AtAK7mOqFRVJT16mVlIk6Elf1WQuWpwv+2/w8AEBoSCpW3CrnI5ct5engarOsr8UUOcgAAnwR+gv+i/qufShtBmSFCCCGkCkxT3jt4zJgx8Pb2BgCEh4VBKpVaq1pWp+szpD/EgIe7B5zEwlyLk8gw9+Ls5Mw/P3jooFXPIwVDhBBCSFX0MkPhEeEQPwgCxE4Nu4GFezDEgEa/I7kGBpfZ6weT/DxWPs/Xz7cOald9FAwRQgghVdD/MheMrdPAiTjDe7QxDTMMfoxddKc/z8qnlIIhQgghpCp6X9ycmAOYHQ+qY0G6ZrKaZIYE4xJRMEQIIYTYNv0vc5FI76uTa9hZIg6GfYa2X96OVFWqoJxarTZYV7+ZjIIhQgghxNbpZ4ZElBnS0TWT6WeGtl3chuuq64Jyymyl4cr6sZCVg0oKhgghhJAqCDJDYsoM6QR5ByFCGiHIDHGMg4gJw4ui0iLDlW2omaxhd4MnhBBCqqNiZogAAMQiMU69cgr/Fv0LbNPOe6XTK3C55QLcLC/HaYycMxsKhigzRAghhFTB4GoyXTNZA88MAYC3qzc6NenET7cPaA+pa4Uxg4z1n2a2c4UeBUOEEEJIVfQyQ4JmMqKlf0qMXE1GmaFqOnjwIAYNGoSgoCBwHIdt27YJljPGMHv2bAQFBcHd3R19+vRBYmJildvdvHkz2rRpA1dXV7Rp0wZbt26toyMghBDisKgDdaX0MztGxxkydrponCFD+fn56NChA7799lujyxcsWIAvv/wS3377LU6cOAGZTIb+/fsjNzfXaHkAOHLkCEaNGoXnn38eZ86cwfPPP4+RI0fi2LFjdXUYhBBCHJGpzBA1k2lVlRlihufJli6tt5kO1HFxcYiLizO6jDGGxYsX44MPPsCwYcMAAKtWrUJgYCB++eUXvPLKK0bXW7x4Mfr3748ZM2YAAGbMmIEDBw5g8eLFWLduXd0cCCGEELtw/8Z9nFacRlSvKPj5+wmW3bx+EwknEtCsWTOkpqaiJLf8rvXW7t9ii/TPyZWzV+BW4CZY7pHngcz0TPgFlJ/nstKy8vXp0vqqpaSkIC0tDY899hg/z9XVFb1798Z//5m+y+2RI0cE6wDA448/Xuk6xcXFyMnJETwIIYQ4ljPPnMH50PNwGuaEnYE7cS/pHr9s5ZSVuBRxCb4jfZHTLQe+I33Rem9rfnlhUSF1oK5IL5oQrxaj9EqpYLFnoScOyQ/h9v7bAACVSoWTJ0/yy1VZqnqppil2EQylpaUBAAIDAwXzAwMD+WWm1jN3nfnz50MikfCP4ODgWtScEEKIrSlVlUK1ofzLV66W49zyc/y020Y3OGucja0KDTS4l3/P6LKGzEXuUmUZiUaCy0svAwASEhJQWlIeMJ0+fbquqlYtNtNMVh0V02iMsSpTa+auM2PGDEyfPp2fzsnJoYCIEEIcCCsx7M3bLKgZ/9xJrf1qLBGXQBGoKF8PDAneCfhp8E/AJMoM6fPp6gP5LDm2f7YdRcVF4MChCEW43OcyBp0eBHmWHAAQIA0AAERHRyPJOQl4EA91iu5katP1wi6CIZlMBkCb6ZHL5fz89PR0g8xPxfUqZoGqWsfV1RWurq61rDEhhBBbZeymoR7uHvxz3ejJuR65GHV4FJKTkxEeHo7k5GRMipoEqVRqsH5Dx3EcWs5uiYD/BUChUPDn69uEb5HwUAJ+XPYjAGB38m68+8u7AIBnQp4BrmnXf/fwu/hp6E/Wqr59NJOFhYVBJpNh9+7d/LySkhIcOHAA3bt3N7leTEyMYB0A2LVrV6XrEEIIcXCayufxVz6JtN8/sbGx/P8UCFVOKpUKzpe7szvUXPlNWm+qbuLvq3/j76t/I7som59/IPWANarLs5nMUF5eHq5du8ZPp6Sk4PTp0/Dz80OzZs0wdepUzJs3Dy1atECLFi0wb948eHh44Nlnn+XXGTt2LJo0aYL58+cDAP73v/+hV69e+PzzzzFkyBD89ttv2LNnDw4fPlzvx0cIIcQ2GMsMCUaYfhAMMVElYwlRB+pqmdxlMlYkreCn9e9ZJrh/GV1ar3Xy5En07duXn9b12xk3bhzi4+PxzjvvoLCwEJMnT4ZKpULXrl2xa9cueHt78+ukpqZCJCo/ud27d8f69esxc+ZMfPjhh4iIiMCGDRvQtWvX+jswQgghtqWqAQAfLNe/+SipmfceeQ8veb+E89+dBwA8H/k83n/nfQDA1T+vovB2IQDg+MTjVqsjYEPBUJ8+fYQDMFXAcRxmz56N2bNnmyyzf/9+g3nDhw/H8OHDLVBDQgghdqukBPj9d+DOHbAMZwCtBYvZ3v1AWQYAQKTRdqZmTA0sWWJ8e2UPxsihzFCVPNzK+2O5cC7wc9eONeTEPQhBOMDXzdcKNStnM8EQIYQQUmfmzQPmzMFpGfB/beUYhl8Ei9nmLcDmLQAAzl172yamUQP/+1+9V9XhiPWeG8nAWbuJDLCTDtSEEEJIrZw6BQB49Ulgc2vDb98r/uXz+D5DnLGe1hXQBTlVEty3TF3eAqRrDbKFEb0pM0QIIaTBuCkBRCWGeYCsPt2Bp58GAHATtKkM5iQCfvnFoCzPzw+Ija2TejoSTmw8GLKlzBAFQ4QQQhzfgywEAyAydtPQps2A0dr+pdwLf2nniTlg9Oh6q6LD0msmy/gzA0fDjwIAim8Va2dSMEQIIYTUH8ZVuKRbN1/vAh7d8kovrSfVJvYUazvlaABNgQZFKUXC5d5i4yvWIwqGCCGEOL4HwY6G0xtUUX+x2sg4Q3RpvUU4S50R8mEIlD8pwcqE51TkLkLIjBAr1axcjYKh1atXY9SoUQa3rSgpKcH69esxduxYi1SOEEIIsSRtM5mRzJCxQRcpGLKYsNlhCJsdZu1qmFSjq8leeOEFZGdnG8zPzc3FCy+8UOtKEUIIIXWBmcoMaQybyWyhLwupHzXKDJm68/utW7cgkUhqXSlCCCHEohg/rDTa3GpjsFh0QIT7t+6j9FYpnNXOD4pSZsiSVCoVEhISEBERgaSkJERHR9vMvd7MCoY6deoEjuPAcRxiY2Ph5FS+ulqtRkpKCgYMGGDxShJCCCGW8PLON/HouScN5gdcCsD54POCeRQMWY5KpUJkZCSUSiVEIhE0Gg3kcjkSExNtIiAyKxgaOnQoAOD06dN4/PHH4eXlxS9zcXFBaGgonn4wTgMhhBBiMx5khjpe71LtVW43ul1XtWlwEhISoFQqAQAajXYwS6VSCYVCgVgbGKvJrGBo1qxZAIDQ0FCMGjUKbm5udVIpQgghpC7o9xf6JuIb3Iy5iQVrFhiUW9djHU4POo338X59Vs9hRUdHQy6XCzJDQUFBiIqKsnbVANSwA/W4ceNQVFSE5cuXY8aMGcjMzAQAKBQK3L5NkTQhhBDbxD3oHH1Peg9Pb3gaJ5qfwLYu2wzKbYrZhFLf0nquneOSSqVITEzEnj17cO3aNezZswfnz5+3iSYyoIYdqM+ePYtHH30UEokE169fx8SJE+Hn54etW7fixo0bWL16taXrSQghhNTcg2Yy3ejTnIiDj7cPAEBj5B5kGk5j9EIhUnNSqZRvEgsLs63L7GuUGZo2bRrGjx+Pq1evCprK4uLicPDgQYtVjhBCCLEkXWaIcQxiTsw/r4hxDBxdW99g1CgzdPLkSSxbtsxgfpMmTZCWllbrShFCCCEWpbtD+oMAh4kYnMXaS+hNZYZ0y4njq1FmyM3NDTk5OQbzL1++jMaNG9e6UoQQQkhd0M8M9QjugdaNWhvNDInFYozvML6ea0espUbB0JAhQzB37lyUlmo7l3Ech9TUVLz33nt0aT0hhBDbU6HPEOMY3J3dcX7yebwc/bJB8eT/JeOVzq/UaxWJ9dQoGFq0aBHu3buHgIAAFBYWonfv3mjevDm8vb3x6aefWrqOhBBCiEXoZ4YAQMSJ4O3ubVDOx8OnXutFrKtGfYZ8fHxw+PBh7N27FwqFAhqNBlFRUXj00UctXT9CCCHEYozehNVIWoATUefphqRGwZBOv3790K9fP0vVhRBCCKkbug7UukEX9QIgo4FPjdpNiL2qUTC0ZMkSo/M5joObmxuaN2+OXr16QSwW16pyhBBCiCWJKjSTaWcalqPMUMNSo2Doq6++wr1791BQUACpVArGGLKysuDh4QEvLy+kp6cjPDwc+/btQ3BwsKXrTAghhJinQmZIPxiizBCp0cs9b948dOnSBVevXkVGRgYyMzNx5coVdO3aFV9//TVSU1Mhk8kwbdo0S9eXEEIIqTHKDBFjapQZmjlzJjZv3oyIiAh+XvPmzbFo0SI8/fTTSE5OxoIFC+gye0IIITaFzwyJqsgMUSzUoNQoM6RUKlFWVmYwv6ysjB+BOigoCLm5ubWrHSGEEGIB+Rpgc/BjEOm+9vSDnYrfhBzovmQNTI2Cob59++KVV17BqVOn+HmnTp3Cq6++yl9ddu7cOZu7ERshhJCGaf29SPjfnMFPa1B+C46KmSEN00ClUtVb3Yj11SgYWrFiBfz8/BAdHQ1XV1e4urqic+fO8PPzw4oVKwAAXl5e+OKLLyxaWUIIIaQmNNlywbSqVXmwI+klESw7jdNQKBT1Ui9iG8zuM8QYQ3FxMX777TfcvHkTly9fBmMMrVq1QsuWLflyffv2tWhFCSGEkJoS6f32/+Kh+Yhf/Qs/7fuIL1qdaIWXH3sZaao03JXfxemo01aoJbGWGgVDLVq0QGJiIlq2bCkIgAghhBDbVN4U9uUvC+Dv7y9YKussw6qkVVAoFIiKioJUKq3vChIrMruZTCQSoUWLFsjIyKiL+hBCCCEWx+kFQ76+vkbLSKVSxMbGUiDUANWoz9CCBQvw9ttv4/z585auj0mhoaHgOM7gMWXKFKPl9+/fb7T8pUuX6q3OhBBCbAQr/7oTO9HdEYhQjcYZeu6551BQUIAOHTrAxcUF7u7uguWZmZkWqZy+EydOQK1W89Pnz59H//79MWLEiErXu3z5Mnx8yu8+3LhxY4vXjRBCiI1j5ZkhkYiGlyZCNQqGFi9ebOFqVK1iEPPZZ58hIiICvXv3rnS9gIAAkylRQgghDQOnHwyJa3WPcuKAavSOGDdunKXrYZaSkhKsWbMG06dPr3JgrE6dOqGoqAht2rTBzJkzq7zKrbi4GMXFxfx0Tk6ORepMCCHEmsq/K8RiygwRoVq/IwoLC5GTkyN41LVt27YhKysL48ePN1lGLpdj2bJl2Lx5M7Zs2YKWLVsiNjYWBw8erHTb8+fPh0Qi4R90o1lCCLF/wswQBUNEiGOMsaqLCeXn5+Pdd9/Fxo0bjV5Vpt+3py48/vjjcHFxwR9//GHWeoMGDQLHcfj9999NljGWGQoODkZ2drag7xEhhBD7Ed9kKULvtAEAxKii4OpLn+eOLicnBxKJpFrf3zUKj9955x3s3bsXS5cuhaurK5YvX445c+YgKCgIq1evrlGlq+vGjRvYs2cPXnrpJbPX7datG65evVppGVdXV/j4+AgehBBC7Jt+hwpqJiMV1ajP0B9//IHVq1ejT58+mDBhAnr27InmzZsjJCQEa9euxZgxYyxdT97KlSsREBCAJ5980ux1T506BblcXnVBQgghjkXv0noRXVpPKqhRMJSZmcnfhNXHx4e/lP6RRx7Bq6++arnaVaDRaLBy5UqMGzcOTk7Cqs+YMQO3b9/mM1OLFy9GaGgoIiMj+Q7XmzdvxubNm+usfoQQQmwTR5fWk0rUKBgKDw/H9evXERISgjZt2mDjxo14+OGH8ccff9TpZex79uxBamoqJkyYYLBMqVQiNTWVny4pKcFbb72F27dvw93dHZGRkfjrr7/wxBNP1Fn9CCGE2KgHwZCaUwNVXIVMGp4adaD+6quvIBaL8cYbb2Dfvn148sknoVarUVZWhi+//BL/+9//6qKuVmFOByxCCCG26WfZTwi+2wJlojI8WtgLcHGxdpVIHTPn+7tGmaFp06bxz/v27YtLly7h5MmTiIiIQIcOHWqySUIIIQ5KpVIhISEB0dHR9Xrfr3XvrkPu/lyUlJQgLCsUAKDhNPW2f2I/zA6GNBoN4uPjsWXLFly/fh0cxyEsLAzDhw9H+/bt66KOhBBC7JRKpUJkZCSUSiXkcjkSExPrJSA6uesk5AvkkEN40YxapIYqKwvSgIA6rwOxH2b1ImOMYfDgwXjppZdw+/ZttGvXDpGRkbhx4wbGjx+Pp556qq7qSQghxA4lJCRAqVQC0PbtVCgU9bLf9KR0o/PvBP2FU6dO1UsdiP0wKzMUHx+PgwcP4p9//jG4rcXevXsxdOhQrF69GmPHjrVoJQkhhNin6OhoyOVyKJVKBAUFISoqqn52rNcbdm3oWjxxfxti1CXocyMHqqhZ9VMHYjfMygytW7cO77//vtH7e/Xr1w/vvfce1q5da7HKEUIIsW9SqRSJiYnYs2cPzp8/X299hpi6PBpq3aU14lqGQFaYw9eJEH1mBUNnz57FgAEDTC6Pi4vDmTNnal0pQgghjkMqlSI2NrZegxCmKQ+GGjVuBDc3t/KFdGk9qcCsYCgzMxOBgYEmlwcGBkKlUtW6UoQQQkht6AdDtb8lOXF0Zr1F1Gq1wcjP+sRiMcrKympdKUIIIaQ2DIIh84fUIw2IWR2oGWMYP348XF1djS7Xv9s7IYQQYjV6wwlxogrNYtRMRiowKxgaN25clWXoSjJCCCHWpt+BGhwoM0QqZVYwtHLlyrqqByGEEGIx+neaoswQqUqNbsdBCCGEVKYstwz3Nt5D6f1Sg2WSXhJIYiR1un9BZog6UJMqUDBECCHE4pLfTcad7+8YX8gBXa92hXuEe91VoGKfIf1mMsoMkQooXiaEEGJxeWfzTC9kQP75/Drdf6XNZIRUQJkhQgghlqeXiIncHAmIgPtb7uPuz3e1i+u4QzNdWk/MQcEQIYQQy9OLPRo91Qgcx6HwSqHR5XWiskvrCamAmskIIYRYnH5mhtP10dGPSTSoUzQCNTEHZYZIg6JSqZCQkICIiAgkJSXx/0dHR0MqlfLLddOEkBp6EIswjkGlUkEqlaKwqDwzVJtmsrLSMsQPiIfPaR8wxsBxHEqcSyBpLUHj9MYozCyEpKD8ajVBB2rqPE2MoGCINBgqlQqRkZFQKpUQiUTQaDT8/3K5HP/++y969OgBpVIJuVyOxMRECogIqaGyUu2tmTRMg8jISPz7779YuHAhnsNzAID8nJp3oD6w/gCa721uuOAuUIQicODgDGd+dqG40LAsIXooeUgajISEBCiVSgCARqMR/K9UKrFp0yZ+uVKphEKhsE5FCXEABXkFAAAGxv99ZeVm8ctTklNqvm1VAf+80LkQak4tWK6GGplOmcj0zMS/Lf/FxaCLlBkilaJgiDQY0dHRkMvlAACRSCT4PygoCCNGjOCXBwUFISoqyjoVJcQBeLh7ANAGQ7q/Lx8fH355aGhojbetP6DiyqCVuC69LliehjQM7zgcT7/9NGaOnolmDzWr8b5Iw0DBEGkwpFIpEhMTsWfPHly7dk3w//nz5xEWFsYvP3/+PDWREVILYpEYAODk7MT/fb034z1+uaeHZ423rd85OqZ7DNw83ATLg5oEYe7Hc/lpL08vurSeVIr6DJEGRSqVIjY2FgAQFhYm+L/ickJIzek6SIvFYv6HhYeXh16BWmxbLxgKCAwAJ67Q9OUK3GP3+EkRp/e7n5rJiBGUGSKEEGJ5ukvn9WIPTi8QEVz6bqaKd6SvOI5Qak4qlhxfoleEAiBSOQqGCCGEWJ4uXtH/lhEZWV6TTeuPYSTm4OYsbCZjnHDjrRu3pg7UpFLUTEYcFmMMqj0q5CkquUdSJVyCXNB4eGOI3cUWrhkhjuV8+nlsv7odGlY+kmK7vHbwgAdKNaX4/PDnAICApACEQdss/delv3D/8H14unhiZORIBHgGVHt/gmBIxKGJbxMUoPwKs0ZejTCz50wAQExwDDoHda7V8RHHR8EQcVjZh7Nx9rGztdpGUXIRQmeFWqZChDig3OJcxKyIQV6J8EfHyryVCEUoCjWFeO8fbcfpgZcH4k28CQD49cKv2OG6AwCw9dJW/DP2n2rvs2Iw5CQWfpUFegfi434fV1iJMkPENGomIw4r/2zt74pd6Z23CSG4nnXdIBACABEz/HrRb77SX37u7jmz9qnfZ4gTcQbfZHQvMmIuygwRh6U/3H+T/zWBbx/faq1XllmGyy9efrCROqgYIQ6E6f2R9A/vj8ldJgMAvOO9gQzAw9UDW0dtBQC4iFyAP7Rlp3SeggT3BNzNvyvYRrX2WSEzZBD80M98YiYKhojj0vt89enig8ZDG1drtWJlsdFtEEIM6f/oCJeGY2iroQCAY87HUIhCuDi58POUTZS4DO0PjY6yjvAq9NIGQ2aOAaRfvtqZIWomI5Wg+Jk4Lv3PV3M+//TK1uZmkoQ0BPqdpvUvYeezN/p/exXuWq+71L5WmSExZYZI7dnNW2b27NngOE7wkMlkla5z4MABREdHw83NDeHh4fjhhx/qqbbEFgjGMTEjGNIfCwUa0+UIIcJARvC3o4uF9AIV/eeMMT54MjszVJM+Q5QZIpWwq2ayyMhI7Nmzh58Wi01f8pySkoInnngCEydOxJo1a/Dvv/9i8uTJaNy4MZ5++un6qC6xNv1YyJwOlRYaC4WQhkA/kBGM9Gxk0EVLZYb0f6RQnyFiCXYVDDk5OVWZDdL54Ycf0KxZMyxevBgA0Lp1a5w8eRKLFi2iYKih0Pt8PXf+HMIfDkdSUhKio6MN7jumUqmQkJCA6OholGWXlW+CMcEyul8ZIQ8UFQGFhWA52QAAzyJPZJy/jWQvBVJvpEJcpP1bYUwDqFTadQrLxwK6euwMXJo7wa3EDRpndXmZashXZgPQ3lSZKy0B1GWC5ZzGyPbKHpShzBAxwq6CoatXryIoKAiurq7o2rUr5s2bh/DwcKNljxw5gscee0ww7/HHH8eKFStQWloKZ2dno+sVFxejuLi8A21OTo7lDoDUL71g6ONPPsaheYeg0Wggl8uRmJjIBzYqlQqRkZFQKpUIDAyEN/PGT/gJAFCYX8gvq7geIQ3Wzz8Dr7wCFBZCHQR87v45Hk56GACQihwAvlA/+AMU3bsL+PUCAHB4FMAH2m2s9sA3+A5lojKseyQe+MCvWrte3nQWmt/qw09zy5eBu/UYgG7lhY7+B/g9XLtjJA2K3SQTu3btitWrV2Pnzp346aefkJaWhu7duyMjI8No+bS0NAQGBgrmBQYGoqysDPfv3ze5n/nz50MikfCP4OBgix4HqT/6fYYYGDQabW5dqVRCoVDwyxISEqBUKgEAd+/eRVp6Gr9MeUfJL6u4HiEN1k8/AYWFAABNWTAfCBnjgky954af104aJzyhGFKt3d708BMEQgDgW5op2Iep/fCq2bpAGha7yQzFxcXxz9u1a4eYmBhERERg1apVmD59utF1uArpUF3bdsX5+mbMmCHYXk5ODgVE9orpP2UQiUTQaDQICgpCVFQUvyw6OhpyuRxKpRIymQweGg8gXbtMJpNBnqtdVnE9QhqskhL+KesQDezWPs91S4dHUXL5MhQi2/k3lPZ+FM7OzvBlQLMr+3Hjugc0Gg3KnDrCtcwNTmpnlD76qMmMvU5xmQu/LwA4FbYSr4Y3QmnzZNw5shNMIwEnykVw12uAb5zhBtzdgddeq9WhE8dkN8FQRZ6enmjXrh2uXr1qdLlMJkNaWppgXnp6OpycnODv729yu66urnB1dbVoXYmV6AVDs2bPQvjYcCQnJyMqKkrQ1CWVSpGYmAiFQoGoqCioc9Q4H3oeAODu5i5YRk1khKD8yiwAmulvAru1/XGud72LgStHITk5GeHhur+3qXB+8HfDAQgHIFWpoFAokDX6PgLvuYGDCM67dxvZkZDm9BWg0x0AwPno8xi/ezHcpFK4Aej0YJudoqLgQ3+nxEx2GwwVFxfj4sWL6Nmzp9HlMTEx+OOPPwTzdu3ahc6dO1f564M4Bv2rXNp3aI/GYY0RFhZmtKxUKkVsbCwAoEys1xmTCZcRQipQlz/18vZCWFgY/3dW1d/bBtEGAMZv3WGU3g8cN3c3gx819HdKaspugqG33noLgwYNQrNmzZCeno5PPvkEOTk5GDduHABt89bt27exevVqAMCkSZPw7bffYvr06Zg4cSKOHDmCFStWYN26ddY8DGIBCqUCe5L3VDk2SVByEIKhbeLccmkLsg5nVWv7ogIRuqALAOB65nXsOLzDoIy3qzdGRo5EI49G5lWeEEegN2aPwWjQ5mzmwb3KOFa99XT9/gDYUY9XYg/sJhi6desWRo8ejfv376Nx48bo1q0bjh49ipCQEADazq2pqal8+bCwMPz999+YNm0avvvuOwQFBWHJkiV0Wb2dS89PR7fl3VCqKa2y7JikMXgJLwEAVp1dhSPFR6q1D7cSN2zHdgBAUmYSf8ftiv688if+HvN3NWtOiGPSqMsDFCYyc/BEM4Mh/cEWzRpVnpAq2E0wtH79+kqXx8fHG8zr3bs3Xf3jYK5kXKlWIAQIP2DNGdSNCXtem3Qu3bw7bRPiMPQzQzUc6R0oD4aq20wmyAZTMEQsyG6CIUIA4YfhkJZDML7jeJNlXTNcgX3a5x/0/gBlvctMlhUoBjBP+7RdQDv+jts6L/7+IjILM+m+ZYQABqNBm8PsZjKmvzOzdkVIpSgYInZFP2vzkP9D/N2wjbnufx3XcR0AENMsBv6tTF9FqE9TrMFBHAQANHJrhP6t+guWv/Y3XZpLGrgKmSH+Bq1m9uPRNatRMxmxNuqCRuyKoLNmFZ+GNU6p65etJPlj9v2UCHFA+p2aa9pniJrJiLVRMEQcVx3dqLWyQTsJaRBM9Bkyt5lMF9DU6Goy+jMkFkTBELEr+tmYKoMSvc9Ncz449bcr6BxasS7UZ4gQwThDZjeT6foMVfMPVPD3SN9exILo7UTsii00k1X3g5sQh6WXGRJka8zdjKi8maw6Py5qsy9CKkPBELFbVWaG9GMhc5q2qgqGHmyL+gwRAmEGtoaZIe1EdVbQe06/SYgFUTBE7IpZAYh+UTPe6YJmMmoKI8SQhfsMAZU3SRstQ99exILo0npiVxhjcC92R+vbrVGaU4oLdy5AqVRCLpcjKSkJABAREQGlUgnvc97lK5r7K5IDwICcOzlI3ZoKL28v5OXm4erVq2iT3Aaeak+oWqgsdlyE2BMNY1D4N4fKTYqUvSlohVbaBbXIDN05dAdN+zYFAJRmlEJ5UImrV69CLpfjjuoOosZEITc3t7yZmjJDxIIoGCJ2hRUw/PL1L/At8AUApCMdYoiRjnR4w1swrwAF/Hp5eXmQwow7WT8IhribHJKHJfOzxRDjXbwLANj78F7gnVofEiF2Z1VpD4RljIAzgFZry+eXqqs3OryORq+N7Vq/ayj4tAAhL4TgaMRRsELG/207wQk/vPMDtgVvwwIs0K5L/YeIBVGikdiXC+ADoepSQ40r+VfMWoc1qzpl3/5ye7O2SYijEGW0NTq/yL/IrO3cbnxbOL3tNrIPZ4MVGv79tStuh+ysbH66oKjAoAwhNUWZIWJf9C7jPe9zHqdyToFBOwKurj+R7rnu/xT/FGx5bItZu2m9pTXm9J6D4txieHt5Y/z48YiPj0duXi6ednkaniWedFUZabD0B0lcHboaYMA9t3v4v/f+z6zt/DPiH1xxu4KX9mpvqOzt5S3okH0CJ9ACLeALXzhxTvD19QXua5e5e7rX9jAI4VEwROyKfgdK9SNqzPx2JpKTkxEeHo4zZ84AADp06MDPS05ORlRUFKRSM5rIAMg6yTDvxjwoFAp+/dC5oVAoFMganqUNhqo5UBwhjkf73tdAgw/3fljjvzO1jxqbu27mgyFnsbPgbzx6SjRc/3FF6aVSeLh64JOPPwFGa5c5OdHXF7EcejcRu6L/QSmRShAWFoawsDAA4P/Xf64/z1xSqRSxsbEG05ux+UFlarxpQuyaLivKOCb4GzR7Oxwn6ETNNEyQGXqo1UNIO5KGUpQCZYCHu4f2OUCdPIhF0duJ2JcajiptUfzFLJQZIg3Ug/hFME5QDXDgoOH0/qg1MLhUn3N6EHipGXWaJnWGgiFiX2oxwBshxDK4B32GajvwaMXMUGlZKfKL8vnpgrICqEUPOgoyoKCwQLAuIZZCXyfErtjCoGv8hzc1k5EGSheGWDozdCT1CF776zV+evru6TiiPMJPz9g5o3xl+vYiFkRvJ2JX9IOh2n4Q1xY1k5GGq7zPUG0EeQcJmrtFTCS4Uk3DaaDmyi8hnbl5Jv/c09WzVvsmRB91oCb2pYa32LBoFXR32qaryUhDxSxzf745febAReTCT0tdpWjXuB0/3VbWFl53vIDr2mm/fD9+WYeIDrXaNyH6KDNE7IveOEPUZ4AQ6xAxy2SGWvi3QPxT8fx0K79WeKPLG/z01O5TMXLxSHi284RzgDP/kDwiQcgLIbXaNyH6KDNE7IrgxqkUyhNiJZYJhngiaC+OMHI1me8jvuhytotl9kOICfR1QuyLXmbIWn2GqJmMNHgWaibT0d3tvuI4Q/QNReoLvdWIfbGBD0o+GKIO1KSB4t/5lswMAUYzQ4TUBwqGiF2xqWYyurSeNFC6cYY0FgqGKDNErI3easS+6DWTWSsxQ81khOhQZog4BgqGiN0oulEEpxV6ff5t/N2rUqmwZ88eqFQqh96nPdSlNmpyHCqVClu3bsXWrVvt/viN4Sx0NRm/vQdBT96tPNz54075ggd/447yXiK2i64mI3aBaRgUfRVwSSkfk6S0rLTe66FSqVBSWvKgUtppY3fqVqlUiIyMhFKphFwuR2Jiotl39K5J3ep7n/ZQl9qoyXGoVCq0bt0ad+/eBQDIZDJcuHDBLo/fGJVKxSeENBwz+TdgDj7bmsWhcF8hPz8/P99h3kvEttn4b2tCtDSFGpSklJRPQ4Orsqv1Xo+EhATBFTQKhcJkOaVSCQBQKpUmy1m6bvW9T3uoS23U5DgSEhL4QAgA0tLS7Pb4jUlISNBrImYWObaytmUG80pQgmS3ZId5LxHbRpkhYhcE9yQDMOZ/Y/BKt1fqvR7R0dG4zd0GoG0qiIqKMllOLpdDqVQiKCjIZDlL162+92kPdamNmhxHdHQ0AgMDBZkhez1+Y6Kjo/EPdgDQZnQscWwdt3XE6NajkZ+ZDxEngoZpoApU4fBjhwHAId5LxLZRMETsg14sdCLiBNKkafDw8Kj3akilUji7OAPQXlpvKl0vlUqRmJgIhUKBqKioeknrW2Of9lCX2qjJcUilUly8eBEHDhwAAPTu3dtuj98YqVTKDyvBOGaRY/ML8MP6a+uhUCgQHh6O5ORkwfl2hPcSsW0UDBG7oJ8Z0t3l2mrj/HC6/yrfv1QqRWxsbD1UyLr7NMWW6lIbNTkOqVSKoUOH1k2FbIDu0npLDboICM9zWFiYyWWE1AW76TM0f/58dOnSBd7e3ggICMDQoUNx+fLlStfZv38/OI4zeFy6dKmeak0sRm/sEWvfrZ6Qho7PDNFgW8RB2E0wdODAAUyZMgVHjx7F7t27UVZWhsceewz5+flVrnv58mUolUr+0aJFi3qoMbEko5kha92oVZcZonGGSENHfwLEQdhNM9mOHTsE0ytXrkRAQAASEhLQq1evStcNCAiAr69vHdaO1DkjmSFrNZPRr2HSEDENQ1m29qovkUY3ArWmslUIsRt2EwxVlJ2dDQDw8/OrsmynTp1QVFSENm3aYObMmejbt6/JssXFxSguLuanc3Jyal9ZUmuUGSLEekrul+BUzCkUXtOOASSB/4Ml9MOAOAa7aSbTxxjD9OnT8cgjj6Bt27Ymy8nlcixbtgybN2/Gli1b0LJlS8TGxuLgwYMm15k/fz4kEgn/CA4OrotDIOayoT5D1s5MEVLfMv/O5AMhwXyvTCvUhhDLs8vM0GuvvYazZ8/i8OHDlZZr2bIlWrZsyU/HxMTg5s2bWLRokcmmtRkzZmD69On8dE5ODgVENsCmriZ7gDJDpKHQlJT/GvFs64njeftw1zMPf3bZhEmYbMWaEWIZdhcMvf766/j9999x8OBBNG3a1Oz1u3XrhjVr1phc7urqCldX19pUkdQFY32GrNVMRkhDo5eMbTqtKcafm4kLvqXwKqG/QeIY7CYYYozh9ddfx9atW7F//36DcSiq69SpU5DL5RauHalr+pkha3dgtnYzHSH1Tr+fNFceG1EoRByF3QRDU6ZMwS+//ILffvsN3t7eSEtLAwBIJBK4u7sD0DZx3b59G6tXrwYALF68GKGhoYiMjERJSQnWrFmDzZs3Y/PmzVY7DlJDNnQ1mQ41kzmurINZuLX4FtT5aott0z3CHaFzQ+HSyKXqwjaGMb0fAHpve/pdQByF3QRD33//PQCgT58+gvkrV67E+PHjAWhv4peamsovKykpwVtvvYXbt2/D3d0dkZGR+Ouvv/DEE0/UV7WJpeh96NrM1WT0u9hhXZl0BQUXCyy6TRVUcG3iipAPQiy63XqhHwtxHF1DRhyO3QRDgl8mJsTHxwum33nnHbzzzjt1VCNSnwTNZNYeZ0j3c5i+ERxWSVqJXW23zum/1/WuQaafA8RR2E0wRKqWcywHKYtTkHE7A15eXsjLyxP+X5aHlu+1RJN+TSyyP5VKhYSEBERHR9fJzRPv5t3F5/9+jvzEfPT6sReaQFtvWxroTaVSGT12/XMDAAkJCYiIiEBSUhL/vyXPW8XXwtj0/v37AWizq3Xxeun2qX98QN0ce2XvvYr1qNE+dfFuEEO7/9rBV+qLLFUWTp8+jbCwMJw9dxYA0PORngbLUlJS0LFjR/hKfQEAykNKJA1M0m5PY6cRtF61ExMTrd5vjxBLo2DIgSQ+n4jiq8UQQYQCFBj9f/e+3RiSPqTWX4YqlQqRkZFQKpWQy+VITEy0+Bfsl0e+xFdHv8IXq75Ak5TyAK5MrB0F11nsbNH9VZfui4BjHCIjIw2OXf/cBAYGguM4pKWlQSQSQaPR8P9b6rxVfC3+/fdf9OjRQzAdExODu3fvAgBkMhkuXLhg0ddLvw6646urY6/svWesHjXZp0atDbhT76RiTMwYwTnlOI7PVMtkMvz333/8sor7AoCR40diPuYDAIqLio3v0MbpZ+Y/+/wzlL2m/RukzBBxFHY56CIxruhmUZVlpGVSKBSKWu8rISEBSqUSgLavliW2WdHNnJsAgEY5jfh5BS4F2Nd2H+RecjzZ4kmL77M6NBrtF6UIIqPHrn9u7t69y3f2162n+99S563ia7Fp0yaDaV0gBABpaWkWf73066A7vro69sree8bqUZN9qtXajtMMzOCc6gcGaWlpgmUV95WQkIB79+/x5e+llz+3K/oXMFBeiDggCoYciOjBy3kDNzCKG4URGMH/fwd3AABiToyoqKha7ys6OpofoiAoKMgi26xI95ErYuVv01eDX8Wqj1fhxtQbCJZYZzBMTlz+e9jYseufG5lMBplMBgAQiUSC/y113iq+FiNGjDCYDgwM5MvLZDKLv176ddAdX10de2XvPWP1qMk+xSIxAO17sOI51e+4L5PJBMsq7is6Ohr+jfz58o38ywN7u8L0nzK9W9JYpzqEWBo1kzmSB7/emkU0w9HdR5GcnIzw8HAkJyfD9RVXlCWVwcfbxyLNI1KpFImJiVAoFIiKiqqTPii6X+C6S9iZL8PpE6frZF9m0WsbOHfunEF9Kp4bAFAoFPxrofvfUufN2GtRcfrixYs4cOAAAKB3794WP4f6+9Q/PsDyx17Ze89YPWq0zwd/S6GhoTivOG+w3TNnzgAoP5fGjl23r63btuLKI1cAAK7O9jmgq3427IMPPsCkggMALDfsACHWxrHqXKbVgOXk5EAikSA7Oxs+Pj7Wrk6lDjgfACtj8IryQueEzoJlxyOPo+BCAcTeYvTM6WmlGppn1K+jsDFxI35Z/AvkWXI4BzqjR1oPa1cL8RHxCE0OBQD0uN+jRvlVTszByYd+i9iqgx4HoSnUwLOdJ7qc7VKrbeUn5uNE2xMAANkEGVqtaGWJKtarm1/dRNJ0bSfwNhva4JGjTXBVUga/Ig4Z823nggZC9Jnz/U2fxg5Ed6UKJzLs1qibZ09Xs1TMDBk7Lmv7t9G/NV638fDGiNwUacHaEEvh/04s0ZFAfxv28+cnpF9vzn4PgxBTqM+QI9H9QDP2qooqlLEDBn2GbCQWyvHNsch27v16D8V37PPqIof34NveIgN76m3Cnn6MCFQIhsqf2sgfJSG1RJkhB6Hf2llZZsieftLZamZo55CdSCtLg6RAgrgWcWavn3c6DyVK7eB7+ncDJzbEgjffErxv7fTl1g/itCNQ29EHCSHVQMGQnStVlyKvJE/wYVWGMqgKVYJyavbgUmENQ1FZEdyc3Oq1njWhYQ9uu6G7B5iN5DHvB93H/GHzwYHDu7PeNXv9C2MuIP2XdO2EnX45OjpqJqvAVGbIXo+HkAps5OuF1IRCqUDTr5rCb4EfGn/emJ9/9M5R+C3wEzxOp58GAJSWlaLxwsbYcnGLlWpdfRWbyWwlM6RT01/H+sdht80mjs6CzWT627Db17tinyHb+lMkpNYoGLJj68+vR3q+NsOgPxYPM/Jzjb+5KeOQV5KHFadW1E8la6FiM5mtvFtr3U9C/zjo6mTbZMFmMkfIDJlqhqeYiDgKaiazYyXq8ps+dmvSjX8u9ZAirrmwL4vEXQIAEDOxwbq2ylYzQ7XNFugP2mi3mQJHp3tZLBCAO0QmkK4mIw6OgiE7pv9rbWHsQhRBezuO9vL2GDtmrKCs4jsFcpIeXAXFhOvaKlvNDNWa/nFQnyGbI8iCWPhqMrt9vU1eTUaIY3CUr5cGj9NrxDeaQdF7pUVMxHdOtmU2mxnS+wqoSVDpEJkCR6b/p0HNZAAqvE8pM0QcEGWGrOWJJ4BLl2q1CfZwBtD6wcToMQCWa5//ewgIf0pQlrvzLoCW2ueMg+boEWBOeK32X9dYvzQgWC/QS7oKhD9j3UoBQNwdIODB84hwmP2Nef95AH21zwc8CbjetGDlSG0xJgL/t6RIAMKH12p7XJkvgC+12/57OxA+qFbbswrVIADazxTupZeAZ7Sd3ehqMuIoKBiyltu3gZSUWm2C6Y3qz91JK39emG9k24X8MxETgRUV1Xr/dY09qDKfGSottok6c0Xlz/1GXjc7efDyrhwMOKV93vuxO0gJtP4xkXJitRibFmmfH21ciEdH1u71keb5YcV32ud/Ni3AWw+nYMsGwKO0lhWtV1nlT++mlV9NJqLGBeIYKBiyFj8/ICCg6nKVYB65AArRNrUtCjNnlC9wcQF8hdvmVE7Agw/fpT8txZ6Yr4GAdLP3qWEMJSUlD3bjAhHHQcMYSktL4SQWo0ythpNYjNKyMgCAs5MTytRqODs7C8qamtbnV+iDxStfg3upu3aGWAz41+6cWYIvywKgPQdZ7uavX+hU3g6T5yyCqgbbIHXHuaz8C75UxGr/+qjL0yfdrvRCgfOH+CtqKUakiE2uUtnfRWVqsp6xv1/d3ywAlJaWQlPsUf57SuLzIAjSgPP0qnbdCLFlFAxZy759td4E+2sKcHIpRh8eDVYSxM9XP9EH2DpNUFbzhALYru1A3fxucxSkTgTuvlH9fakZbmy4gXmvzENuXi4AwNvLG5MnT8b333+PnNwciDhtXyQO5SPU6ub5ePvg1Vdf5cvqT9/OvY2LgRdx8uJJwV3FO8V+iQ43OvDTClERml+6ZPW71n9w8z9k7X4b6bnpSLmeAnWZGmInMZo1a4bU1FR+Oiw0DCKx4S9nH4/yGwaKskSAKyotr0+j1vD7rKjiNvTL6pYBMFi/uvuuqj41OQZT61RV94rnu7LzYGx7AEyeQ3FZeZDi5uyGh/wfqtWxu3m4QcNp+Axnv8R+uPFpBPD+OKPbValUiIyMhPLePcjlciQmJlbrPV+T9fTXEYlE0Gg0/P+BgYHgOA5p9+5hPIqgq23e8p/AbvUCslMBd9sfvJWQ6qBgyAF4Fnnyz6/jOoL7BRuUyXw0E/e334cMMgCAc56zWfu4t/Uero+5jmfxbPnMPCBrQRZGY7R22lj/Ad283AplK0xvv7sdCoUCsbGx/Kquha788xKUIL40Hs0VzQVlrKF7cHf8O+Ff7NmzB/2n9gcAqKHGpM8n4d3F7/LTP+z5wWhdryVdw60DtwAA3CauyvL69PdZUcVtVKzfD3t+AGPMYP3q7ruq+tTkGEytU1XdK57vqo6n4vZMUUMNMcqDoYe8HsLl1y7X+tj/SfwHWFU+rbqhMloOABISEqBUKgEASqXS4O/CkuslJCQgR5kDL3iVdxx/8H/+3XwAgBe84AIXfp1rSdeAB3+adG8y4iiowdeOGdzIFMBM+UxEPRdlUDb6hWi8F/gePy3mTKfojck/m1/DWlZPS+eWiIoS1pvTlH/QjnUZi9SgVIMy1hQdHQ25XA4ACAoKwogRIwTTJuuqf2XfgwmZTFatY9Pfp+hBfw3d/xX3WbF+UVFRRtevtK5m1Ke626nOOlXVveL51r8E3ti51F9XJpNBJtP+KKh4DmUyGeQBcn49bx9vixx71FdR+O6h7/hpP6mfRbZbm/WYhkEyR4I/8Sf+qOIf/yMGQIsWLexiaA5CzEGZITtmMA4PgLPnzxpNjUulUiQoEnCuyTkA5V/C1d9Z+dOiUUVQh6kRGRkJTy9P5OflIyk5CbJAGdLupkEWKMP169cBAKGhoUi7m4aI8AhBWd104ohEoAyIbBNpWG+9fS5fvhw9BvawehOZPqlUisTERCgUCkRFRRmdNkb/0vqvZV8DboCnlyeu9b1Wrf1u8d+CQo9CuLi4oKSkhP/f3d3dYBu6svrLKq5vbD1zGNuHJdaprO7u7u64/9R9wbEUFWp7tps6l/rrAkChp/FzCDVQlK7dlpOL6Y/I6r7eurJDhw0FPtNOOzuZzsyas93arJd3Ng/5h83/keP3kB9wQ/vcIuMwEWIDKBiyY8YyQ35+pn9xSv3LPxw5M28upP9LsOvErpDGlm+rMRojFKEAgEhECv6v+Fy/LKAdjZmVMeOZKr1gqEcP2wqEdKRSqaApouK0MSLP8tdLnKY9bt2AmdXFgUMpSgX/m9qGsWXVWc/c+tTkGKpax1Td9efpjkXXvFXZNvXXrc45FHtUnkGtzuut4+5W3hO7qvGlzNluTddjxeV1cA1xhWcbz0pKP9j+Y1J4tfUC202ZIeJYKBhyANUeobk2Ix+bGIG2tjiRtrO1sS+HKgeStFOBzwXi/tb7KLxSWHVhYjXOgc5o8noTi23P1m7Dov8Dp9HQRmixuIXZ26A+Q8RRUDBkx3QfZtUdoVlwg0Vzbztt6VF5dXQBWmWdr+FYwZBHcw90OdPF2tUg9czm7l6v//dlZnMX9RkijoY6UNsxXTNZTTJDtWkms2hm6MGHsNHMkMYxM0OkYbK527BYINtLfYaIo6BgyI6ZnRnS++DSDzSqtzPj26k13TvQWLOd3j5FNNItsXOCZjK1fQdDjO5ORhwMfcPYMbMzQwDUnO6eQjUPhizdZwgwkRly0D5DpGESDMhoA/dJtkS2l/oMEUdBwZADMOuu7roi5v6w0y9vyXdNZfVx0D5DpGGyuWYyvYCM+gyRho46UNsxg3GGqhGkaDgNxBCDqRn2JO+p9r5EKhE/NtHJOyeBZLOra5QYYnDgUFBcYFCfsgf3NwNQo1tFEGJLbC4Yoj5DhPAoGLJjFccZqk72hHHadcrKytD/Z+O3dTBmcuJkjMAIAMDUnVOReDHR3OoatbV4K3zhizs5d/Dcz88Jln1e/Dn/nD50ib3T7zNUUlYCVaHpW3LUh7yiPP55kbrIrPpomA208xFiQXYXDC1duhQLFy6EUqlEZGQkFi9ejJ49e5osf+DAAUyfPh2JiYkICgrCO++8g0mTJtVjjetOTfoM6cqY3WdIf7+c5X7Vajjth6qx+uj3R6DMELF3Iq78PbwveR9eWfCKFWsDdEzpiK/wFQDg62Nf46cFP5m9DeozRByFXQVDGzZswNSpU7F06VL06NEDP/74I+Li4nDhwgU0a9bMoHxKSgqeeOIJTJw4EWvWrMG///6LyZMno3Hjxnj66aetcASWZe7VZADg6uwKVsIQ6BGImT1nVntfLc6WD8g2vuN45LTJMbO2xnku8QTyAamr1KA+4b+H88+pzxCxd36efsiH9vYXtfkxYin6dajpDxyZl8xS1SHEquwqGPryyy/x4osv4qWXXgIALF68GDt37sT333+P+fPnG5T/4Ycf0KxZMyxevBgA0Lp1a5w8eRKLFi2yuWBIpVIhISEB0dHR/G0nNKUaZNzKwJnEM4juEW30dhRitRg+hT4AqveBJhKJoIYanmWeGOc3DtdTriOqdxR/Gw9j9QCA8xvO4z7uAwBeefgV+DzsU6vj0027ubihDGVw07hhQtgEJCUl8WVOep9EHrSpfFsPhkydt9qurz8fQK32UdW+IyIikJSUxP+vv5+KZSxVH2PHbey9sn//fgBAnz59anXsVb1OtX0dKxPmH4bzOA8ACHYLRu8mvVFQUACJjwTOztp7lZWWliI7J5ufp5v28PBAbk4uAO0tdyouKygo4P+X+EgAQLBMfx86TfLLR9cOl4YjrnkcP12xHsb4uPrgzZg3LXeCCLEmZieKi4uZWCxmW7ZsEcx/4403WK9evYyu07NnT/bGG28I5m3ZsoU5OTmxkpISo+sUFRWx7Oxs/nHz5k0GgGVnZ1vmQIzIzMxkcrmcAWByuZxlZmay7GPZ7FCjQ2wf9rFd2MVe8n6JZWZmCtabtmga2+y5me3DPrYP+9gW0RaDMhUd8DnAl9c9VjutZvdS7xmth65+73m8x5e/tedWrY4vOTmZn/5V9Cvbh31sAzYwkUgkKPOty7f8Pu/fum/eSa1Hps5bbdfXnx8YGMhkMlmN91GdfevOv/7rkJmZabSMJepj7LiNvVcCAwMZtN19mUwmq/GxV/U61fZ1rMr9P+/z7+exGMucOCcmgog1kTVhGekZLCM9gzWRNeHnJV1J4qfFEDMRREwEEQsKDBIs021H9788QM6CAoOE8yocT2ZmJuvv15+vT+L0xHo7D4TUl+zs7Gp/f9tNZuj+/ftQq9UIDAwUzA8MDERaWprRddLS0oyWLysrw/379yGXyw3WmT9/PubMmWO5ildDQkIClEolAECpVEKhUCDkzxCU3ddeTeUMZ/TN7QuFQiG4CWPo4VD45ZffmPWe5p5BmYrUEjW4HGGWJbgsGGdWngHrzgzqERsbi4SEBBQUFPDlL12+hCax1b9nU8Xj27RpEz9dptEeIwcOGo1GUMalxIXfxumzpxHbxPwbV9YHY6+fOTfZNLW+/vy7d+/y5Wuyj+rsW3f+9V8HhUIBxphBGUvUx9hx6+9L9z7Q31daWlqNj72q16m2r2OV9Lq9vYAX8AJ7QTuRBpwNOAsAWIM1/LzUh1LLp/XdrbBMlxDW/Z+uV/bBvGPKY1CcVCC2v/Z4EhISkJmZyRdLu5uGNmjDL6vT80CIDbK7XqkVrypijFV6pZGx8sbm68yYMQPZ2dn84+bNm7WscdWio6P5wCwoKAhRUVHQFAuv1nATuSEqKkowT1xafkftNFEa1jZaa1CmouY/NMcxt2M4iqO4gRv8/LCgMKP10NXP06P8jtatWreq1fGNGDGCn9aNLC2CiH+uK+Pq4spvo2NUR7P2WZ9Mnbfarq8/XyaTQSaT1Xgf1dk3/1rovQ5RUVFGy1iiPsaO29h7Rf8HjUwmq/GxV/U61fZ1rIpLgEvVhepIV3RFK1H53210dDT8/fz5af0fhnV9HgixRXaTGWrUqBHEYrFBFig9Pd0g+6Mjk8mMlndycoK/v7/RdVxdXeHq6mp0WV2RSqVITEyEQqFAVFQUpFIp0jXpgjIBAQGGfRj046VFwLbx26rs5xD8RDBevvMyFAoFGh1sBNVc7eW0nh6eRuuhq9/oUaORsTIDAOAjMa+/kLHt6qbdJ7ijJLUEfr5+uKa4huTkZL5M56jOyD+q7XAq9bNs/w1LMnXeart+xfkAaryP6uw7PDwcycnJ/P/6+6lYxhL1MXXcFeddvHgRBw4cAAD07t27xsde1etU29exKl5RXgidHYp7O+4hLy8Pbm5uKCoqgpeXF5yctB/FZWVlyMvL4+fppt3c3JCfr/1bkEgkBsuKiooE2wOAvLw8ON13gjpNO+q8p0v5DxqpVIplPy5DyogUAICbh1u9nQdCbBHHmP0MJdq1a1dER0dj6dKl/Lw2bdpgyJAhRjtQv/vuu/jjjz9w4cIFft6rr76K06dP48iRI9XaZ05ODiQSCbKzs+HjY14QUBuXX74M5U9Kfto50Bk90noIyiyNW4o2O7Sp7YB/AtCmXxuz9nH7u9u4+tpVAECr1a0ge970lSGXJ12G8kdtfaJPRcO7o7dZ+zLlaMRRFCUXARzgFuomWFZ8pxisWPv27F3WWzBOCyGkaknvJeHm59rsdsf9HeHb25dflrEjA+fizgEAQmaFIGx2mDWqSEidMef7224yQwAwffp0PP/88+jcuTNiYmKwbNkypKam8uMGzZgxA7dv38bq1asBAJMmTcK3336L6dOnY+LEiThy5AhWrFiBdevWWfMwqqdiiGosZNUfTr8mV1uVt7JVfePIOrpRq9hbzG+/KKXIaBmRu8ii90MjpKGodNTrurr5MiF2yK6CoVGjRiEjIwNz586FUqlE27Zt8ffffyMkJASAtrNfamoqXz4sLAx///03pk2bhu+++w5BQUFYsmSJzV1Wb4zBB5eRAV8FNzKtQdZEsI66qgrpr2j2rkwK/TAUyTOSUZZdZnS5yFWE4DeDbf7SekJskn6v0IqfIXX0N02IPbKrYAgAJk+ejMmTJxtdFh8fbzCvd+/eUCgUdVyrOlDhg8vovYxqmRnSD4bMyQxZ8oOz8dON0fjpxpbbICGEV93MEAVDpKGzu2DIUVx76xqKbxQbzPds64ngd4LNzwzVcTAkqA99cBJiHyrJDAm6i9LfNGngKBiyEtVuFfLP5hvMv/frPW1H4upkhvRn1WSQBBvoM0QIqTvUZ4iQ6rG7cYYcxZWMKyaXvbP6Haw9s1Ywr7Ss1LCgBZvJzOozRO8aQuyD3t+qwQ8eaiYjhEeZISv5/o3vcTntMj/d8XpHfLD1AwAAp+EMbuRoLBjSL1OTu7rrB0PX517Hra9vmSxbkl6it6LZuyKEWIHgRxJ1oCbEJAqGrKRZRDMUS8v7DDVm5Z2Igz2D4Z8nHBSS0xj5tKplZkjsU95OVpZZhrJM41d0GaznJa66ECHE+vQzQxWayagfICHlKBiykl+e/kUwrdqvwpnFZwAAE9pNQKFHIe6dvFfpNgTZoxp8mEljpWj0dCNkH8quVnlOzCFwTCDcmrpVXZgQYnXVzQxRnyHS0FEwZCMEV3aVMYNfcRWbzQAIM0M1GGdI5CxC21/bmr0eIcROVJIZomYyQspRV1gbwTkJg6GKv+JEzPClElxaT7/sCCEVVJYZokvrCSlHmSErU6lUSEhIQGuuNT8v9Xgq3IvdBeXEGjE2Lt6IzMxM+Pn5ITMzEy73yu+CXZv7dunqEBERgaSkJERHRwOAwbz6umGjrj71uU9bUdlrYY3XwNTrb8nXqKp9ObKaHLtZ517vN9T5befR3Lk5AODq1asIuhdUvpCCIdLAUTBkRSqVCpGRkVAqlejWqBvmQ3uzWdF/IhTDcEDGgGkBCECA9vmD/3Vy83JrXQeRSASNRoPAwEBwHIe0tDR+nlwuR2JiYp1/SenXp772aSuqei2s8RoYe/0t+RpVtS9HVpNjN/fc62eGROtESF6XDAAQQ4y7uFtejjLLpIGjZjIrSkhIgFKpvRP8+fvnoXauarAf47Lcs3Dz7s1a10Gj0ebR7969i7S0NME8pVJZL7c10a9Pfe3TVlT1WljjNTD2+lvyNapqX46sJsdu7rn3aONRrbp4tK5eOUIcFWWGrCg6OhpyuRxKpRI+QT5o8l0TfPf8d8jPywcHDmUow/XA62jq2xSuRa7aVDaD4H81U+O803ns6b6n1nXQ/TqVyWQAIMgMBQUFISoqylKHXq361Nc+bUVVr4U1XgNjr78lX6Oq9uXIanLs5p57aawUIatCsHjKYuTm5cLL0wsAkJefB28vb7zyyito1K0R/Ab4WfTYCLE3HBP0oiMV5eTkQCKRIDs7Gz4+PhbfvkqlgkKhQFRUFN8EoVAoEB4ejuTkZP7DTn9exf9169a2DpXts7b7qEl96nOftqKy18Iar4Gp19+Sr1FV+3JkNTn2mpx7/XWA+n8/EWIN5nx/UzBUhboOhgghhBBieeZ8f1OfIUIIIYQ0aBQMEUIIIaRBo2CIEEIIIQ0aBUOEEEIIadAoGCKEEEJIg0bBECGEEEIaNAqGCCGEENKgUTBECCGEkAaNgiFCCCGENGgUDBFCCCGkQaNgiBBCCCENGgVDhBDSQKhUKuzZswcqlcraVSHEpjhZuwKEEELqnkqlQmRkJJRKJeRyORITE+mu9YQ8QJkhQghpABISEqBUKgEASqUSCoXCyjUixHZQMEQIIQ1AdHQ05HI5ACAoKAhRUVFWrhEhtoOayQghpAGQSqVITEyEQqFAVFQUNZERooeCIUIIaSCkUiliY2OtXQ1CbI5dNJNdv34dL774IsLCwuDu7o6IiAjMmjULJSUlla43fvx4cBwneHTr1q2eak0IIYQQe2AXmaFLly5Bo9Hgxx9/RPPmzXH+/HlMnDgR+fn5WLRoUaXrDhgwACtXruSnXVxc6rq6hBBCCLEjdhEMDRgwAAMGDOCnw8PDcfnyZXz//fdVBkOurq6QyWR1XUVCCCGE2Cm7aCYzJjs7G35+flWW279/PwICAvDQQw9h4sSJSE9Pr7R8cXExcnJyBA9CCCGEOC67DIaSkpLwzTffYNKkSZWWi4uLw9q1a7F371588cUXOHHiBPr164fi4mKT68yfPx8SiYR/BAcHW7r6hBBCCLEhHGOMWWvns2fPxpw5cyotc+LECXTu3JmfvnPnDnr37o3evXtj+fLlZu1PqVQiJCQE69evx7Bhw4yWKS4uFgRLOTk5CA4ORnZ2Nnx8fMzaHyGEEEKsIycnBxKJpFrf31btM/Taa6/hmWeeqbRMaGgo//zOnTvo27cvYmJisGzZMrP3J5fLERISgqtXr5os4+rqCldXV7O3TQghhBD7ZNVgqFGjRmjUqFG1yt6+fRt9+/ZFdHQ0Vq5cCZHI/Ba+jIwM3Lx5kx+FlRBCCCHELvoM3blzB3369EFwcDAWLVqEe/fuIS0tDWlpaYJyrVq1wtatWwEAeXl5eOutt3DkyBFcv34d+/fvx6BBg9CoUSM89dRT1jgMQgghhNggu7i0fteuXbh27RquXbuGpk2bCpbpd3m6fPkysrOzAQBisRjnzp3D6tWrkZWVBblcjr59+2LDhg3w9vau1/oTQgghxHZZtQO1PTCnAxYhhBBCbIM539920UxGCCGEEFJX7KKZzJp0iTMafJEQQgixH7rv7eo0gFEwVIXc3FwAoMEXCSGEEDuUm5sLiURSaRnqM1QFjUaDO3fuwNvbGxzHWXTbugEdb968Sf2R6hCd5/pB57l+0HmuH3Se60ddnmfGGHJzcxEUFFTlcDyUGaqCSCQyuILN0nx8fOiPrR7Qea4fdJ7rB53n+kHnuX7U1XmuKiOkQx2oCSGEENKgUTBECCGEkAaNgiErcnV1xaxZs+heaHWMznP9oPNcP+g81w86z/XDVs4zdaAmhBBCSINGmSFCCCGENGgUDBFCCCGkQaNgiBBCCCENGgVDhBBCCGnQKBiykqVLlyIsLAxubm6Ijo7GoUOHrF0luzZ79mxwHCd4yGQyfjljDLNnz0ZQUBDc3d3Rp08fJCYmWrHG9uHgwYMYNGgQgoKCwHEctm3bJlhenfNaXFyM119/HY0aNYKnpycGDx6MW7du1eNR2L6qzvP48eMN3t/dunUTlKHzXLX58+ejS5cu8Pb2RkBAAIYOHYrLly8LytB7uvaqc55t7T1NwZAVbNiwAVOnTsUHH3yAU6dOoWfPnoiLi0Nqaqq1q2bXIiMjoVQq+ce5c+f4ZQsWLMCXX36Jb7/9FidOnIBMJkP//v35e88R4/Lz89GhQwd8++23RpdX57xOnToVW7duxfr163H48GHk5eVh4MCBUKvV9XUYNq+q8wwAAwYMELy///77b8FyOs9VO3DgAKZMmYKjR49i9+7dKCsrw2OPPYb8/Hy+DL2na6865xmwsfc0I/Xu4YcfZpMmTRLMa9WqFXvvvfesVCP7N2vWLNahQwejyzQaDZPJZOyzzz7j5xUVFTGJRMJ++OGHeqqh/QPAtm7dyk9X57xmZWUxZ2dntn79er7M7du3mUgkYjt27Ki3utuTiueZMcbGjRvHhgwZYnIdOs81k56ezgCwAwcOMMboPV1XKp5nxmzvPU2ZoXpWUlKChIQEPPbYY4L5jz32GP777z8r1coxXL16FUFBQQgLC8MzzzyD5ORkAEBKSgrS0tIE59zV1RW9e/emc14L1TmvCQkJKC0tFZQJCgpC27Zt6dybaf/+/QgICMBDDz2EiRMnIj09nV9G57lmsrOzAQB+fn4A6D1dVyqeZx1bek9TMFTP7t+/D7VajcDAQMH8wMBApKWlWalW9q9r165YvXo1du7ciZ9++glpaWno3r07MjIy+PNK59yyqnNe09LS4OLiAqlUarIMqVpcXBzWrl2LvXv34osvvsCJEyfQr18/FBcXA6DzXBOMMUyfPh2PPPII2rZtC4De03XB2HkGbO89TXettxKO4wTTjDGDeaT64uLi+Oft2rVDTEwMIiIisGrVKr5THp3zulGT80rn3jyjRo3in7dt2xadO3dGSEgI/vrrLwwbNszkenSeTXvttddw9uxZHD582GAZvactx9R5trX3NGWG6lmjRo0gFosNItv09HSDXyOk5jw9PdGuXTtcvXqVv6qMzrllVee8ymQylJSUQKVSmSxDzCeXyxESEoKrV68CoPNsrtdffx2///479u3bh6ZNm/Lz6T1tWabOszHWfk9TMFTPXFxcEB0djd27dwvm7969G927d7dSrRxPcXExLl68CLlcjrCwMMhkMsE5LykpwYEDB+ic10J1zmt0dDScnZ0FZZRKJc6fP0/nvhYyMjJw8+ZNyOVyAHSeq4sxhtdeew1btmzB3r17ERYWJlhO72nLqOo8G2P197TFu2STKq1fv545OzuzFStWsAsXLrCpU6cyT09Pdv36dWtXzW69+eabbP/+/Sw5OZkdPXqUDRw4kHl7e/Pn9LPPPmMSiYRt2bKFnTt3jo0ePZrJ5XKWk5Nj5ZrbttzcXHbq1Cl26tQpBoB9+eWX7NSpU+zGjRuMseqd10mTJrGmTZuyPXv2MIVCwfr168c6dOjAysrKrHVYNqey85ybm8vefPNN9t9//7GUlBS2b98+FhMTw5o0aULn2Uyvvvoqk0gkbP/+/UypVPKPgoICvgy9p2uvqvNsi+9pCoas5LvvvmMhISHMxcWFRUVFCS45JOYbNWoUk8vlzNnZmQUFBbFhw4axxMREfrlGo2GzZs1iMpmMubq6sl69erFz585Zscb2Yd++fQyAwWPc/7drPyFR9HEcx99jjxuSWyEsbYimeYkwlIgOnipQJBY8Bh3UPFUEFUQHT8FCWpTIRtGpNrJL0R+72CXtnhVlUKdKITbCDsFqVFvToQdBnuep4EnXdt6v0zDM/Ph+v8zhM7+Zrq4wDH9trh8+fAgPHDgQVlVVhRUVFWEqlQqnpqaK0M3S9aM5z87Ohm1tbWEikQjLy8vD2trasKur6x8zdM4/928zBsKLFy/OXeMz/f/9bM5L8ZkO/i5ckiQpkvxnSJIkRZphSJIkRZphSJIkRZphSJIkRZphSJIkRZphSJIkRZphSJIkRZphSJIkRZphSNIfr7u7myAICIKA8vJy1qxZQ2trKxcuXODr16+/vE42m2X16tULV6ikJckwJKkktLe3k8vlePXqFSMjI2zfvp2DBw+SSqUoFArFLk/SEmYYklQSli9fTjKZpLq6ms2bN9Pb28vw8DAjIyNks1kABgYG2LRpEytWrKCmpob9+/eTz+cBuHfvHnv27OH9+/dzu0zHjh0DYGhoiC1bthCPx0kmk+zevZu3b98WqVNJv5thSFLJ2rFjB01NTdy4cQOAsrIyMpkMT58+5dKlS4yOjnL06FEAWlpaGBwcZOXKleRyOXK5HEeOHAHg06dPpNNpHj9+zK1bt3j58iXd3d3FakvSb/ZXsQuQpIW0YcMGnjx5AsChQ4fmztfX15NOp9m3bx/nzp0jFouxatUqgiAgmUzOW6Onp2fueP369WQyGbZu3Uo+n6eysnJR+pC0cNwZklTSwjAkCAIAxsbGaG1tpbq6mng8TmdnJ+/evWNmZuaHazx69IiOjg7WrVtHPB5n27ZtAExNTS10+ZIWgWFIUkl79uwZ9fX1TE5OsnPnThobG7l+/ToPHjzg7NmzAHz+/Pk/75+ZmaGtrY3KykqGhoa4f/8+N2/eBL5/PpP05/MzmaSSNTo6ysTEBIcPH2Z8fJxCocDp06cpK/v+Hnj16tV518diMb58+TLv3PPnz5menqa/v5+amhoAxsfHF6cBSYvCnSFJJeHjx4+8efOG169f8/DhQ44fP05HRwepVIrOzk4aGhooFAqcOXOGFy9ecPnyZc6fPz9vjbq6OvL5PHfv3mV6eprZ2Vlqa2uJxWJz992+fZt0Ol2kLiUtBMOQpJJw584d1q5dS11dHe3t7YyNjZHJZBgeHmbZsmU0NzczMDDAiRMnaGxs5MqVK/T19c1bo6Wlhb1797Jr1y4SiQQnT54kkUiQzWa5du0aGzdupL+/n1OnThWpS0kLIQjDMCx2EZIkScXizpAkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYo0w5AkSYq0b9JnvmHh6f7aAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "
    "
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png
index df0446359..b34267fb5 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png
index 51b2a3e78..377d57935 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
index 427e843d9..e1204b4dc 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": [
-      "0.04570437990371566\n",
-      "4.420442688206847\n",
-      "[[ 1.01597952  3.06059304]\n",
-      " [ 3.06059304 10.1387933 ]]\n"
+      "-0.11743722141098414\n",
+      "3.5452708224046345\n",
+      "[[ 1.27880068  3.85600299]\n",
+      " [ 3.85600299 12.61955303]]\n"
      ]
     }
    ],
@@ -340,10 +340,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.07663067400487368\n",
-      "1.9423652864980914\n",
-      "[[1.         0.72782592]\n",
-      " [0.72782592 1.        ]]\n"
+      "0.07178264457746288\n",
+      "1.6714298027296224\n",
+      "[[1.         0.59987612]\n",
+      " [0.59987612 1.        ]]\n"
      ]
     }
    ],
@@ -397,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.51761523 -1.42486342]\n",
-      " [ 1.91816586  6.87585634]\n",
-      " [-0.34694145 -1.09920915]\n",
-      " [ 0.31244861  1.08282867]\n",
-      " [ 1.12441319  3.24411906]\n",
-      " [-0.51892347 -1.08417181]\n",
-      " [-0.54509921 -2.1148557 ]\n",
-      " [ 0.26084008  0.60846694]\n",
-      " [ 0.01029574  0.21049575]\n",
-      " [-1.69758412 -6.29866668]]\n",
+      "[[-1.05589275 -2.32845846]\n",
+      " [-1.43650129 -5.0020496 ]\n",
+      " [ 0.30685269 -0.25002882]\n",
+      " [ 1.1511986   3.95940231]\n",
+      " [-0.84931504 -2.84538739]\n",
+      " [-0.63401971 -1.90876452]\n",
+      " [ 0.39256409  1.76775004]\n",
+      " [ 1.07828283  3.52988562]\n",
+      " [-0.18753987  0.14133772]\n",
+      " [ 1.23437046  2.9363131 ]]\n",
       "          0         1\n",
-      "0 -0.517615 -1.424863\n",
-      "1  1.918166  6.875856\n",
-      "2 -0.346941 -1.099209\n",
-      "3  0.312449  1.082829\n",
-      "4  1.124413  3.244119\n",
-      "5 -0.518923 -1.084172\n",
-      "6 -0.545099 -2.114856\n",
-      "7  0.260840  0.608467\n",
-      "8  0.010296  0.210496\n",
-      "9 -1.697584 -6.298667\n",
+      "0 -1.055893 -2.328458\n",
+      "1 -1.436501 -5.002050\n",
+      "2  0.306853 -0.250029\n",
+      "3  1.151199  3.959402\n",
+      "4 -0.849315 -2.845387\n",
+      "5 -0.634020 -1.908765\n",
+      "6  0.392564  1.767750\n",
+      "7  1.078283  3.529886\n",
+      "8 -0.187540  0.141338\n",
+      "9  1.234370  2.936313\n",
       "          0         1\n",
-      "0  1.000000  0.993148\n",
-      "1  0.993148  1.000000\n"
+      "0  1.000000  0.972745\n",
+      "1  0.972745  1.000000\n"
      ]
     }
    ],
@@ -461,37 +461,37 @@
      "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  0.079243  0.085251  0.080541  0.083416  0.086394  0.074010  0.075867   \n",
-      "2   0.0  0.085251  0.093570  0.084995  0.089008  0.093218  0.076658  0.079150   \n",
-      "3   0.0  0.080541  0.084995  0.088240  0.090361  0.092452  0.084878  0.086337   \n",
-      "4   0.0  0.083416  0.089008  0.090361  0.093080  0.095821  0.086090  0.087899   \n",
-      "5   0.0  0.086394  0.093218  0.092452  0.095821  0.099275  0.087175  0.089365   \n",
-      "6   0.0  0.074010  0.076658  0.084878  0.086090  0.087175  0.084042  0.084968   \n",
-      "7   0.0  0.075867  0.079150  0.086337  0.087899  0.089365  0.084968  0.086108   \n",
-      "8   0.0  0.077847  0.081832  0.087845  0.089793  0.091685  0.085877  0.087254   \n",
-      "9   0.0  0.079975  0.084740  0.089414  0.091796  0.094163  0.086774  0.088416   \n",
-      "10  0.0  0.067272  0.068624  0.079437  0.079971  0.080322  0.080193  0.080702   \n",
-      "11  0.0  0.068609  0.070338  0.080572  0.081322  0.081908  0.081000  0.081647   \n",
-      "12  0.0  0.070043  0.072194  0.081762  0.082754  0.083604  0.081821  0.082621   \n",
-      "13  0.0  0.071587  0.074211  0.083015  0.084278  0.085427  0.082657  0.083630   \n",
-      "14  0.0  0.073256  0.076413  0.084340  0.085908  0.087393  0.083511  0.084678   \n",
+      "1   0.0  0.093993  0.092566  0.095420  0.093559  0.091714  0.087802  0.086054   \n",
+      "2   0.0  0.092566  0.091571  0.094206  0.092560  0.090919  0.086830  0.085223   \n",
+      "3   0.0  0.095420  0.094206  0.103273  0.101409  0.099552  0.098879  0.097049   \n",
+      "4   0.0  0.093559  0.092560  0.101409  0.099701  0.097995  0.097238  0.095528   \n",
+      "5   0.0  0.091714  0.090919  0.099552  0.097995  0.096434  0.095596  0.094001   \n",
+      "6   0.0  0.087802  0.086830  0.098879  0.097238  0.095596  0.097294  0.095624   \n",
+      "7   0.0  0.086054  0.085223  0.097049  0.095528  0.094001  0.095624  0.094050   \n",
+      "8   0.0  0.084365  0.083669  0.095273  0.093866  0.092450  0.093996  0.092516   \n",
+      "9   0.0  0.082734  0.082168  0.093551  0.092254  0.090945  0.092412  0.091021   \n",
+      "10  0.0  0.079914  0.079165  0.092493  0.091090  0.089678  0.092852  0.091372   \n",
+      "11  0.0  0.078377  0.077731  0.090832  0.089523  0.088202  0.091293  0.089893   \n",
+      "12  0.0  0.076897  0.076349  0.089227  0.088007  0.086773  0.089781  0.088456   \n",
+      "13  0.0  0.075471  0.075017  0.087674  0.086540  0.085390  0.088314  0.087062   \n",
+      "14  0.0  0.074096  0.073734  0.086172  0.085121  0.084051  0.086891  0.085709   \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   0.077847  0.079975  0.067272  0.068609  0.070043  0.071587  0.073256  \n",
-      "2   0.081832  0.084740  0.068624  0.070338  0.072194  0.074211  0.076413  \n",
-      "3   0.087845  0.089414  0.079437  0.080572  0.081762  0.083015  0.084340  \n",
-      "4   0.089793  0.091796  0.079971  0.081322  0.082754  0.084278  0.085908  \n",
-      "5   0.091685  0.094163  0.080322  0.081908  0.083604  0.085427  0.087393  \n",
-      "6   0.085877  0.086774  0.080193  0.081000  0.081821  0.082657  0.083511  \n",
-      "7   0.087254  0.088416  0.080702  0.081647  0.082621  0.083630  0.084678  \n",
-      "8   0.088665  0.090123  0.081150  0.082248  0.083393  0.084594  0.085858  \n",
-      "9   0.090123  0.091913  0.081538  0.082805  0.084141  0.085557  0.087063  \n",
-      "10  0.081150  0.081538  0.077571  0.078106  0.078624  0.079125  0.079606  \n",
-      "11  0.082248  0.082805  0.078106  0.078732  0.079353  0.079969  0.080577  \n",
-      "12  0.083393  0.084141  0.078624  0.079353  0.080089  0.080832  0.081584  \n",
-      "13  0.084594  0.085557  0.079125  0.079969  0.080832  0.081718  0.082632  \n",
-      "14  0.085858  0.087063  0.079606  0.080577  0.081584  0.082632  0.083726  \n"
+      "1   0.084365  0.082734  0.079914  0.078377  0.076897  0.075471  0.074096  \n",
+      "2   0.083669  0.082168  0.079165  0.077731  0.076349  0.075017  0.073734  \n",
+      "3   0.095273  0.093551  0.092493  0.090832  0.089227  0.087674  0.086172  \n",
+      "4   0.093866  0.092254  0.091090  0.089523  0.088007  0.086540  0.085121  \n",
+      "5   0.092450  0.090945  0.089678  0.088202  0.086773  0.085390  0.084051  \n",
+      "6   0.093996  0.092412  0.092852  0.091293  0.089781  0.088314  0.086891  \n",
+      "7   0.092516  0.091021  0.091372  0.089893  0.088456  0.087062  0.085709  \n",
+      "8   0.091072  0.089664  0.089925  0.088521  0.087159  0.085835  0.084550  \n",
+      "9   0.089664  0.088339  0.088510  0.087180  0.085888  0.084633  0.083414  \n",
+      "10  0.089925  0.088510  0.089979  0.088563  0.087184  0.085842  0.084536  \n",
+      "11  0.088521  0.087180  0.088563  0.087212  0.085898  0.084617  0.083371  \n",
+      "12  0.087159  0.085888  0.087184  0.085898  0.084644  0.083423  0.082234  \n",
+      "13  0.085835  0.084633  0.085842  0.084617  0.083423  0.082260  0.081126  \n",
+      "14  0.084550  0.083414  0.084536  0.083371  0.082234  0.081126  0.080045  \n"
      ]
     }
    ],
@@ -916,10 +916,10 @@
      "output_type": "stream",
      "text": [
       "          0         1\n",
-      "0  3.987648  2.034723\n",
-      "1  2.034723  2.038727\n",
-      "[[3.98764765 2.03472297]\n",
-      " [2.03472297 2.03872663]]\n"
+      "0  4.068439  2.030371\n",
+      "1  2.030371  2.006046\n",
+      "[[4.06843936 2.03037095]\n",
+      " [2.03037095 2.00604596]]\n"
      ]
     }
    ],
@@ -949,13 +949,13 @@
      "output_type": "stream",
      "text": [
       "Centered covariance using own code\n",
-      "[[3.98764765 2.03472297]\n",
-      " [2.03472297 2.03872663]]\n"
+      "[[4.06843936 2.03037095]\n",
+      " [2.03037095 2.00604596]]\n"
      ]
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -1044,12 +1044,12 @@
      "output_type": "stream",
      "text": [
       "Eigenvalues of Covariance matrix\n",
-      "5.269217029290255\n",
-      "0.7571572558830478\n",
+      "5.314471861842257\n",
+      "0.7600134536106469\n",
       "First eigenvector\n",
-      "[0.84614892 0.53294653]\n",
+      "[0.8522997  0.52305374]\n",
       "Second eigenvector\n",
-      "[-0.53294653  0.84614892]\n"
+      "[-0.52305374  0.8522997 ]\n"
      ]
     },
     {
@@ -1057,7 +1057,7 @@
      "output_type": "stream",
      "text": [
       "Eigenvector of largest eigenvalue\n",
-      "[-0.84614892 -0.53294653]\n"
+      "[0.8522997  0.52305374]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png
index 23a504d1f..f767fd5a7 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
index b19d84d2f..39c50bc63 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
@@ -924,14 +924,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11016/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8738/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -1101,7 +1101,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
@@ -1802,16 +1802,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.27637358 4.69167569]\n",
-      "[[4.08692465]\n",
-      " [2.84462849]]\n",
-      "[[4.08692465]\n",
-      " [2.84462849]]\n"
+      "[0.33015882 4.55906894]\n",
+      "[[4.11836068]\n",
+      " [2.85949635]]\n",
+      "[[4.11836068]\n",
+      " [2.85949635]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
    "
       ]
@@ -1896,9 +1896,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4.17086577]\n",
-      " [2.92317667]]\n",
-      "[4.14538257] [2.90670236]\n"
+      "[[4.13451895]\n",
+      " [2.8383548 ]]\n",
+      "[4.19783086] [2.93535577]\n"
      ]
     }
    ],
@@ -2002,15 +2002,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4.11027723]\n",
-      " [2.92805329]]\n",
-      "[[4.04931542]\n",
-      " [2.97504526]]\n"
+      "[[4.12575322]\n",
+      " [2.87918262]]\n",
+      "[[4.08876865]\n",
+      " [2.90510842]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -2389,20 +2389,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.22532324]\n",
-      " [3.44210664]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30012384 4.62464344]\n",
+      "[[3.78515112]\n",
+      " [3.19029687]]\n",
+      "Eigenvalues of Hessian Matrix:[0.30739146 4.15768662]\n",
       "theta from own gd\n",
-      "[[3.22532324]\n",
-      " [3.44210664]]\n",
+      "[[3.78515112]\n",
+      " [3.19029687]]\n",
       "theta from own sdg\n",
-      "[[3.17736035]\n",
-      " [3.48289037]]\n"
+      "[[3.81425332]\n",
+      " [3.25285802]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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eXo4ZM2agc+fOaNCgAaKjo/Hoo4/i/Pnz3mswERGRN+l0QHY2kJFh+K3TebtFsuf1YKi4uBhdunTBkiVLaqy7desW9uzZg7/85S/Ys2cPMjMzcezYMTzwwANeaCkREZGXZWYCrVoBycnAxImG361aGZaT01RCCOHtRhipVCpotVqMHTvW6jY7d+5E7969cfr0abRo0cKu/RYVFSE0NBSFhYUICQlxUWuJiIg8KDMTGDcOqH7ZVqkMv1etAlJTPd8uN/LU9dvrPUOOKiwshEqlQuPGja1uU1paiqKiIrMfIiIi2dLpgOeeqxkIAZXL0tJ4y8xJsgqGSkpKMHPmTEycOLHWCHHBggUIDQ01/cTFxXmwlURERC62bRtw9qz19UIAeXmG7chhsgmGysvL8fDDD0Ov1+PDDz+sddtZs2ahsLDQ9JOXl+ehVhIREbnBhQuu3Y7MBHi7AfYoLy/H+PHjkZubi82bN9u8b6hWq6FWqz3UOiIiIjeLinLtdmRG8j1DxkDo+PHj+OGHHxAeHu7tJhEREXnWwIFAbGxlsnR1KhUQF2fYjhzm9Z6hmzdv4sSJE6bHubm5+PXXXxEWFobo6GiMGzcOe/bswfr166HT6ZCfnw8ACAsLQ1BQkLeaTUREUqTTGfJmLlww9JIMHAj4+3u7VXXn7w+8+65hNJlKZZ5IbQyQFi/2jWP1Aq8Prc/OzkZycnKN5Y899hjmzp2L+Ph4i8/LysrCkCFD7HoNDq0nIlKAzEzDiKuqicaxsYYgwleGnFs6xrg4QyDkK8dYhaeu314PhjyBwRARkY9TUg0eX+39soDBkAsxGCIi8mE6naEKs7Wh5yqVoYcoN9dngwZfxaKLRERE9mANHqojBkNERCRvrMFDdcRgiIiI5I01eKiOGAwREZG8sQYP1RGDISIikjdjDR6gZkDEGjxkBwZDREQkf6mphuHzMTHmy2NjfWtYPbmF1ytQExERuURqKpCSopgaPOQ6DIaIiMh3+PsDds5OUIOCihmSOQZDRERESpjKg6xizhARESmbcSqP6oUbz50zLM/M9E67yGMYDBERkXLpdIYeIUszUxmXpaUZtiOfxWCIiIiUi1N5EBgMERGRknEqDwKDISIiUjJO5UFgMERERErGqTwIDIaIiEjJOJUHgcEQEREpHafyUDwWXSQiIuJUHorGYIiIiAio21QeJGsMhoiIiDyN86BJCoMhIiIiT+I8aJLDBGoiIiJP4TxoksRgiIiIyBM4D5pkMRgiIiLl0OmA7GwgI8Pw25OBB+dBkyzmDBERkTJ4O1eH86BJFnuGiIjI90khV4fzoEkWgyEiIvJtUsnV4TxoksVgiIiIfJu7c3XszUPiPGiSxWCIiIh8mztzdTIzgVatgORkYOJEw+9WrazfduM8aJLEBGoiIvJt7srVMeYhVb/9ZsxDshbccB40yVEJYekmqm8pKipCaGgoCgsLERIS4u3mEBGRJ+l0ht6ac+cs5w2pVIaemdxc+wMS4z6t3X5zZp9Ug6eu37xNRkREvs0duTqsGeRTGAwREZHvc3WuDmsG+RTmDBERkTK4MleHNYN8CoMhIiJSDn9/YMgQ6+t1OvuCJWPNIFt5SKwZJAu8TUZERAQ4NkyeNYN8CoMhIiIiZ6brYM0gt7l68ho+/eOPmNB2l0dez+vB0NatWzFmzBhER0dDpVJhzZo1ZuuFEJg7dy6io6NRr149DBkyBAcPHvROY4mIyPfUZbqO1FTg1CkgKwtITzf8zs1lIOSEszsvYMnvtuDesD2IuKsRHv/XAPy3oKdHXtvrwVBxcTG6dOmCJUuWWFy/aNEivP3221iyZAl27tyJ5s2bY9iwYbhx44aHW0pERD6prsPkjXlIEyYYfkv51pi9U4d4yNFvfsMb92Wjd8ODiOsdhWmrBmPzte7QIQBJwUcxs79nShN4PYF65MiRGDlypMV1QggsXrwYs2fPRuqdKPvTTz9FZGQk0tPTMXnyZE82lYiIfJFShslnZhp6wKoGfrGxhtwnD/VkCb3A7s8PQ/vPAmh3xeFwWRsArQEAKujRr9EBaAZfheaF1mg9pD2KiqLwRqj72+X1YKg2ubm5yM/Px/Dhw03L1Go1Bg8ejJ9//tlqMFRaWorS0lLT46KiIre3lYiIZEoJw+SdnTrEBSpKKrDtw/3QflqENQfvQp6uI4COAIBAlOHepvugGXEbD7zcAc2TktzSBlskHQzl5+cDACIjI82WR0ZG4vTp01aft2DBAsybN8+tbSMiIh/h68PkbeVEqVSGnKiUFJfd4iu5XoKNb+VAu7IU6050xBXRzbSuAW5iZMx+aFL0GDUjEaEternkNetC0sGQkarasEUhRI1lVc2aNQvTp083PS4qKkJcXJzb2kdERDJmHCY/bpwhMKgaNPjCMHlHcqJqq8FkQ+GZQmxYeADatX745lxnFKO3aV246goeuOsQNA+pMfT5zqgX1tfp13EHSQdDzZs3B2DoIYqq0j1ZUFBQo7eoKrVaDbVa7fb2ERGRjzAOk7eUU7N4sbxHh7kxJ+rigUtY+8ZhaL+rh02Xu6Ac/U3r4vzPYWynE9A8FoKBUzojIFi6PWuSDobi4+PRvHlzbNy4Ed26GbrYysrKsGXLFixcuNDLrSMiIp/iyuk6pOT4cfu2szMn6rfsM9C+9Ru0W8Lw841ECAwyrUsIOglNzzxoJkegxyMJUPnF1LIn6fB6MHTz5k2cOHHC9Dg3Nxe//vorwsLC0KJFC6SlpeH1119H27Zt0bZtW7z++uuoX78+Jk6c6MVWExGR5Nk7tUZVtqbrkBudDli2zPZ2teRECb3A/szj0H5wHtodUdhX0h5AC9P6Xg0OInXAJWiea4H2I9sAaOOatnuQ14OhXbt2ITk52fTYmOvz2GOPYcWKFXj55Zdx+/ZtTJkyBdeuXcPdd9+N77//Ho0aNfJWk4mISOokMIy8TpwJ5CzZts2QGG7LH/9otn99hR7bPzoA7cdXod0Xj98q2gFoBwDwRwUGN8mB5t4bGPtyO8T26uR4uyRGJYSl9HLfUlRUhNDQUBQWFiIkJMTbzSEiInf66itg/Piay43J0FKfKsOVgVxGhmGeNVvS01E25kFkvZuDzM9vYe2xDriojzCtDsZtDG+eg9TR5Rj9ckeEtw1zrB1O8tT12+s9Q0RERC6zapWhErQlbhpG7lKurgdkZx7Qa89fxpsTb6MQldNfhKIQo1sdgGacH+57KQkNIu62/3Vlhj1DRETkGzIzgQcftG/brCzp5QbpdECrVtaHwRvrHeXm2h/IGfdppYaSHiqcRSzikQs9/NHc7yLGdjgKzSMNMGRaZwQ1DHL6cFyBPUNEROTbXJUXY9zXc8/Zv70Up9ZwRz2gOzWUxIPjAABVK/Tp7zx6w282pnffBs1T4ejzZCf4BVgvXeOrGAwREZHnuTrB2VYgUV3120euDMyc5eJ6QIfXn4T2vTxof+qAOKzCu3gOcah8j26om+L6tFfxwcI/QeVnvZCxEjAYIiIiz3LHPFmO9PTExZkPI5fKyLM6zpEm9AK7PrszCeruOBwpqxzmvhsdcKVRK0ztvBVD7g1AxD2JCB04EKFSzJvyAuYMERGR57gjLwYAsrOBKmVaarV6dWWQYy0w88bIMxv5PZbem4qSCmz94M4kqIfa4qwu2rR5IMowtNk+aO67jQdeTkBkYjPPHIcLeer6zWCIiIg8x96gxdEEZ1uBBGAIIDIygN/9zvw5rg7M6sIYnAGW50hbtQq3h4w0TIL6ZRm+PtkRV0S4abMGuIn7Y/dDM1bg/pc6IbRFqONtkMItwzuYQE1ERL7HXfNk1TbZqtEXX1QGGoDHJjB1iJU50vTNo/Fj0hS892wUvj2nQzEqh7k3VV3GA3cdhuZhNYZOT0Jw4zpMgiqVW4YexmCIiEjuJPSfvE11zIuplTGQ+NOfgCtXzNeFhwN+fubL3DiBaZ3cmSPt6op12PPJXnx/IAbvXXgUpRfqmTaJ8z8HTeIJpD4Riv6TE10zCao7crlkgsEQEZGcye0/+YEDDe2zlRdjZZ4su1y9annZuHHAypVAs2aGAOfiRfv250xg5qSTm09D+3YutFvDsP1GCgQ0pnUd1Seg6XkWmj9HovvEDq6dBNVYmsDSZyKHYpV1xJwhIiK5klLyryPsyItxqt22coAAw4Vcp7P+uCoP5AwJvUDOqmPQfngBmb9EY39JO7P1vRscQOrAy9CktUS7EfFuaQMA9+Vy1RFzhoiIyDo5/ydvJS8GsbHA4sXOB3D21BqqHvjUFggBhva4+P3Tlemw/V8Hof3kKrT7WiO3oj2A9gAMk6AOaZIDzdAbSHmpHWJ7Jbr0ta2S6i1DD2EwREQkR1JM/nXEnbwYl+Y61eVCXb2HqK6BWTWlRaXYvDgH2ozbWHs0AQUiybQuGLdxX9Q+aEZXYPSMTghr090lr+kQd+ZyyQCDISIiOfKF/+T9/V0bqNXlQq3TAe+8A0RGuiwJ/Wb+TXyzaD+0mXpsOJ2IIvQyrWusun5nEtQAjHixMxpE9KnTa9WZJ3K5JIzBEBGRHCn8P3mLbF3QbYmMtD7jvZ0uH72CdW8cgva/amwsSEIpKoe5R/nlY2zCMcMkqM8mIbD+gDq9lkvVVprAjbcMpYLBEBGRHCn8P3mL7Kk1VBsnA8cz289hzZsnoN0cgq3Xk6BH5Xt+V+ApaLqeguapcNz9h07wC2ju1Gt4hLtyuWSAo8mIiOTKXaOyvMGVtZIslRtw8aixw+tPIvPdPGh/jsDuWx3N1nWrdxiavheheSYGnVLukt8kqBKqW8XpOFyIwRAR+SxLF/64OHn9J++OWknVL+iXLwPjxxvWORE46iv0hklQl12Cdk8LHC1rXbkL6DEgZD80ydcwdnobxA+Kc67NVAODIRdiMEREPk1C/8k7zJO1khwMHMtvlRsmQf3sBtYcbIdz+srbaEEoxdBmOdCMLMEDL3dARCf5TYIqBwyGXIjBEBGRBHljolQbgePtq7fx/Zs50H5ZjnW/dcI10cS0riFu4P64/UjVACNfSkRILK8n7saii0RE5Nu8USvJwnD+66cLsf6NA9Cu88e35zvjVrVJUFPaHoZmQjDuTeuM4Mb9XNMOkhQGQ0RE5B1erJV04deLWLPwKLTfN0DW1SRUoL9pXQv/s9B0PonUPzRG/8mJ8A9S0Ig8hWIwRERE3uHhWkknNhknQQ3HjpudIBBpWtdJfRyaXueg+XMkuk3oAJVfrEtek+SBwRAREXmHm2slCb3Avq+OIfODC9D+LwYHStsCaGlaf3eDA0gddBma51uh7bC2ANo6dxwkewyGiIikyJERYnIdTeaGqse6Mh1+XnYA2uXXoM1pg1NVJkENQDmGhOVAM+wmUl5sh5ieHpoElSSPwRARkafZCl4cqbvjjho9nuSCqselRaXY9I5hEtR1xxJQILqY1tXDLYyIykHqAxUYPSMRTeJ7uOEgZEyugbSLcWg9EZEn2QpeHKm748kaPe7m4EX5xvkb+Obv+6HNBDacScQNVP5tb6y6jjHxdyZBfSkJ9ZvW98QRyI8MAmnWGXIhBkNEJAm2gpcvvwSef96+ujuA52v0eNmlw5exbuFhaL9R44eCJJQi2LQuyi8fmo5HoZnUCIOf6YzA+oFebKkMyCSQZjDkQgyGiMjr7Ckw2LQpcOmS7X1lZRl+Jyfbt62ravR4wemfzmLNWyeh3RyKbYWdoUdlYNc2MBeabqeh+WNT9H68I/wC/LzYUhmx9V0EgGbNDOuDgjzWLEtYdJGIyJfYU2DQnkAIcKzujhtq9LiT0Asc+voktO+fhfbnSOy5nQCgcph793qHoel3EZppseg4pg1UfvHea6xc2fouAobvYmwssHSpJHqI3I3BEBGRJ7gyKHGk7o6LavS4k75Cj52fHkLmssvQ7m2J4+V3AbgLAOAHHQaE7ocmuRBjX2iDVgMSACR4tb2yYS0Py97v4qVLhltpErll5k4MhoiIPMHeoKRpU+DKFfvq7rixRo+7ld8qx5YldyZBPdQO5/WVw9yDUIphEfugGVmKB2YkoFlCV+811NNcNbqrtuRoRwPktDQgJcVncs8sYTBEROQJ9hYYfOst4KGH7Ku74+IaPe526/ItfP/WfmR+WY71uZ1wTXQ3rWuEItwfdwCpDwIjX+qMRtG9vdhSL3HV6C5rydHnzhmWf/ll7d/FqtwxP5wUCQUoLCwUAERhYaG3m0JESrZ6tRAqleHHcJkx/BiXrV5duV1srPk2cXGV66vv095tveDqb9fEvydvE5qo7aIeis2a2UxVIJ5qv0VsmPs/UVJY4u2mepfxu1H1DbL03ahNRYUQP/wgRFhYzf1U3V9cnBBffmn59az9pKe7/z2wwFPXb44mIyLXYPE2+1j67z8urmaBQRlXoD6/Jx9rFh2FdmNDZF9NQgUqh7m39D8LTZJhEtR+f0qEfxC/I3aNNLRVJsHS96o2WVnA1avA5MnA5cv2be+FniEOrXchBkNEbiaD4m0Oc2eAIbHgxRWObzwF7TunoN0Wjh03O5utS1Qfh6b3OWiebo6uD7WHyk/lpVZKVHZ23cokWLstVpv0dGDCBKCszHCuWhvJ6OV6VRxaT0TyYCs/QY4jUdwd3Pn7yz7/QugF9mYcgXbpRWh3xuBgaVsArUzr+zTcj9RBVzA2jZOg2mTv6C5L2+l0hu+qo/0axiTqoCDD8Plx4wyPZZB75g7sGSIi57mie19qZFKZ1xt0ZTr89M8D0C6/Dm1OG5zWVdb/CUA5ksNyoBlejJSX2iG6e3MvtlRm6tIzZO9zjaydk/bevvUw3ia7o6KiAnPnzsV//vMf5OfnIyoqCo8//jheeeUV+PnZV22UwRCRm9S1e19qfDG4q6PSolL88HYOtBklWHe8Ay6JZqZ19XALI6NzoHlAh1Evd0KT+Mbea6icGb93tkYaWvreZWQAEyfa9zq2AnoJ3r7lbbI7Fi5ciKVLl+LTTz9Fp06dsGvXLjzxxBMIDQ3Fc8895+3mESlbXbr3pcieKtEKGGZcdLYI3/z9ADK1wH/zOuMmepnWNVFdw5j4g9CMD8TwFzqjftM+Xmypj/D3d75MgiM1g2Jja+/p8YHbt86SfDC0fft2pKSkYNSoUQCAVq1aISMjA7t27fJyy4jI7j/EMqiCDMD3gjsHFBy8hHWLjkD7TTB+uJSEMvQzrYvxu4CxnY5BM6kRBk3tjMD6A7zYUh+VmmrosbGUq1ZbAGOrfhUAhIUZagsNGeL1nh6pknwwNGDAACxduhTHjh1Du3btsG/fPvz4449YvHixt5tGRPYWEpRoFeQafC24s+HUj4ZJUDM3N8ZPRYnQo/JzaheYC03300id3Aw9JyXAL8A3jlnSUlMNlZ4duVVlT6/SRx8B997r3rbLnOSDoRkzZqCwsBAdOnSAv78/dDod5s+fjwkTJlh9TmlpKUpLS02Pi4qKPNFUIuWpS/e+FA0cCISHG6bDsERuwV01Qi9wcO0JaJecg3Z7JPZWmwS1R/1D0PQrgGZaLBJG++gkqK7Ii7F3H868ljO3qpztVaJKbi3p6AIZGRkiNjZWZGRkiJycHPHvf/9bhIWFiRUrVlh9zpw5cwSAGj+sQE3kJhKvgmy31attV+KV2THpynVi+0f7xUu9ssRdgblmh+KHCjE4dK9YrMkWp37M83ZT3c/S9zQ21rHP1N59uOK1HFVRIURWlqFadFaW4bHMsQL1HXFxcZg5cyamTp1qWvbaa6/h888/x5EjRyw+x1LPUFxcHEeTEbmTBEeiOMTWSDLA0Gt08aLkj6v8Vjmy38uB9vNirDncDhf0lcPc1SjBsIgcaO4vxZiXE9AsoakXW+pBriiZYKu44VdfGdazPIPLcDTZHbdu3aoxhN7f3x96vd7qc9RqNdRqtbubRkRVyX0kiq2RZIDh9plER5IVFxTjuzf3Q7uqAutPJeK66GFa1whFGNXiAFIfVOG+FxOVNwlqbYUJhTAEKbZmZrenuOHDDwP/+Q/w4ot1ey3yOMkHQ2PGjMH8+fPRokULdOrUCXv37sXbb7+NP/zhD95uGhH5EhmOJLt68hrWLzwI7foAfHchCbdROcw9QnUJKe0PQzOhHu5JS4I6pF8te5IRZ3ogXVEywZ5gWaczBES1UUh5BrmRfDD0/vvv4y9/+QumTJmCgoICREdHY/LkyXj11Ve93TRyN7nfdiF5kclIsnO7LmDNomPQ/tAI2deSoEPlMPdWAXmGSVCfDEPfpzrBP2iQF1vqBs5Ok+KKQNfVQbCEgmqSQTDUqFEjLF68mEPplcYXJ/4kaZNwmYBj3+VCu/g0Mrc1xf+KEwFUBmSdg49B0/s8NFOi0OV37aDyi/N4+zyiLnPguSLQdXUQ7CPlGXyF5BOoXYHTcciMHJIP2Wvlm4zfPcByQDRvHjB7tts/a6EX2JN+ZxLUXbE4VHqX2fq+DfcjdfAVjH0+Hnfd29KtbXGaK8+Ruk6TUpfpLuxtg70UOKVLXXjs+u3WsWoS4amheeQCFRU1h6NW/VGpDEO2vTlk1BtDZslzLH2+jnzWTg5vriitENmL94rnumaLFv55Zi8ZgDIxPHyn+MeELeL83nyXHKZbufocycqyXfIAMGxXW5tUKsNP9b8pKpV9bbOn9EL1fTv7WiSE8Nz126Fg6MyZM+5qh1sxGJIRV/zRcyfjH1RLf/T4R853VFQIMW+e9Quctc/awSDg9rXb4uu//CL+0HaraKq6ZPa0+rgpHoz5WXz+9I/i2qnrltsoxZoy7jhH0tPt+7uQnm67bXWth/XVV0L4+9v+h+3LL32j9paXSTIYql+/vnjllVfEzZs33dUet2AwJCOu+qPnDnLotSLXcOaztjMIKMwrFBnTfhLj434SDVFktmmY6op4rM02sWbWDnHryi3r7XNFz4sxmPr8cyHeecfwu65BlbvOEVf+k+SKIPLLL+0LlKUasMqIJIOhn376SfTu3VtERUWJTz75xF1tcjkGQzIi5Z4hKbeNXMvRz9pGEKBXqcSN+hFiVNPtIgglZqtj/M6LZzpni01v7hHlt8ttt80VPS+13Qr09u0sS4zvr6Xj9tY/Ir5SdV3iPHX99rOeTVRTv3798Msvv+CNN97Aq6++im7duiE7O9vFWUykaMYRPcZk6epUKiAuzjtzQ8mwDg05ydHP2kYNGpUQaHirADcvl6AMarQP+g0z+2TjfysOIa+8Od7PGYx7XuiGgGAbA3xtFQ8EDAX9dDrr+zAmiVtr79mzlVWUHeWuc8Q4Bx5Q82+Dt+bAS00FTp0CsrKA9HTD79xc7w/uIKc4FAwZPfroozh27BjGjBmDUaNGQaPR4MSJE65uGymRFP/oGcmkDo2i6XRAdjaQkWH4XVtQUBtHP2s7L+5piZtw6OuTOFLaGgu2D0GvxzpC5Wcl8LfEkeKBlthTRdnU2DTH3z93niPGyUhjYsyXx8Z6b4Spser6hAmG3xwdJl/OdikVFxeLbdu2ibS0NOHn5yfUarWYPn26KCoqcmXPlUvwNpkMSbELWopd9VTJlSOY7PysdSVl4qelOeLDdu945hZqXXPq7L2NJeXbWczDURRPXb8dKrq4dOlS7Ny5Ezt37sThw4fh7++PpKQkTJ06FV27dsV//vMfdOzYEVqtFj179nRP9EbKkJpqmLtHSrV8jL1W48YZeqmq/nft7V4rpatLQT5LavmsxZ3Hy8SfMK/+FVzQd4YfOmI03kIMzsEPFnpdXFWwsa49L47ennL2dpY7zxG5z4FH0uRI5BQbGyvGjRsn3nzzTfHjjz+KkpKSGtvMnz9fdOrUyWXRmiuwZ4hcSoq9VkrmzlF+Fj7rPMQIDVabFoXgupjQ8kfx0+jXhb6udWzsPVZne16c7RlytDeG5wi5iKeu3y6vQH3x4kVER0dD5+y9ejdgBWpyOVaglo7sbCA52fZ2WVkO9ShcPXkNX79xEGu/9sPNizcQjqu4gChsw0A087uClHZHoPl9fdyTloSghkGGJ1maRiYuztAb4qqcFmtVsu2p0G5vFeWqVZLXrnVuahyeI+QCnrp+uzwYEkJg69atGDx4sCt3WycMhoh8WEYGMHGi7e3S0w2JrrU4u/POJKibGmHLtSToqkzfGB9wBqldfoPmyTD0ebIT/INqmbbB3UFAXYIua7cUq1KpDEEVUPvUOCtXAs2aMeAht5FtMCRFDIaIfFgde4aOfvMbtO+egfZH4ySolZKCj0Jz9wVopkQhaVw7x0Z+uVtdgi5LwZSRMahKSbHdi+Tvbz7ijJMpk4sxGHIhBkNEPszBSTiFXmD354eh/WcBtLvicLisTeWm0KNfowPQDL6Ksc/Ho809LT13HJ5mDKbOnQMuXTL08MTEVAZV9gaZVUlpMmXyCQyGXIjBEJGPs5FHo0tfiW35bZG5vBBrDt6FPF1lrZpAlOGe8BxoRtxCyowOaJ4U4cmWS5e9tx+r46zs5EKeun47NLSeiFyMSaauYSzIV+3Wz61GEfikwTOYO/EeXBHhpuUNcBMjY/ZDk6LH/S8nonFLlgKpwdnioVULP3IIPMkEgyEib7GUt8GcC+elpqKw2xDsfP4/OLD1Kr671hvfFw2HvsgQXIapruKBNgehGR+EYS8koV5YX++2V+qMU+NYu/1oC6elIRlhMETkDa4uEqhgFw9cwto3DkP7XT1sutwF5ZhmWhfrfx6aTseheSwEA6d0RkCwi+a0U0KPXm0FFO3BaWlIRpgzRORptmq9MOfCpt+yz0D71m/QbgnDzzcSIapMs9gh6CQ0PfKgmRyBnpMSXD8CTGk9epaOt/oosqr4/SUXYgK1CzEYIklxU5FAXyb0Avszj0P7wXlod0RhX0l7s/W9GhyEpv8laJ5rgQ73t3bdC1fvAbp0CXjoIet1d3y1R6/6+3D5MjB+vGGdo4UfiRzABGoiX2VvLoXCcy70FXps/+gAtB9fhXZfPH6raAegHQDAHxUY1Hg/UocWIeXFtoi7u5PrG2CtR8TS/49CGAKBtDRDfR5f6xGxNB+YhYR1xMa6tto2kYcwGCLytLpOtukoGeW3lN0sQ9a7OdD+5xbWHm2PfH2SaV0wbmN48xxoRpVjzIyOCG/bzX0NsZbTVds0Q0obRSXFyZQ9SUbnFdnGYIjI02yN0nHVDOeALPJbbubfxLd/3w/taj02nE5EISqHuYegEKNbHkDq7/ww4oXOaNj8bvc3SKczvGfOZhA406Mn1wurUmeQl8F5RY5hMETkabWN0jHmXCxeXPeLoYRHrF05fhVfLzyEzPWB2HgxCSWoHObe3O8iUtofheb39ZH8XBKCGvb3bOO2bbM9kWltHO3R44VVXiR8XpHzmEBN5C3unOFcgiPW8n45jzV/Pw7tphBsvd7ZbBLUNgGnoemaC81T4ejzZCf4BfjVsic382TlZWsXViYiS5MEzytfx9FkLsRgyIvk2v3vKe56fyQyYu3w+pPQvpcH7U8R2HWro9m6LsFHoelzAZqp0eic2lY6k6B6ak4uXljlRyLnlZJwNBnJH7v/bXNXzoWXRqwJvcCuz+5Mgro7DkfK2gAwTISqgh79Q/ZDM+Qaxj7fGq2HtAfQvtb9eYU9lZctzdbuaI+erdtxSkvIlgOOBPVZDIbIPXhf3bs8OGKtoqQCWz/YD+2nRVhzqC3O6joCMPQCBaIM9zbdB82I20iZmYDIxC51fj23syen64svgKZN69ajxwur/Hh6JCh5DG+Tkeux+9/7jJ+BrRFrTn4Gt6/exsa3cqD9sgxfn+xYYxLU+2P3QzNW4P6XOiG0RWgdDsSL3JnTBfCWixy5+byimpgz5EIMhjzMF//IyzH3ydg7B7ikSvD104XYsPAAtOv88O25zihGQ9O6cNUVPHDXIWgeUmPo851RL6yeK47A+9z5ufPCKk8uPq+odswZIvnyte5/ueY+pabWuUpwfk4B1i48Au139bH5ShLKUTnMPc7/HDSJJ6B5PBQD/pzouklQpcSddXQ8VWLBSI4BvRS54Lwi6WHPELmevT1DP/xg+GMs5T/OvjD02cGL4MnNp6F9OxfarWHYXm0S1ISgk0jtlQfNnyPRfWIH6YwAkzN3346z9hpyCOiljMGlR/A2mQsxGPIwe7r/w8KAevWk/cdZIblPQi+Qs+oYtB9eQOYv0dhf0s5sfe8GB6AZcBma51qg/UgXToJKldx5YfWFgJ4Ui8GQCzEY8oLa7qtb+8pJ7Y+zo7lPMvpPUVemw/Z/HYT2k6vQ7muN3IoWpnX+qMDgJjlIHXoDKS+1Q2wvjoyRLYUE9OS7mDNE8mbtvnpMDHD7NnDlSs3nSG3mb0dyn2RwG6K0qBSbF+dAm3Eba48moECYT4I6onkONKPLMfrljghv292LLSWXYS0jIrswGCL3sTSrtU4HDB1q/TlS+uNsb62Q48eBuXMlWVPpZv5NfLNoP7SZenxzOgFdUIwoXEAC9ChHEkbFH4JmXABGvNgZDSI8MAkqeZavDWYgchMGQ+Re1UfjZGTY9zwp/HG2txLxe+9ZXu+lnq7LR69g3RuHoP2vGhsLklCKvtAgEzl4GHGo7CUQMTFQvfmeZHquyA1YJJDILl6cDZEUSU5/nI1Dn2uj01m+5WdUtafLjc5sP4f3HtyC5CZ7EdmhMZ5cMRDrC3qjFMF42u+fWI0HEQvz2yWq8+cNPVeZmW5tmyLodIYcs4wMw++qU3V4kzGgV1kZ9adSGUauDfTBsghEDmAwRJ4ltz/OqanAypV179VxQ0/X4fUnMX9YNno2OISW/WLwXOZgZF/vBj380bXeEcxLzkbOysP4IPo1qADUeMeNvVlpadK5eMtRZqYhSTk52TDbfXKy4bEUgsyqAX31c84dtYyIZEoWwdC5c+fwyCOPIDw8HPXr10fXrl2xe/dubzeLnFHbH2fAcIF++21p/XFu1qzuwYILerr0FXr8b/lBzOqbjQ7q39BxTBu88sMQ7L7VESroMTBkH95OycZvW/Kw91YHvLp5CDpHXITK3gRacpxx1GT199iYLyaFgMg4mCEmxnx5bKx0Rm4SeZnkc4auXbuG/v37Izk5Gd988w0iIiJw8uRJNG7c2NtNI2dZG2lm9PzzgJ+f+/5IOzoEvi69Osahy072dJXfKjdMgvrZDaw52A7n9J1M64JQinub5UBz32088LKVSVCZQOs+Op3hOyyhfDGrLA1mkHDpByJPk3wwtHDhQsTFxWH58uWmZa1atfJeg8g1UlMNF5Px42uuc+coLGeGwDvbq+PkbYjbV2/j+zdzoP2yHOt+64RronKYe0PcwP1x+6EZC9z/ciJCYnvVvjNX5WjJqIaSx8ht2Lo7pxYhkjnJF13s2LEjRowYgbNnz2LLli2IiYnBlClT8Mc//tHqc0pLS1FaWmp6XFRUhLi4OPuKNvGPvmd4oxicPZV4Lf33DNhXUTs42LCNkQNTKlw/XYj1bxyAdp0/vj3fGbfQwLSuqeoyHrjrMFInBuPetM4Ibhxs/zG7YjJQGdRQ8oqMDEOOkC3p6cCECe5vD5EP8ljRZCFxarVaqNVqMWvWLLFnzx6xdOlSERwcLD799FOrz5kzZ44AUOOnsLCw9hdbvVqI2FghDJcNw09srGE5uVZWlvn7bO0nK6vmcysqDMvT0w2/Kypsv15FRc3PtuqPSiVEeLgQMTGWP//Vqw3bqFQ1n6dSGdY72K7ze/PFhw9vEcPCdokAlJnttoV/nniua7bIXrxXlN8ud/jtNWNP220919L7Zeu5vq4u32EiskthYaF91+86knwwFBgYKPr27Wu2bNq0aaJPnz5Wn1NSUiIKCwtNP3l5ebbfTP7R96z0dPsuJOnp5s9zNmC198JV2+dv6bXj4hz6bhz/4ZRYdH+W6NswR6igM9tVR/VxMbt/ltj9+SGh1+kdf09r40zb7Qkg4+LsC0Z9kfH9sfR3g+8PkUt4KhiSfM5QVFQUOnbsaLYsISEBq1evtvoctVoNtVpt/4vIKRHSVziTy2LtNpc9OUbOJghX/fxzc20noVa7zSr6D8C+zJPI/OACtP+LwYHStgBamja/u8EBaAZehiatJdqNuAvAXc610xZnEmjllhPjacaRkePG1Zxzj8PWiWRF8sFQ//79cfToUbNlx44dQ8uWLa08wwn8o+95tqo7Vx+FZStgBYA//hEIDTV8RtUvQHUZ2l7987f2HbCQW3MBUfgrlkALQ5DmjwoMaZKD1OE3kPJiO8T0THS+XY5yNIGWI9FsszYyMjbW7nwxIvI+yQdDzz//PPr164fXX38d48ePx//+9z8sW7YMy5Ytc92LKOmPvlQSxB39r9pWwAoAV68a5j2zlNxrz9QattTy+Zd/vhIBkx42NL/K8ubIxyqMw98bv46o3w3A6BmdENbGy5Og2vsdkFO1cG/isHUi+XPrTTgX+frrr0ViYqJQq9WiQ4cOYtmyZQ493+Y9R6UkQkoxQdzeXBZ7c4xqy/Oylkhs70+1z7/oXJFYmfaTmBC3VeQhWuisPE8vpdwRR74DzBkiIi/zVM6Q5IfWu4LNoXmuGH4sdfYMK/dWl749PRXZ2YZpDuxl7TOzNkz89m1Dz5KNz//SsWtYt/AwtN+o8UNBEkoRjMHIRjbsaFtWlndvszr6HcjMBP70J8tzr0nhe0NEPs9TQ+sZDBkZLxSA5Vs2cv6j742aPq5mK2C1xlIAYin4WrvW6ucvBLCh9zy8eXQMthV2hh6V79Fdgacwr8XHmHjyNdtt8Wa9GUe/A9YCJ6PwcGDZMvmeE0QkC54KhmQxN5lH+PL8PY4kiEuVrTnNrLGU52NMJJ4woTLZ2srnny8i8SBWYcz//oIthV2hhz+61TuMv96Tjf2Zx3GspCUm/ute+9rizdwaR74DtSWrG9WrZ8iTISLyAZJPoPYoX02E9JUEcVtzmlliZwCir9Bj57V2WBO9AnkXCqDTGUaCbYNhNNug0F+hSS7E2LRWaCUuGt6rJgBEa8dHxnmDI98Be5LVz57l6Eoi8hkMhqrzxfl7fGlUkDFgzc42zGt29arl7VQqQy+PTmeYNsFCYFt+qxxbltyZBPVQO5zXJwIwDHUPQimGNsvBP0f+jAde7oCITl0Nt44eecTytBRSrzfjyHfAV4JnIiI7MWfIV9SWhOyrCeK15XkJYchrqZr8GxuL0r8twjdHWyPzy3Ksz+2Ea6KJaXVD3MCouP3QaICRLyUiJLbKd8We5GOgZq+VA/OTuZUj34Ft2+xLVvd2QjgR+TwmULuQzwdD9kyk6asJ4paOvXoQdIceAKDCOKwyFUFsprqEB9oeMUyC+nwS1CEWKpc7knwMSPc2q73fAV8NnolIdhgMuZBPB0OODJe2FDhIpeeiLqr2ikVEAI89BnHuHCylWeuhQj6i8GbXzzD2D+HoPzkR/kE2Luj2DuuXQ0+Jvd8BXw2eiUhWGAy5kM8GQ84Mma9rBWqpVLC24PjGU9j1yhpM+N/ztjd2JHDJyAAmTrS9nbWh81J7z+xtj68Gz0QkG566fjOBWs6cmVOtLgni9tyO8yChF/h15VFkfpgP7c4YHCxti4cRCbsq+TiS/Gtv8vHx4zWXSew9A2D/d8BXR1cSEVXDYEjOPDnqpy4zxruQrkyHn/55ANrl16HNaYPTug4AOgAAAlCO6EZFwA07duTIyLmBAw0j086dq327jz4CZs+uDBYk8p7ViS+OriQiqoZFF+XMU0Pm7ZkxPi3NsF1d6XSGHJ2MDMNvnQ6lRaXYMHcnnmq/DVHBVzH42S5YvHcwTutiUQ+3oInagX9P/hEFvxXjrWtPGXpeaivM6O8PXLpkf5v8/Q3TUthirL1jPA5PvWdERFQn7BmSM08V+3PmdpwzLNxSuuLfDM/q3kE6fm9a1lh1HQ/EH4BmfCCGv9AZ9Zv2Md+PseaPNTod8NBDlZWn7dG2rX3bGXvhPPWeuZPUcp2IiNyEwZCcGaeocHexP0/cjsvMhLhzS6lqn04T3WV8hkmopyqButNd0ExqhMHPdEZg/QHW95WaCnz5JfDww7X3vKSlGXJi7Hl/HO2Fk3vhQinmOhERuQlvk8mdJ+ZUc+PtuFM/nsW7YzejYNwUiGqBEAD4wbDso5h5+ODXARj6cncE1g+0veOmTWsPhBydj83YC2ft9ptKZRhpZeyFk3PVb2OuU/WeLWOuU2amd9pFROQm7BmSKkduUaSkAKGhhhwbwHDbxTgBqSu48Hac0AscXHsC2iXnoN0eib23EzAYJ/AcLlp9jgoCOOvgLSVX98w42gsnh/nKLLGV66RSOdajRkQkA+wZkqLMTEP9oORkQ32b5GTDY0v/kRu3HToUeO01w8/jjwNr17quPbXNGG/H7Th9hR47lu3DPxMWY3rQ+3gm9Rzmbh6IvbcT4Acd7qm33b52uGM4vCM9M470wtXxPfMaR3KdiIh8hVCAwsJCAUAUFhZ6uym2rV4thEolhOGyU/mjUhl+Vq92bltXtS021vy14uIsvk5ZcZn4fsEu8XSnLeIPqn+JMzB/XoFfhPgh+W+i4NAlIbKyah6DpZ+sLPvbWlFhaKul98f4HsXFGbZzVEWFoS3p6Ybfte3DgfdMEtLT7fss0tO93VIiUgBPXb9ZgVpKHJ0Dy9Hq065qo5Xbd7cu38J3f89B5lcVWH8qEddFY2iQiVUYB0CYd0NWndYhJcU9c2FJZUoJOY3K8qWpR4hI9jgdhwvJJhhy5EIESOKidfXkNaxfeBDa9QH47kISbqO+aV0k8nEoIAlNKi5ZnCfMLMhZu9Y9gQunlHAMJ2klIgnhdBxK5I7h2G4Yun1u1wWsWXQM2h8aYeu1TuiHCkQhD71Rgjz/VkjpcgqaJ5qgX/vL8B9eS3HDqvknqanAiy8Cb79tPgrMzw+YPt35wMXdyeW+xlPlGoiIJITBkJS4I+nXRUO3j32XC+3i09Bua4pfihMBREGDTJxECuJQ2esiomKhmv0ukDrEUEXaHhcuGHpw3nyzZm+ETmdY3qeP4wGRpV6hFStYK8cWY6K4pTpD7FEjIh/E22RS4sgtCsCttzOEXmBP+hFol16EdlcsDpXeZbZ+ZvA7eL1kuuGlqr8uYLiYhoXZdyvvhx8MI+Bcmf9kbV4wT+cLyZmccp2IyCcxZ8iFZBMMAY4l/bo4QVhXpsOP/9gP7YpCaPe3wRldrGldAMpxT/g+aIbfQsoLdyFq7N22g5cTJ4A2bWwHbMuXG0oD2GJv/pMjiei8uBMRSZanrt+sMyQ1jtSycUH16ZLrJVj/6v/wZLttaB58DUPSuuLdXwfjjC4W9VGM1Ogd+OzPP6Hgt2J8d7kn/pw+CFE3jtlXi+bnn+2rtVNQYLOdAOzPf2KtHCIicgBzhqQoNdWQ+GvPLQpHtr2j6GwR/rvoALRrgP/mdcZN9DatC1NdxZjWh6AZH4hh0y1Mggo4lug9YYLt/BNjcrMt9uY/yX1eMCIi8igGQ1Ll72//kHg7tr144BLWLToM7bf1sOlSEsrQz7Quxu8CxnY6Bs2kRhg01cYkqIDjid7VA7aICMPyggJDINSvn2unrpDzvGBERORxzBmyRcZJpLlb86B96yS02U3wU1Fns7KH7YN+g6b7GWj+1Aw9JyXAL8CBO6Z1qUVjbTb0CRMMo8aAuuc/sVYOEZFPYJ0hKbB24Zbo0GyhFzigPQ7tB+eh3dEcv97uACDOtL5H/UPQ9CtA6nNxSBjdBkBr517ImVo0Oh0wfz4wZ07N/Z07ZwiEXnzRMBy/rsO5WSuHiIgcwJ4ha2QyNFtfoceOjw9C+68r0P4aj5MVLU3r/KDDoMY50NxThLEv3oUWfWNq2ZMT7K3unJkJPPusIeixpuoItJ9/dk1PHKtPExHJGofWu5DDb6Y3h2bbcVuu7GYZst/fD+3nxVh7pB0u6Jub1qlRgmEROdDcX4oxLyegWUJT17bP0fZaCyqtcfX0ITK+zUlEpHS8TeZNjgzNduWFu5bbcsUDRuDbv+dAu0qP9acSUYgepk1CUIhRLQ9Ak+qH+15MRKPo3hZ27ia1JW/rdIbjcSTedvUIL0cS0YmISJEYDFnijaHZVnpQxNmzwIMP4kmkYyUmmJZHqC4hpf1haCbUwz1pSVCH9HddW1zFVlBpCUd4ERGRhzEYssTTQ7Nr6UFRAdBDhUWYgV3+fZDS9TQ0fwhD36c6wT9okGte310cCRYdHT5PRETkIgyGLBk40LV1b4Bac1fOvLkSLWrpQfGDQAvk4fj3p6C6x465vqTC0WCRI7yIiMgLGAxZ4uqh2RZygcqaROKriCmYn/t7dClTwZ753VUX82sulHKCsK2g0kjC5QqIiMj3cW4ya1ww7xcAUy6QqNbzE3CtABOOzkWHsn24BDtHfFXvacnMNIx6S04GJk40/G7VyrBcCoxBJVBzbjKjefOAU6cYCBERkddwaL0tdeh5KblSDBHfGsE3CmApFNADuN0wAuV7DqDxPd0dq5gskzpIAORR70fKPWxERArFOkMu5Kk3EwAKzxRiw8ID0K71w81z1/EN7rf9pKws4OpVQ3AD2J6Owpt1kJwl5WBDZpXGiYiUwlPXb9ndJluwYAFUKhXS0tK83RSTiwcuYdkjWzGy2U40a1kPv/+wP1ad64vGuG7fDi5ccOy2nCN1kKTCWO9nwgTDbykFQuPG1Xw/z50zLJfKLUciInIbWSVQ79y5E8uWLUNSUpK3m4Lfss9A+9Zv0G4Jw883EiFQOcy9Q9BJaHrk4dFBpcBCO3ZmbXZ3az0o3qiD5ItqKwophKGHLS3N8JlIJXgjIiKXk00wdPPmTfz+97/HRx99hNdee83jry/0AvszjZOgRmFfSXsALUzre9Y/BE3/AmieNU6C2sZwsf3PXxwbom9PxWRP10HyVd6qNE5ERJIim2Bo6tSpGDVqFIYOHWozGCotLUVpaanpcVFRkVOvqa/QY/tHB6D9+Cq0++LxW0U7AO0AAP6owKDG+6G5twhjX2qLuLs7AuhovgN3zZ7ujjpISsQeNiIigkyCoS+++AJ79uzBzp077dp+wYIFmDdvnlOvVXazDFnv5kD7n1tYe7Q98vWVt+TUKMHwyH1IHV2OMTM6IrxtN9s7NOYCWUrQdXY0lbuCLKVhDxsREUEGo8ny8vLQs2dPfP/99+jSpQsAYMiQIejatSsWL15s8TmWeobi4uKsZqPfzL+Jb/++H9rVemw4nYhChJrWhaAQo1segOZBP9z3Umc0bN7QuQNxx2gqOQxZlzLjqDxHShoQEZHHcGj9HWvWrIFGo4F/lYuRTqeDSqWCn58fSktLzdZZYunNvHL8Kr5eeAjaDYH4Pj8JJahn2j7SrwBj2x+B5vf1kfxcEoIaBrnn4FxBykPW5cA4mgywXdJAjvj9ICIZYzB0x40bN3D69GmzZU888QQ6dOiAGTNmIDEx0eY+jG/mwU1HsOnDfGg3hWDr9c7QVblL2DrgNDRdcqF5Mgx9nuwE/yBeMBTDV3vYWD+JiGSOwVAtbN0mq874ZgKFACrfzC7BR6HpcwGaqdHonNoWKj8rU0aQ7/O1HhQ5VSgnIrLCU8GQLBKoXUeP/o1yoBl8FZoXWqP1kPYA2nu7USQF9pQ0kAvWTyIicogsg6Hs7Gynnnfspyto28/7BRuJ3Ir1k4iIHCK76TjqIjKxmbebQOR+rJ9EROQQRQVDRIrA+klERA6R5W0ySfK1BFySL1YoJyJyCHuGXCEz01C8LzkZmDjR8LtVK854Tt5hrFAOVI4eM2KFciKiGhgM1VVmJvDggzUTVs+dMwxtZkBE3mCcBiYmxnx5bCyH1RMRVSPLOkOOcrpOga1bXzodEBkJXLli+fmczoG8jbdviUjGWGfI2+yp3jt/vvVACOAQZvI+X6qfRETkJrxNZomxem9tt750usq8DFs4hJmIiEiy2DNUnb3Ve0NDgatX7dsnhzATERFJFnuGqrO3eq+9VbDDwjiEmYiISMIYDFXn6ltazz3HhFUiIiIJYzBUnb23tIYMMSRUV6/jUlV4ODB7tkuaRURERO7BYKi6gQMNQYw1KhUQF2cIhqwVtjMuW7aMvUJEREQSx2CourVrbQ+XN1bvtVbYLi6Ohe2IiIhkgkUXq9LpDNNo1JZAHR4OXLxYs/giC9sRERG5FIsueoOtkWSAodeoehFFFrYjIiKSLd4mq8rekWQsokhEROQzGAxVZe9IMhZRJCIi8hkMhqoaOLD24fLGkWQsokhEROQzGAxV5e9vfbi88bFxJBkRERH5BAZD1VkbLh8by+HyREREPoijySxJTQVSUjhcnoiISAEYDFnD4fJERESKwNtkREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI1D632ZTsdaSURERDYwGPJVmZnAc88BZ89WLouNNUw3wiraREREJrxN5osyM4Fx48wDIQA4d86wPDPTO+0iIiKSIAZDvkanM/QICVFznXFZWpphO50OyM4GMjIMv3U6DzaUiIhIGnibzNds21azR6gqIYC8PGD+fOCjj3gbjYiIFI89Q77mwgX7tpszh7fRiIiIwGDI90RFOf/c6rfRiIiIFIDBkK8ZONBwu0ulcu75xtto27a5tl1EREQSxWDI1/j7G/J+gJoBkSMBkr2324iIiGRO8sHQggUL0KtXLzRq1AgREREYO3Ysjh496u1mSVtqKrBqFRATY748NhaYN8++fdTldhsREZGMqISwNAZbOu677z48/PDD6NWrFyoqKjB79mzs378fhw4dQoMGDezaR1FREUJDQ1FYWIiQkBA3t1hCLFWgBoBWrQzJ0pY+epXKEDTl5rJaNREReZWnrt+SD4aqu3TpEiIiIrBlyxYMGjTIrucoNhiyxliUETAPiIy30Vat4vB6IiLyOk9dvyV/m6y6wsJCAEBYWJjVbUpLS1FUVGT2Q1XUdhuNgRARESmMrHqGhBBISUnBtWvXsK2W0U5z587FPAu5MewZqoYTuRIRkYTxNpkFU6dOxYYNG/Djjz8iNjbW6nalpaUoLS01PS4qKkJcXByDISIiIhnxVDAkm+k4pk2bhnXr1mHr1q21BkIAoFaroVarPdQyIiIikjPJB0NCCEybNg1arRbZ2dmIj4/3dpOIiIjIh0g+GJo6dSrS09Oxdu1aNGrUCPn5+QCA0NBQ1KtXz8utIyIiIrmTfM6QykrV5OXLl+Pxxx+3ax8cWk9ERCQ/zBm6Q+KxGhEREcmc7OoMEREREbkSgyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkWTTTD04YcfIj4+HsHBwejRowe2bdvm7SYRERGRD5BFMLRy5UqkpaVh9uzZ2Lt3LwYOHIiRI0fizJkz3m4aERERyZxKCCG83Qhb7r77bnTv3h3/+Mc/TMsSEhIwduxYLFiwwObzi4qKEBoaisLCQoSEhLizqUREROQinrp+S75nqKysDLt378bw4cPNlg8fPhw///yzl1pFREREviLA2w2w5fLly9DpdIiMjDRbHhkZifz8fIvPKS0tRWlpqelxYWEhAEOESURERPJgvG67+yaW5IMhI5VKZfZYCFFjmdGCBQswb968Gsvj4uLc0jYiIiJynytXriA0NNRt+5d8MNS0aVP4+/vX6AUqKCio0VtkNGvWLEyfPt30+Pr162jZsiXOnDnj1jdTaoqKihAXF4e8vDxF5UrxuHncSsDj5nErQWFhIVq0aIGwsDC3vo7kg6GgoCD06NEDGzduhEajMS3fuHEjUlJSLD5HrVZDrVbXWB4aGqqoL5FRSEgIj1tBeNzKwuNWFqUet5+fe1OcJR8MAcD06dMxadIk9OzZE3379sWyZctw5swZ/PnPf/Z204iIiEjmZBEMPfTQQ7hy5Qr++te/4sKFC0hMTMR///tftGzZ0ttNIyIiIpmTRTAEAFOmTMGUKVOceq5arcacOXMs3jrzZTxuHrcS8Lh53ErA43bvccui6CIRERGRu0i+6CIRERGROzEYIiIiIkVjMERERESKxmCIiIiIFE2WwdCHH36I+Ph4BAcHo0ePHti2bVut22/ZsgU9evRAcHAwWrdujaVLl9bYZvXq1ejYsSPUajU6duwIrVbrruY7zZHjzszMxLBhw9CsWTOEhISgb9+++O6778y2WbFiBVQqVY2fkpISdx+KQxw57uzsbIvHdOTIEbPtfO3zfvzxxy0ed6dOnUzbyOHz3rp1K8aMGYPo6GioVCqsWbPG5nN84fx29Lh95fx29Lh95fx29Lh95fxesGABevXqhUaNGiEiIgJjx47F0aNHbT7PE+e47IKhlStXIi0tDbNnz8bevXsxcOBAjBw5EmfOnLG4fW5uLu6//34MHDgQe/fuxf/93//h2WefxerVq03bbN++HQ899BAmTZqEffv2YdKkSRg/fjx++eUXTx2WTY4e99atWzFs2DD897//xe7du5GcnIwxY8Zg7969ZtuFhITgwoULZj/BwcGeOCS7OHrcRkePHjU7prZt25rW+eLn/e6775odb15eHsLCwvC73/3ObDupf97FxcXo0qULlixZYtf2vnJ+O3rcvnJ+O3rcRnI/vx09bl85v7ds2YKpU6dix44d2LhxIyoqKjB8+HAUFxdbfY7HznEhM7179xZ//vOfzZZ16NBBzJw50+L2L7/8sujQoYPZssmTJ4s+ffqYHo8fP17cd999ZtuMGDFCPPzwwy5qdd05etyWdOzYUcybN8/0ePny5SI0NNRVTXQLR487KytLABDXrl2zuk8lfN5arVaoVCpx6tQp0zI5fN5VARBarbbWbXzl/K7KnuO2RI7nd1X2HLevnN9VOfN5+8L5LYQQBQUFAoDYsmWL1W08dY7LqmeorKwMu3fvxvDhw82WDx8+HD///LPF52zfvr3G9iNGjMCuXbtQXl5e6zbW9ulpzhx3dXq9Hjdu3Kgx2d3NmzfRsmVLxMbGYvTo0TX+s/Smuhx3t27dEBUVhXvvvRdZWVlm65TweX/88ccYOnRojSrtUv68neEL57cryPH8rgs5n9+u4Cvnd2FhIQDUOgmrp85xWQVDly9fhk6nqzFbfWRkZI1Z7Y3y8/Mtbl9RUYHLly/Xuo21fXqaM8dd3VtvvYXi4mKMHz/etKxDhw5YsWIF1q1bh4yMDAQHB6N///44fvy4S9vvLGeOOyoqCsuWLcPq1auRmZmJ9u3b495778XWrVtN2/j6533hwgV88803eOqpp8yWS/3zdoYvnN+uIMfz2xm+cH7Xla+c30IITJ8+HQMGDEBiYqLV7Tx1jstmOo6qVCqV2WMhRI1ltravvtzRfXqDs23MyMjA3LlzsXbtWkRERJiW9+nTB3369DE97t+/P7p37473338f7733nusaXkeOHHf79u3Rvn170+O+ffsiLy8Pb775JgYNGuTUPr3F2TauWLECjRs3xtixY82Wy+XzdpSvnN/Okvv57QhfOr+d5Svn9zPPPIOcnBz8+OOPNrf1xDkuq56hpk2bwt/fv0a0V1BQUCMqNGrevLnF7QMCAhAeHl7rNtb26WnOHLfRypUr8eSTT+LLL7/E0KFDa93Wz88PvXr1ksx/EnU57qr69Oljdky+/HkLIfDJJ59g0qRJCAoKqnVbqX3ezvCF87su5Hx+u4rczu+68JXze9q0aVi3bh2ysrIQGxtb67aeOsdlFQwFBQWhR48e2Lhxo9nyjRs3ol+/fhaf07dv3xrbf//99+jZsycCAwNr3cbaPj3NmeMGDP8xPv7440hPT8eoUaNsvo4QAr/++iuioqLq3GZXcPa4q9u7d6/ZMfnq5w0YRmucOHECTz75pM3Xkdrn7QxfOL+dJffz21Xkdn7XhdzPbyEEnnnmGWRmZmLz5s2Ij4+3+RyPneN2p1pLxBdffCECAwPFxx9/LA4dOiTS0tJEgwYNTFn1M2fOFJMmTTJt/9tvv4n69euL559/Xhw6dEh8/PHHIjAwUKxatcq0zU8//ST8/f3FG2+8IQ4fPizeeOMNERAQIHbs2OHx47PG0eNOT08XAQEB4oMPPhAXLlww/Vy/ft20zdy5c8W3334rTp48Kfbu3SueeOIJERAQIH755RePH581jh73O++8I7RarTh27Jg4cOCAmDlzpgAgVq9ebdrGFz9vo0ceeUTcfffdFvcph8/7xo0bYu/evWLv3r0CgHj77bfF3r17xenTp4UQvnt+O3rcvnJ+O3rcvnJ+O3rcRnI/v59++mkRGhoqsrOzzb63t27dMm3jrXNcdsGQEEJ88MEHomXLliIoKEh0797dbFjeY489JgYPHmy2fXZ2tujWrZsICgoSrVq1Ev/4xz9q7POrr74S7du3F4GBgaJDhw5mJ5dUOHLcgwcPFgBq/Dz22GOmbdLS0kSLFi1EUFCQaNasmRg+fLj4+eefPXhE9nHkuBcuXCjatGkjgoODRZMmTcSAAQPEhg0bauzT1z5vIYS4fv26qFevnli2bJnF/cnh8zYOnbb2vfXV89vR4/aV89vR4/aV89uZ77kvnN+WjhmAWL58uWkbb53jqjsNJCIiIlIkWeUMEREREbkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDRCRLGRkZCA4Oxrlz50zLnnrqKSQlJaGwsNCLLSMiueHcZEQkS0IIdO3aFQMHDsSSJUswb948/Otf/8KOHTsQExPj7eYRkYwEeLsBRETOUKlUmD9/PsaNG4fo6Gi8++672LZtGwMhInIYe4aISNa6d++OgwcP4vvvv8fgwYO93RwikiHmDBGRbH333Xc4cuQIdDodIiMjvd0cIpIp9gwRkSzt2bMHQ4YMwQcffIAvvvgC9evXx1dffeXtZhGRDDFniIhk59SpUxg1ahRmzpyJSZMmoWPHjujVqxd2796NHj16eLt5RCQz7BkiIlm5evUq+vfvj0GDBuGf//ynaXlKSgpKS0vx7bfferF1RCRHDIaIiIhI0ZhATURERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlK0/wcAvWfyqiS6mgAAAABJRU5ErkJggg==\n",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png
index fb4d54f55..4c6c4edd8 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png
index 0d0f832ac..a43e8750c 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
index 85e9ff71d..cb41148a1 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
@@ -116,20 +116,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.97739698]\n",
-      " [2.9189726 ]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27987128 4.51549827]\n",
+      "[[3.69887085]\n",
+      " [3.34977681]]\n",
+      "Eigenvalues of Hessian Matrix:[0.25923926 4.67193435]\n",
       "theta from own gd\n",
-      "[[3.97739698]\n",
-      " [2.9189726 ]]\n",
+      "[[3.69887085]\n",
+      " [3.34977681]]\n",
       "theta from own sdg\n",
-      "[[3.9577228 ]\n",
-      " [2.91473433]]\n"
+      "[[3.63685221]\n",
+      " [3.37369014]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -374,9 +374,9 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.32133765]\n",
-      " [2.59905073]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29426584 4.28858038]\n"
+      "[[3.64342603]\n",
+      " [3.30485583]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29030069 4.6608358 ]\n"
      ]
     },
     {
@@ -384,13 +384,13 @@
      "output_type": "stream",
      "text": [
       "theta from own gd\n",
-      "[[4.32133765]\n",
-      " [2.59905073]]\n"
+      "[[3.64342603]\n",
+      " [3.30485583]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -481,73 +481,73 @@
       "Own inversion\n",
       "[[4.]\n",
       " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30311767 4.03556032]\n",
-      "0 [-14.28624958] [-15.54071847]\n",
-      "1 [-0.04900086] [0.04473913]\n",
-      "2 [-0.04532032] [0.0413787]\n",
-      "3 [-0.04191624] [0.03827068]\n",
-      "4 [-0.03876784] [0.0353961]\n",
-      "5 [-0.03585592] [0.03273744]\n",
-      "6 [-0.03316272] [0.03027848]\n",
-      "7 [-0.03067182] [0.02800421]\n",
-      "8 [-0.02836801] [0.02590077]\n",
-      "9 [-0.02623724] [0.02395532]\n",
-      "10 [-0.02426651] [0.022156]\n",
-      "11 [-0.02244382] [0.02049182]\n",
-      "12 [-0.02075802] [0.01895265]\n",
-      "13 [-0.01919885] [0.01752908]\n",
-      "14 [-0.0177568] [0.01621244]\n",
-      "15 [-0.01642305] [0.0149947]\n",
-      "16 [-0.01518949] [0.01386842]\n",
-      "17 [-0.01404858] [0.01282674]\n",
-      "18 [-0.01299337] [0.0118633]\n",
-      "19 [-0.01201742] [0.01097223]\n",
-      "20 [-0.01111477] [0.01014809]\n",
-      "21 [-0.01027992] [0.00938585]\n",
-      "22 [-0.00950778] [0.00868086]\n",
-      "23 [-0.00879363] [0.00802883]\n",
-      "24 [-0.00813313] [0.00742577]\n",
-      "25 [-0.00752224] [0.00686801]\n",
-      "26 [-0.00695723] [0.00635214]\n",
-      "27 [-0.00643466] [0.00587502]\n",
-      "28 [-0.00595134] [0.00543374]\n",
-      "29 [-0.00550433] [0.0050256]\n",
+      "Eigenvalues of Hessian Matrix:[0.27335131 4.46000649]\n",
+      "0 [-13.27099835] [-15.86570776]\n",
+      "1 [0.01163425] [-0.00974702]\n",
+      "2 [0.01092119] [-0.00914964]\n",
+      "3 [0.01025184] [-0.00858886]\n",
+      "4 [0.00962351] [-0.00806245]\n",
+      "5 [0.00903369] [-0.00756831]\n",
+      "6 [0.00848002] [-0.00710445]\n",
+      "7 [0.00796028] [-0.00666902]\n",
+      "8 [0.0074724] [-0.00626028]\n",
+      "9 [0.00701442] [-0.00587659]\n",
+      "10 [0.00658451] [-0.00551642]\n",
+      "11 [0.00618095] [-0.00517832]\n",
+      "12 [0.00580212] [-0.00486095]\n",
+      "13 [0.00544651] [-0.00456302]\n",
+      "14 [0.0051127] [-0.00428336]\n",
+      "15 [0.00479935] [-0.00402083]\n",
+      "16 [0.0045052] [-0.0037744]\n",
+      "17 [0.00422908] [-0.00354307]\n",
+      "18 [0.00396988] [-0.00332591]\n",
+      "19 [0.00372657] [-0.00312207]\n",
+      "20 [0.00349817] [-0.00293072]\n",
+      "21 [0.00328377] [-0.0027511]\n",
+      "22 [0.00308251] [-0.00258249]\n",
+      "23 [0.00289358] [-0.00242421]\n",
+      "24 [0.00271624] [-0.00227563]\n",
+      "25 [0.00254976] [-0.00213616]\n",
+      "26 [0.00239349] [-0.00200523]\n",
+      "27 [0.00224679] [-0.00188233]\n",
+      "28 [0.00210909] [-0.00176697]\n",
+      "29 [0.00197982] [-0.00165867]\n",
       "theta from own gd\n",
-      "[[3.98320492]\n",
-      " [3.01533437]]\n",
-      "0 [-0.00509089] [0.00464812]\n",
-      "1 [-0.0047085] [0.00429899]\n",
-      "2 [-0.00424012] [0.00387135]\n",
-      "3 [-0.00378113] [0.00345227]\n",
-      "4 [-0.00335942] [0.00306724]\n",
-      "5 [-0.00298058] [0.00272135]\n",
-      "6 [-0.00264305] [0.00241318]\n",
-      "7 [-0.00234327] [0.00213947]\n",
-      "8 [-0.00207732] [0.00189665]\n",
-      "9 [-0.00184151] [0.00168135]\n",
-      "10 [-0.00163245] [0.00149047]\n",
-      "11 [-0.00144711] [0.00132125]\n",
-      "12 [-0.00128282] [0.00117125]\n",
-      "13 [-0.00113717] [0.00103827]\n",
-      "14 [-0.00100807] [0.00092039]\n",
-      "15 [-0.00089362] [0.0008159]\n",
-      "16 [-0.00079216] [0.00072326]\n",
-      "17 [-0.00070222] [0.00064115]\n",
-      "18 [-0.0006225] [0.00056836]\n",
-      "19 [-0.00055182] [0.00050383]\n",
-      "20 [-0.00048917] [0.00044663]\n",
-      "21 [-0.00043363] [0.00039592]\n",
-      "22 [-0.0003844] [0.00035097]\n",
-      "23 [-0.00034076] [0.00031112]\n",
-      "24 [-0.00030207] [0.0002758]\n",
-      "25 [-0.00026778] [0.00024449]\n",
-      "26 [-0.00023737] [0.00021673]\n",
-      "27 [-0.00021042] [0.00019212]\n",
-      "28 [-0.00018653] [0.00017031]\n",
-      "29 [-0.00016536] [0.00015097]\n",
+      "[[4.00679887]\n",
+      " [2.994304  ]]\n",
+      "0 [0.00185848] [-0.00155701]\n",
+      "1 [0.00174457] [-0.00146158]\n",
+      "2 [0.00160348] [-0.00134337]\n",
+      "3 [0.00146287] [-0.00122558]\n",
+      "4 [0.00133103] [-0.00111512]\n",
+      "5 [0.0012099] [-0.00101364]\n",
+      "6 [0.00109941] [-0.00092107]\n",
+      "7 [0.00099888] [-0.00083685]\n",
+      "8 [0.0009075] [-0.00076029]\n",
+      "9 [0.00082447] [-0.00069073]\n",
+      "10 [0.00074902] [-0.00062752]\n",
+      "11 [0.00068048] [-0.0005701]\n",
+      "12 [0.00061822] [-0.00051793]\n",
+      "13 [0.00056165] [-0.00047054]\n",
+      "14 [0.00051025] [-0.00042748]\n",
+      "15 [0.00046356] [-0.00038836]\n",
+      "16 [0.00042114] [-0.00035283]\n",
+      "17 [0.0003826] [-0.00032054]\n",
+      "18 [0.00034759] [-0.00029121]\n",
+      "19 [0.00031579] [-0.00026456]\n",
+      "20 [0.00028689] [-0.00024035]\n",
+      "21 [0.00026064] [-0.00021836]\n",
+      "22 [0.00023679] [-0.00019838]\n",
+      "23 [0.00021512] [-0.00018022]\n",
+      "24 [0.00019544] [-0.00016373]\n",
+      "25 [0.00017755] [-0.00014875]\n",
+      "26 [0.0001613] [-0.00013514]\n",
+      "27 [0.00014654] [-0.00012277]\n",
+      "28 [0.00013313] [-0.00011154]\n",
+      "29 [0.00012095] [-0.00010133]\n",
       "theta from own gd wth momentum\n",
-      "[[3.99951642]\n",
-      " [3.00044152]]\n"
+      "[[4.00040199]\n",
+      " [2.99966322]]\n"
      ]
     }
    ],
@@ -631,17 +631,17 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.66959644]\n",
-      " [3.26513904]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33285444 4.11450263]\n",
-      "0 [-12.48534921] [-14.7906583]\n",
-      "1 [-1.09712586e-14] [-5.19623863e-15]\n",
-      "2 [-1.27068495e-16] [-2.9424428e-16]\n",
-      "3 [5.91540705e-16] [7.2837521e-16]\n",
-      "4 [-1.27068495e-16] [-2.9424428e-16]\n",
+      "[[3.71369789]\n",
+      " [3.2314999 ]]\n",
+      "Eigenvalues of Hessian Matrix:[0.30237154 4.4642383 ]\n",
+      "0 [-17.75091492] [-21.33108943]\n",
+      "1 [-4.60742555e-15] [5.64228618e-16]\n",
+      "2 [-5.34294831e-16] [-5.79981535e-16]\n",
+      "3 [-5.34294831e-16] [-5.79981535e-16]\n",
+      "4 [-5.34294831e-16] [-5.79981535e-16]\n",
       "beta from own Newton code\n",
-      "[[3.66959644]\n",
-      " [3.26513904]]\n"
+      "[[3.71369789]\n",
+      " [3.2314999 ]]\n"
      ]
     }
    ],
@@ -711,24 +711,30 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.68184997]\n",
-      " [3.32507975]]\n",
-      "Eigenvalues of Hessian Matrix:[0.26370919 4.62518501]\n",
+      "[[4.39917327]\n",
+      " [2.69542733]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29765192 4.0375827 ]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "theta from own gd\n",
-      "[[3.68184997]\n",
-      " [3.32507975]]\n"
+      "[[4.39917327]\n",
+      " [2.69542733]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png"
       }
      },
      "output_type": "display_data"
@@ -738,8 +744,8 @@
      "output_type": "stream",
      "text": [
       "theta from own sdg\n",
-      "[[3.68809785]\n",
-      " [3.32032017]]\n"
+      "[[4.32234998]\n",
+      " [2.64530585]]\n"
      ]
     }
    ],
@@ -843,15 +849,21 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.55555773]\n",
-      " [3.41891092]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30326262 4.34133193]\n",
+      "[[3.7635689 ]\n",
+      " [3.10080981]]\n",
+      "Eigenvalues of Hessian Matrix:[0.31633433 3.9824638 ]\n",
       "theta from own gd\n",
-      "[[3.55511609]\n",
-      " [3.41928689]]\n",
+      "[[3.76366462]\n",
+      " [3.1007216 ]]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "theta from own sdg with momentum\n",
-      "[[3.58207928]\n",
-      " [3.37895549]]\n"
+      "[[3.75707243]\n",
+      " [3.12422141]]\n"
      ]
     }
    ],
@@ -975,9 +987,9 @@
      "output_type": "stream",
      "text": [
       "theta from own AdaGrad\n",
-      "[[2.00039962]\n",
-      " [2.99772199]\n",
-      " [4.00233436]]\n"
+      "[[1.99999956]\n",
+      " [3.00000215]\n",
+      " [3.99999797]]\n"
      ]
     }
    ],
@@ -1092,9 +1104,9 @@
      "output_type": "stream",
      "text": [
       "theta from own RMSprop\n",
-      "[[1.99858411]\n",
-      " [2.9981377 ]\n",
-      " [3.99861427]]\n"
+      "[[1.99907985]\n",
+      " [2.99897733]\n",
+      " [3.99754609]]\n"
      ]
     }
    ],
@@ -1205,9 +1217,9 @@
      "output_type": "stream",
      "text": [
       "theta from own ADAM\n",
-      "[[2.00002678]\n",
-      " [2.99985103]\n",
-      " [4.00014662]]\n"
+      "[[2.00003617]\n",
+      " [2.99986253]\n",
+      " [4.00012569]]\n"
      ]
     }
    ],
@@ -1347,7 +1359,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 11,
@@ -1436,7 +1448,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 12,
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png
index 811738404..236367843 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png
index dc0c79e9e..f4ba6bed0 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png and b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
index a77175aad..8fb2b92d3 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
@@ -438,7 +438,13 @@
       "Learning rate  =  1e-05\n",
       "Lambda =  1.0\n",
       "Accuracy score on data set:  0.5\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1e-05\n",
       "Lambda =  10.0\n",
       "Accuracy score on data set:  0.5\n",
@@ -542,6 +548,80 @@
       "Learning rate  =  0.1\n",
       "Lambda =  0.01\n",
       "Accuracy score on data set:  1.0\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
       "\n"
      ]
     },
@@ -571,80 +651,6 @@
       "  warnings.warn(\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on data set:  0.75\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on data set:  0.75\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on data set:  0.75\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n"
-     ]
-    },
     {
      "data": {
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\n",
@@ -654,7 +660,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_3.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_4.png"
       }
      },
      "output_type": "display_data"
@@ -10687,7 +10693,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adam: Eta=0.001, Lambda=0\n",
+      "Adam: Eta=0.001, Lambda=0"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       "\r",
       "  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_4.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_4.png
new file mode 100644
index 000000000..d6998a4a8
Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_4.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
index bcf40d45a..2a2fe3968 100644
--- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
@@ -225,9 +225,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-8.18243276e-01 -1.33525471e+00 -8.98409646e-01 -7.24434901e-01\n",
-      "  2.27832584e+00 -8.78192446e-01 -1.35539164e-03 -7.36097055e-01\n",
-      " -1.06125720e+00  2.86376300e+00]\n"
+      "[ 0.44079937 -0.14839786 -1.00862798 -0.22996417  0.53459992  0.28570701\n",
+      " -0.40043644  0.43989497 -0.27463692 -0.17644873]\n"
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@@ -663,36 +662,26 @@
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-      "  8.22979294e-01 7.32441343e-01]\n",
-      " [5.43608740e-01 2.76181397e-01 6.66722647e-01 7.35434042e-01\n",
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-      "  2.79167312e-01 2.27230624e-01]\n",
-      " [8.97033646e-02 1.88667234e-01 1.80076024e-01 8.65728698e-01\n",
-      "  2.79254918e-01 3.33457718e-01 9.52626194e-01 8.75240336e-01\n",
-      "  7.76059447e-01 3.52565509e-01]\n",
-      " [8.49556373e-03 1.50925722e-04 8.98413020e-01 7.49186362e-01\n",
-      "  1.90602444e-01 4.79877535e-01 6.10615323e-01 7.78031111e-02\n",
-      "  2.39856058e-01 3.20554718e-01]\n",
-      " [2.63843494e-01 1.46306318e-01 7.29933720e-01 5.27826845e-01\n",
-      "  1.33857909e-01 3.50359169e-01 4.20020918e-01 8.58581665e-03\n",
-      "  5.54378734e-02 8.68351374e-01]]\n"
+      "[[0.51727541 0.09312344 0.68284332 0.10399743 0.11110246 0.17604689\n",
+      "  0.83716603 0.72598009 0.89142728 0.54640368]\n",
+      " [0.33656494 0.55847112 0.87450434 0.13621148 0.38488879 0.68655761\n",
+      "  0.74147751 0.2544422  0.33708747 0.6848187 ]\n",
+      " [0.54213329 0.22197349 0.8702764  0.36674564 0.59816099 0.3225819\n",
+      "  0.18856622 0.88595314 0.46132345 0.55685628]\n",
+      " [0.88866133 0.65822169 0.05062537 0.20513942 0.41671085 0.48333258\n",
+      "  0.12786653 0.31532451 0.56548318 0.65766777]\n",
+      " [0.55206229 0.11704038 0.2856881  0.26961519 0.13371503 0.26805987\n",
+      "  0.34460089 0.09215672 0.53367133 0.34362409]\n",
+      " [0.34642944 0.54617756 0.22752605 0.86192438 0.49901588 0.98157799\n",
+      "  0.69802962 0.6513444  0.88050263 0.66725024]\n",
+      " [0.88657125 0.3591093  0.14600426 0.66667985 0.95921115 0.09607524\n",
+      "  0.0470705  0.42777999 0.65318467 0.59602968]\n",
+      " [0.37187359 0.6945612  0.7840642  0.78347558 0.60307141 0.95682858\n",
+      "  0.99534399 0.1859082  0.4281152  0.11232098]\n",
+      " [0.8459616  0.64874236 0.39148625 0.284499   0.35322418 0.37391132\n",
+      "  0.39837199 0.47950427 0.44298022 0.31921368]\n",
+      " [0.89881513 0.0062825  0.19942021 0.86850693 0.90715001 0.3426926\n",
+      "  0.98597638 0.71365128 0.79367372 0.86629645]]\n"
      ]
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    ],
@@ -811,13 +800,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.07291818479810824\n",
-      "4.313183599076104\n",
-      "-0.05687021620384533\n",
-      "[[ 0.87810129  2.59703606  2.52470105]\n",
-      " [ 2.59703606  8.59883217  6.83603568]\n",
-      " [ 2.52470105  6.83603568 13.87354403]]\n",
-      "[19.25091007  0.06413187  4.03543554]\n"
+      "-0.10095106250934528\n",
+      "3.73293228266057\n",
+      "-0.589971818845805\n",
+      "[[ 1.44967228  4.20536556  4.14179769]\n",
+      " [ 4.20536556 13.10814421 12.2161908 ]\n",
+      " [ 4.14179769 12.2161908  17.03056169]]\n",
+      "[28.710746    0.0846527   2.79297948]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb
index b150340ef..9afe15629 100644
--- a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb
@@ -143,7 +143,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11057/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8779/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection='3d')\n"
      ]
     },
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index 4bfac63d1..ece6a788f 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": [
-      "2.0994119523801045\n",
-      "[[12.84061976  8.96489054 20.01094579 11.7317759  10.16168848  9.47128712\n",
-      "   5.28962017 13.19870992 12.47831084 19.16089488]\n",
-      " [ 8.96489054  6.25898624 13.97097196  8.19073291  7.09455047  6.61253537\n",
-      "   3.69303559  9.21489709  8.71193859 13.37749489]\n",
-      " [20.01094579 13.97097196 31.18525109 18.28291282 15.83607342 14.76014528\n",
-      "   8.24339513 20.56899695 19.44632008 29.86052354]\n",
-      " [11.7317759   8.19073291 18.28291282 10.71868557  9.28418209  8.65339992\n",
-      "   4.83283826 12.0589434  11.40075395 17.50626752]\n",
-      " [10.16168848  7.09455047 15.83607342  9.28418209  8.04166112  7.49529781\n",
-      "   4.18604968 10.44507049  9.87496787 15.16336815]\n",
-      " [ 9.47128712  6.61253537 14.76014528  8.65339992  7.49529781  6.9860553\n",
-      "   3.90164278  9.7354157   9.20404676 14.13314468]\n",
-      " [ 5.28962017  3.69303559  8.24339513  4.83283826  4.18604968  3.90164278\n",
-      "   2.1790289   5.43713337  5.14036907  7.8932215 ]\n",
-      " [13.19870992  9.21489709 20.56899695 12.0589434  10.44507049  9.7354157\n",
-      "   5.43713337 13.56678624 12.82629718 19.69524041]\n",
-      " [12.47831084  8.71193859 19.44632008 11.40075395  9.87496787  9.20404676\n",
-      "   5.14036907 12.82629718 12.12622478 18.62025406]\n",
-      " [19.16089488 13.37749489 29.86052354 17.50626752 15.16336815 14.13314468\n",
-      "   7.8932215  19.69524041 18.62025406 28.59206948]]\n"
+      "2.860303069807892\n",
+      "[[24.75576092 22.38035637  6.45399416 23.02131025 23.17927079 13.79482449\n",
+      "  14.58098325  8.09978307 10.32858131  7.04416475]\n",
+      " [22.38035637 20.23288045  5.83471014 20.81233249 20.95513615 12.47116132\n",
+      "  13.18188532  7.32257967  9.33751667  6.36825174]\n",
+      " [ 6.45399416  5.83471014  1.68259989  6.00181115  6.04299253  3.59640396\n",
+      "   3.80136087  2.11166818  2.69273094  1.83646135]\n",
+      " [23.02131025 20.81233249  6.00181115 21.40837954 21.55527296 12.8283245\n",
+      "  13.55940301  7.53229196  9.60493501  6.55063291]\n",
+      " [23.17927079 20.95513615  6.04299253 21.55527296 21.7031743  12.91634595\n",
+      "  13.65244075  7.58397472  9.67083919  6.59558002]\n",
+      " [13.79482449 12.47116132  3.59640396 12.8283245  12.91634595  7.6869858\n",
+      "   8.12506251  4.51349834  5.75546705  3.92526882]\n",
+      " [14.58098325 13.18188532  3.80136087 13.55940301 13.65244075  8.12506251\n",
+      "   8.58810494  4.7707199   6.08346766  4.14896753]\n",
+      " [ 8.09978307  7.32257967  2.11166818  7.53229196  7.58397472  4.51349834\n",
+      "   4.7707199   2.65015024  3.37938584  2.3047648 ]\n",
+      " [10.32858131  9.33751667  2.69273094  9.60493501  9.67083919  5.75546705\n",
+      "   6.08346766  3.37938584  4.30928349  2.9389615 ]\n",
+      " [ 7.04416475  6.36825174  1.83646135  6.55063291  6.59558002  3.92526882\n",
+      "   4.14896753  2.3047648   2.9389615   2.00439232]]\n"
      ]
     }
    ],
@@ -2001,15 +2001,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.035513525941656535\n",
-      "4.0516821246279795\n",
-      "-0.00822879725131466\n",
-      "1.0340060287164625 10.447659635275407 10.449001126081919\n",
-      "3.1285896350792584 2.5721905504656455 7.8028954343792245\n",
-      "[[ 1.03400603  3.12858964  2.57219055]\n",
-      " [ 3.12858964 10.44765964  7.80289543]\n",
-      " [ 2.57219055  7.80289543 10.44900113]]\n",
-      "[19.14871402  0.07942491  2.70252786]\n"
+      "0.07023654656164897\n",
+      "4.208190393562401\n",
+      "-0.023810076900619058\n",
+      "1.0331134070762626 10.076560707521647 14.512204707520711\n",
+      "3.055706923889776 3.0768224464930487 8.922010623244745\n",
+      "[[ 1.03311341  3.05570692  3.07682245]\n",
+      " [ 3.05570692 10.07656071  8.92201062]\n",
+      " [ 3.07682245  8.92201062 14.51220471]]\n",
+      "[22.35796655  0.08015655  3.18375572]\n"
      ]
     }
    ],
@@ -2638,7 +2638,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -2764,12 +2764,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.024318244280276506 1.0399587275832265\n"
+      "-0.006719367598355617 1.0020717457079393\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png
index 6995567e8..296068411 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png
index ac8fce949..72b91adb1 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
index fc53b3d05..321765720 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -985,8 +985,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.98502634 -1.56822376 -0.45668633  1.26063304 -1.43801947 -0.72264336\n",
-      "  0.12428533 -0.64472123 -0.99439119  0.84257054]\n"
+      "[ 1.303107    1.38160211  0.19790229  1.36099915 -2.08992459 -0.86156954\n",
+      "  2.9012691  -0.18410452 -1.06886644  0.01011906]\n"
      ]
     }
    ],
@@ -1424,26 +1424,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.36681298 0.62199022 0.32023229 0.08231145 0.09917246 0.29025302\n",
-      "  0.85115237 0.79516409 0.833774   0.85910255]\n",
-      " [0.08990571 0.56425249 0.34440086 0.37540613 0.30085673 0.93901621\n",
-      "  0.00790262 0.92604308 0.7742213  0.58486384]\n",
-      " [0.57811941 0.84268865 0.11339075 0.57329374 0.78094722 0.46156624\n",
-      "  0.2545724  0.1095957  0.6559956  0.22291364]\n",
-      " [0.12049203 0.50172141 0.38493367 0.62633359 0.13880371 0.37092452\n",
-      "  0.21913628 0.78478186 0.12625715 0.89142357]\n",
-      " [0.69018454 0.02200532 0.63281889 0.28622606 0.84900747 0.44440345\n",
-      "  0.5302517  0.15957051 0.10154612 0.7025846 ]\n",
-      " [0.26518597 0.48577692 0.68736603 0.10401756 0.88473534 0.31949465\n",
-      "  0.00434364 0.20240089 0.46285399 0.64432571]\n",
-      " [0.39229856 0.66289428 0.2591811  0.68871199 0.37021881 0.32041353\n",
-      "  0.93049763 0.30690504 0.63212587 0.56397327]\n",
-      " [0.31212802 0.82402448 0.94610136 0.19407473 0.28535441 0.99933329\n",
-      "  0.55331574 0.96439516 0.4809676  0.21947455]\n",
-      " [0.66489687 0.7613959  0.33285905 0.20726939 0.74587018 0.97375628\n",
-      "  0.62642303 0.95762994 0.22169909 0.82717721]\n",
-      " [0.8470287  0.16761991 0.74042291 0.42143986 0.70262543 0.8964059\n",
-      "  0.69629255 0.05916189 0.89940861 0.19010909]]\n"
+      "[[0.27204759 0.8509716  0.98844754 0.6522099  0.81868633 0.74934715\n",
+      "  0.53632379 0.2257879  0.88228452 0.33213799]\n",
+      " [0.82550815 0.97032289 0.54852248 0.45000312 0.45227801 0.44437409\n",
+      "  0.66608227 0.37871763 0.10320791 0.55812916]\n",
+      " [0.10455569 0.18044829 0.92543216 0.81873184 0.05924492 0.00620039\n",
+      "  0.03106988 0.61631038 0.56830193 0.46580623]\n",
+      " [0.97991527 0.62460522 0.51635486 0.08776426 0.80418542 0.94035843\n",
+      "  0.63870745 0.64347512 0.03019138 0.45811552]\n",
+      " [0.82050873 0.4757488  0.95696156 0.2970942  0.56217428 0.16487517\n",
+      "  0.2522939  0.44886896 0.66951925 0.56135704]\n",
+      " [0.82178144 0.58639705 0.0944958  0.69585594 0.08191117 0.42487977\n",
+      "  0.29009852 0.08368077 0.6691852  0.48444949]\n",
+      " [0.55790428 0.58519863 0.35795044 0.65925306 0.53506617 0.60090208\n",
+      "  0.22830615 0.30506642 0.79142002 0.68029581]\n",
+      " [0.12867125 0.34373214 0.89558707 0.05629549 0.54342461 0.10888134\n",
+      "  0.27485633 0.97326759 0.10477501 0.80621648]\n",
+      " [0.56756375 0.18276764 0.83519995 0.23044077 0.43559429 0.0934955\n",
+      "  0.5712104  0.92054587 0.10120164 0.69666941]\n",
+      " [0.17456211 0.42323635 0.03955811 0.54969188 0.5793788  0.49423098\n",
+      "  0.78834469 0.80312429 0.94756925 0.83793923]]\n"
      ]
     }
    ],
@@ -1557,13 +1557,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.1804736801658276\n",
-      "3.577421319924605\n",
-      "-0.13894606338836166\n",
-      "[[ 0.93900613  3.09173024  2.71615146]\n",
-      " [ 3.09173024 11.22689573  9.13261905]\n",
-      " [ 2.71615146  9.13261905 12.06931309]]\n",
-      "[21.60398689  0.07438088  2.55684718]\n"
+      "-0.14482255345953607\n",
+      "3.467427755242117\n",
+      "-0.7005538702846336\n",
+      "[[ 0.82032378  2.41772265  2.45808919]\n",
+      " [ 2.41772265  8.23849741  7.23077531]\n",
+      " [ 2.45808919  7.23077531 13.06343533]]\n",
+      "[18.91591367  0.08817972  3.11816312]\n"
      ]
     }
    ],
@@ -1916,7 +1916,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11068/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8790/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
       "  data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))\n"
      ]
     },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index 0cb2574ec..bc565ea71 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -1519,7 +1519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9955273625597437\n"
+      "0.996738628265756\n"
      ]
     }
    ],
@@ -1550,7 +1550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.008900933315885705\n"
+      "0.00846262916105675\n"
      ]
     }
    ],
@@ -1585,23 +1585,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.00643899 0.04246989 0.0607062  0.02997344 0.0011878  0.00123457\n",
-      " 0.00324986 0.03285652 0.01028728 0.01571866 0.03940381 0.0814985\n",
-      " 0.038844   0.01828593 0.03967758 0.00303693 0.02227466 0.02702328\n",
-      " 0.00026861 0.00883798 0.02876697 0.00472251 0.03141454 0.02911162\n",
-      " 0.02387339 0.00585113 0.00110716 0.00587564 0.00693821 0.00604105\n",
-      " 0.00804985 0.02058094 0.01151984 0.01782721 0.0286851  0.08874631\n",
-      " 0.01678538 0.0065912  0.03611471 0.02706508 0.00313125 0.04977093\n",
-      " 0.00415289 0.02760079 0.00518122 0.00628874 0.00739489 0.01619456\n",
-      " 0.00996972 0.02210753 0.02030107 0.02100763 0.04699527 0.01512934\n",
-      " 0.00717079 0.01784714 0.01095703 0.01281486 0.0231703  0.04482932\n",
-      " 0.00287871 0.0565419  0.04028659 0.03102525 0.01617722 0.0271761\n",
-      " 0.01736502 0.03394827 0.00328494 0.05750876 0.0059888  0.01915888\n",
-      " 0.01423609 0.01024227 0.03660869 0.01012951 0.00534938 0.03375068\n",
-      " 0.02699539 0.04083439 0.04965227 0.00565625 0.02250553 0.00893027\n",
-      " 0.02244755 0.00741987 0.00189101 0.02042476 0.02036545 0.06362348\n",
-      " 0.03145163 0.02987833 0.07393685 0.0033575  0.02791218 0.00214832\n",
-      " 0.0111154  0.01344581 0.00368581 0.01436601]\n"
+      "[2.18321314e-02 4.63790586e-02 1.86599003e-02 2.57537966e-02\n",
+      " 7.75301638e-03 2.41708096e-03 2.05388549e-02 2.24797183e-02\n",
+      " 3.80669838e-02 1.77124395e-02 5.74437617e-02 8.91992985e-03\n",
+      " 3.79831624e-02 1.33444711e-02 2.46753261e-02 2.04450975e-02\n",
+      " 8.98180203e-02 1.21522960e-02 3.12747234e-03 1.31395784e-03\n",
+      " 1.84447599e-03 2.96525482e-03 6.28909679e-03 1.52006777e-02\n",
+      " 2.87135280e-03 2.40513177e-02 3.35417405e-02 5.92991719e-03\n",
+      " 3.54379087e-02 7.18303628e-03 1.54601264e-02 2.15148810e-02\n",
+      " 2.79754897e-03 4.12602928e-03 4.22227163e-02 3.09676156e-02\n",
+      " 1.25713219e-02 2.32108713e-02 2.44657526e-02 1.05066388e-02\n",
+      " 6.68324974e-02 2.97565845e-02 2.42484290e-02 1.89707309e-02\n",
+      " 2.19461919e-02 1.41644629e-02 1.41226929e-02 5.23396766e-03\n",
+      " 3.21530495e-03 3.66036618e-03 7.91408373e-03 3.18065689e-02\n",
+      " 5.10582403e-02 6.76220793e-03 3.09797549e-02 1.01612033e-02\n",
+      " 4.64257697e-02 1.98270777e-02 2.88088818e-02 5.94948363e-03\n",
+      " 8.94501598e-03 4.64365518e-03 4.55438359e-02 3.34347894e-03\n",
+      " 4.97761429e-03 2.71875845e-02 1.57316402e-02 4.15628391e-02\n",
+      " 4.75979803e-02 8.77079389e-03 3.22623101e-03 2.53596681e-03\n",
+      " 4.02206965e-02 3.06020683e-02 3.07080407e-02 9.75525377e-03\n",
+      " 6.45380691e-02 2.66174067e-02 1.94727053e-03 4.82766482e-03\n",
+      " 3.39313789e-03 5.00126617e-02 3.25794223e-02 3.97663980e-02\n",
+      " 3.51267283e-02 4.43226747e-02 4.45976616e-03 2.86750237e-02\n",
+      " 2.33197004e-02 9.78449688e-05 4.38688646e-02 2.86830766e-02\n",
+      " 2.90763970e-02 9.24053124e-03 1.36970119e-02 6.97177697e-02\n",
+      " 2.34728094e-02 2.31728952e-02 3.08484802e-03 6.25254477e-02]\n"
      ]
     }
    ],
@@ -1655,15 +1663,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.09851217 -1.48209629 10.27096183 -6.79998516  2.87206824]\n",
+      "[ 1.97864285  0.28134042  4.70594499 -0.58368727  0.70917314]\n",
       "Training R2\n",
-      "0.9957273060382023\n",
+      "0.993658072083743\n",
       "Training MSE\n",
-      "0.010053880703541525\n",
+      "0.012874822204495243\n",
       "Test R2\n",
-      "0.9888005551376943\n",
+      "0.9945729062189713\n",
       "Test MSE\n",
-      "0.008043926731954223\n"
+      "0.007472516848671787\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
index 9b86b5406..14b9a2f77 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
@@ -2023,7 +2023,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 99.9889  15.1336        99.9892        0.150749\n"
+      " 100.038  14.9172        100.039        0.150218\n"
      ]
     }
    ],
@@ -2089,7 +2089,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -2473,18 +2473,18 @@
       "Error: 0.026605727637184558\n",
       "Bias^2: 0.010018312644139219\n",
       "Var: 0.016587414993045335\n",
-      "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n"
+      "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n",
+      "Polynomial degree: 10\n",
+      "Error: 0.021592704588021178\n",
+      "Bias^2: 0.010516485576646504\n",
+      "Var: 0.01107621901137467\n",
+      "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Polynomial degree: 10\n",
-      "Error: 0.021592704588021178\n",
-      "Bias^2: 0.010516485576646504\n",
-      "Var: 0.01107621901137467\n",
-      "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n",
       "Polynomial degree: 11\n",
       "Error: 0.07160048164232538\n",
       "Bias^2: 0.014436800088896381\n",
@@ -2494,7 +2494,13 @@
       "Error: 0.11547777218876518\n",
       "Bias^2: 0.016285782696017142\n",
       "Var: 0.09919198949274803\n",
-      "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n",
+      "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 13\n",
       "Error: 0.2284246870217162\n",
       "Bias^2: 0.01975416527168255\n",
@@ -2511,7 +2517,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_162_3.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_162_4.png"
       }
      },
      "output_type": "display_data"
@@ -2987,16 +2993,16 @@
       "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",
-      "Degree of polynomial:  14\n",
-      "Mean squared error on training data: 0.00465099\n",
-      "Mean squared error on test data: 0.28443039\n"
+      "Mean squared error on test data: 0.56965674\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  14\n",
+      "Mean squared error on training data: 0.00465099\n",
+      "Mean squared error on test data: 0.28443039\n",
       "Degree of polynomial:  15\n",
       "Mean squared error on training data: 0.00420072\n",
       "Mean squared error on test data: 568.47202442\n",
@@ -3011,16 +3017,16 @@
       "Mean squared error on test data: 429.23643365\n",
       "Degree of polynomial:  19\n",
       "Mean squared error on training data: 0.00154860\n",
-      "Mean squared error on test data: 238.16356503\n",
-      "Degree of polynomial:  20\n",
-      "Mean squared error on training data: 0.00140849\n",
-      "Mean squared error on test data: 1345.68592431\n"
+      "Mean squared error on test data: 238.16356503\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  20\n",
+      "Mean squared error on training data: 0.00140849\n",
+      "Mean squared error on test data: 1345.68592431\n",
       "Degree of polynomial:  21\n",
       "Mean squared error on training data: 0.00119699\n",
       "Mean squared error on test data: 1836.21110005\n",
@@ -3035,16 +3041,16 @@
       "Mean squared error on test data: 1346.92651068\n",
       "Degree of polynomial:  25\n",
       "Mean squared error on training data: 0.00079910\n",
-      "Mean squared error on test data: 7697.35412147\n",
-      "Degree of polynomial:  26\n",
-      "Mean squared error on training data: 0.00075597\n",
-      "Mean squared error on test data: 1078.81597834\n"
+      "Mean squared error on test data: 7697.35412147\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  26\n",
+      "Mean squared error on training data: 0.00075597\n",
+      "Mean squared error on test data: 1078.81597834\n",
       "Degree of polynomial:  27\n",
       "Mean squared error on training data: 0.00068088\n",
       "Mean squared error on test data: 3189.20355156\n",
@@ -3060,9 +3066,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8815/626635268.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_11090/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8815/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -3197,7 +3203,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8815/3817475779.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_144_0.png b/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png
index 4d8d3f41c..3de58fe37 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png and b/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
index b8c454dd8..ecce146e1 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
@@ -1679,7 +1679,7 @@
      "output_type": "stream",
      "text": [
       "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
-      "                   param_distributions={'alpha': })\n",
+      "                   param_distributions={'alpha': })\n",
       "Best estimated lambda-value: 0.9849967686928113\n",
       "MSE score: 1.0853136633465326\n",
       "R2 score: -0.0002382102844775691\n"
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
index 5fdb1fa92..22d5c4c34 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
@@ -1160,14 +1160,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11106/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8843/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -1337,7 +1337,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
index 65b0f85cf..196de69c8 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
@@ -485,20 +485,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.88391015]\n",
-      " [3.15024162]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29734306 4.63081005]\n",
+      "[[4.34459931]\n",
+      " [2.77172582]]\n",
+      "Eigenvalues of Hessian Matrix:[0.28795864 4.47391428]\n",
       "theta from own gd\n",
-      "[[3.88391015]\n",
-      " [3.15024162]]\n",
+      "[[4.34459931]\n",
+      " [2.77172582]]\n",
       "theta from own sdg\n",
-      "[[3.92822216]\n",
-      " [3.17648722]]\n"
+      "[[4.35401107]\n",
+      " [2.71654553]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png b/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png
index cb9eb54d5..02f3f677a 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png and b/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week41.ipynb b/doc/LectureNotes/_build/jupyter_execute/week41.ipynb
index 7b25afae9..e6d055172 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week41.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week41.ipynb
@@ -2985,7 +2985,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3556,7 +3556,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3574,7 +3574,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3592,7 +3592,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3610,7 +3610,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3628,7 +3628,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3646,7 +3646,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3664,7 +3664,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3682,11 +3682,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3704,11 +3704,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3726,11 +3726,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3748,11 +3748,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3770,11 +3770,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3792,7 +3792,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3810,11 +3810,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3832,11 +3832,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3854,11 +3854,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3876,11 +3876,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3898,11 +3898,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3920,11 +3920,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3942,11 +3942,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -3964,11 +3964,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4024,15 +4024,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74620/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8855/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4666,6 +4666,10 @@
       "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"
      ]
     },
@@ -4673,10 +4677,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.17222222222222222\n",
-      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.11666666666666667\n",
@@ -4688,13 +4688,7 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1388888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.11388888888888889\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb
index 46d6f7dc3..a173fed9a 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb
@@ -1146,7 +1146,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1717,7 +1717,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1735,7 +1735,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1753,7 +1753,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1771,7 +1771,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1789,7 +1789,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1807,7 +1807,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1825,7 +1825,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1843,11 +1843,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1865,11 +1865,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1887,11 +1887,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1909,11 +1909,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1931,11 +1931,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1953,7 +1953,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1971,11 +1971,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1993,11 +1993,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2015,11 +2015,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2037,11 +2037,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2059,11 +2059,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2081,11 +2081,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2103,11 +2103,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2125,11 +2125,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2185,15 +2185,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_74630/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -2773,20 +2773,10 @@
       "Learning rate  =  0.1\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.8805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  0.1\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.8722222222222222\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.8666666666666667\n",
       "\n"
      ]
     },
@@ -2794,10 +2784,20 @@
      "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",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
@@ -2815,10 +2815,6 @@
       "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"
      ]
     },
@@ -2826,6 +2822,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.08333333333333333\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.08888888888888889\n",
@@ -3367,7 +3367,13 @@
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on data set:  0.5\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on data set:  0.5\n",
@@ -3429,7 +3435,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week42_88_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week42_88_3.png"
       }
      },
      "output_type": "display_data"
diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
index 9a7520f62..6422571b2 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
@@ -580,13 +580,7 @@
       "Learning rate  =  1e-05\n",
       "Lambda =  1.0\n",
       "Accuracy score on data set:  0.5\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1e-05\n",
       "Lambda =  10.0\n",
       "Accuracy score on data set:  0.5\n",
@@ -614,7 +608,13 @@
       "Learning rate  =  0.0001\n",
       "Lambda =  1.0\n",
       "Accuracy score on data set:  0.5\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.0001\n",
       "Lambda =  10.0\n",
       "Accuracy score on data set:  0.5\n",
@@ -10904,13 +10904,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adam: Eta=0.001, Lambda=0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Adam: Eta=0.001, Lambda=0\n",
       "\r",
       "  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
      ]
@@ -43596,21 +43590,8 @@
       " 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": [
-      "predictions = (n_inputs) = (1437,)"
-     ]
-    },
-    {
-     "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"
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week44.ipynb b/doc/LectureNotes/_build/jupyter_execute/week44.ipynb
index e376726cf..1c843ac41 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week44.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week44.ipynb
@@ -1967,7 +1967,7 @@
      "text": [
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
       "  super(SGD, self).__init__(name, **kwargs)\n",
-      "2023-11-06 06:34:51.825607: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-11-08 15:30:42.160913: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
index 041dd6334..96715bc38 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "31a10e2b",
+   "id": "7e9869f2",
    "metadata": {
     "editable": true
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@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "019daa83",
+   "id": "fca263d7",
    "metadata": {
     "editable": true
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@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ecf1c2b",
+   "id": "8c3d9fea",
    "metadata": {
     "editable": true
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@@ -42,6 +42,8 @@
     "\n",
     "  * [Video of lab session from week 44](https://youtu.be/EajWMW__k0I)\n",
     "\n",
+    "  * [Video of lab session from week 45](https://youtu.be/tgkj0KAEtZo)\n",
+    "\n",
     "  * [See also whiteboard notes from lab session week 44](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/Exercisesweek44.pdf)\n",
     "\n",
     "  \n",
@@ -67,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d80a4db8",
+   "id": "d0107e8f",
    "metadata": {
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@@ -77,7 +79,7 @@
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   {
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-   "id": "1ded5c92",
+   "id": "44ed2bb7",
    "metadata": {
     "editable": true
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@@ -96,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6dea0513",
+   "id": "7d202a1c",
    "metadata": {
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     "editable": true
@@ -168,7 +170,7 @@
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    "metadata": {
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@@ -180,7 +182,7 @@
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-   "id": "683b1da2",
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     "editable": true
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@@ -195,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "38be79e0",
+   "id": "cdd8cf73",
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     "editable": true
@@ -262,7 +264,7 @@
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@@ -277,7 +279,7 @@
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@@ -297,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0a7e4e2e",
+   "id": "0a320c52",
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     "editable": true
@@ -308,7 +310,7 @@
      "output_type": "stream",
      "text": [
       "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
-      "                   param_distributions={'alpha': })\n",
+      "                   param_distributions={'alpha': })\n",
       "Best estimated lambda-value: 0.9849967686928113\n",
       "MSE score: 1.0853136633465326\n",
       "R2 score: -0.0002382102844775691\n"
@@ -363,7 +365,7 @@
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   {
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-   "id": "dcd07cb8",
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@@ -378,7 +380,7 @@
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-   "id": "65061d95",
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     "editable": true
@@ -429,7 +431,7 @@
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@@ -443,7 +445,7 @@
   {
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-   "id": "08a2ab17",
+   "id": "96a74e80",
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     "editable": true
@@ -517,7 +519,7 @@
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@@ -542,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "76c3d259",
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     "collapsed": false,
     "editable": true
@@ -554,7 +556,7 @@
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@@ -565,7 +567,7 @@
   {
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    "execution_count": 7,
-   "id": "58ccd6c3",
+   "id": "382459a0",
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     "editable": true
@@ -577,7 +579,7 @@
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-   "id": "adda54cb",
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@@ -590,7 +592,7 @@
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+   "id": "2d846128",
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@@ -623,7 +625,7 @@
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@@ -637,7 +639,7 @@
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-   "id": "65f7c63d",
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@@ -649,7 +651,7 @@
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@@ -661,7 +663,7 @@
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@@ -673,7 +675,7 @@
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@@ -683,7 +685,7 @@
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@@ -695,7 +697,7 @@
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@@ -705,7 +707,7 @@
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@@ -717,7 +719,7 @@
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-   "id": "0b571e85",
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@@ -728,7 +730,7 @@
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@@ -747,7 +749,7 @@
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@@ -759,7 +761,7 @@
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@@ -769,7 +771,7 @@
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@@ -781,7 +783,7 @@
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@@ -793,7 +795,7 @@
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@@ -805,7 +807,7 @@
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@@ -815,7 +817,7 @@
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@@ -827,7 +829,7 @@
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@@ -855,7 +857,7 @@
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@@ -867,7 +869,7 @@
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@@ -892,7 +894,7 @@
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@@ -910,7 +912,7 @@
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@@ -921,7 +923,7 @@
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-   "id": "2a6f80df",
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     "editable": true
@@ -1112,7 +1114,7 @@
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@@ -1122,7 +1124,7 @@
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@@ -1149,7 +1151,7 @@
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@@ -1160,7 +1162,7 @@
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-   "id": "8b3ca785",
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@@ -1289,14 +1291,14 @@
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-      "2023-11-08 06:51:57.259901: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-11-08 15:31:22.964588: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 3s - loss: 2.5680 - 3s/epoch - 69ms/step\n"
+      "50/50 - 3s - loss: 1.3549 - 3s/epoch - 66ms/step\n"
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     {
@@ -1310,7 +1312,7 @@
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      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 1.8934 - 563ms/epoch - 11ms/step\n"
+      "50/50 - 0s - loss: 0.4772 - 473ms/epoch - 9ms/step\n"
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@@ -1324,7 +1326,7 @@
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-      "50/50 - 1s - loss: 1.4588 - 512ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.4055 - 471ms/epoch - 9ms/step\n"
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@@ -1338,7 +1340,7 @@
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-      "50/50 - 0s - loss: 0.8688 - 466ms/epoch - 9ms/step\n"
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@@ -1352,7 +1354,7 @@
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-      "50/50 - 0s - loss: 0.4871 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3954 - 475ms/epoch - 9ms/step\n"
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@@ -1366,7 +1368,7 @@
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-      "50/50 - 0s - loss: 0.4162 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3923 - 468ms/epoch - 9ms/step\n"
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     },
     {
@@ -1380,7 +1382,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4068 - 451ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3914 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1394,7 +1396,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4032 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3887 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1408,7 +1410,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4009 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1422,7 +1424,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3978 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3882 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1436,7 +1438,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3964 - 451ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3868 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1450,7 +1452,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3936 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3862 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1464,7 +1466,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3933 - 450ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3862 - 484ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1478,7 +1480,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3938 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3854 - 464ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1492,7 +1494,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3924 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3847 - 476ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1506,7 +1508,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3924 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3839 - 473ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1520,7 +1522,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3919 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3843 - 474ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1534,7 +1536,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3907 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3837 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1548,7 +1550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3906 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3829 - 461ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1562,7 +1564,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3907 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3819 - 494ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1576,7 +1578,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3888 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3812 - 480ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1590,7 +1592,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3898 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3816 - 491ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1604,7 +1606,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3889 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3818 - 459ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1618,7 +1620,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3883 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3812 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1632,7 +1634,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3885 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3811 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1646,7 +1648,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3886 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3809 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1660,7 +1662,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3885 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3798 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1674,7 +1676,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3872 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3809 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1688,7 +1690,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3885 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3802 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1702,7 +1704,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3798 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1716,7 +1718,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3869 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3789 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1730,7 +1732,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3868 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3781 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1744,7 +1746,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3790 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1758,7 +1760,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3872 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3775 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1772,7 +1774,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3856 - 481ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3785 - 459ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1786,7 +1788,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3853 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3777 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1800,7 +1802,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3859 - 483ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3782 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1814,7 +1816,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3862 - 482ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3780 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1828,7 +1830,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3850 - 508ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3764 - 479ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1842,7 +1844,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3841 - 494ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3773 - 498ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1856,7 +1858,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3848 - 473ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3749 - 505ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1870,7 +1872,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3850 - 479ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3769 - 489ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1884,7 +1886,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3841 - 473ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3766 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1898,7 +1900,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3847 - 479ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3752 - 459ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1912,7 +1914,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3810 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3758 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1926,7 +1928,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3843 - 483ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3734 - 477ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1940,7 +1942,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3830 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3753 - 475ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1954,7 +1956,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3827 - 485ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3756 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1968,7 +1970,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3820 - 479ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3748 - 487ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1978,6 +1980,62 @@
       "Epoch 50/100\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3741 - 478ms/epoch - 10ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 51/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3744 - 478ms/epoch - 10ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 52/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3756 - 458ms/epoch - 9ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 53/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3725 - 464ms/epoch - 9ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 54/100\n"
+     ]
+    },
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -2066,7 +2124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0c12b4c",
+   "id": "fcbca2f7",
    "metadata": {
     "editable": true
    },
@@ -2086,7 +2144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d81020f6",
+   "id": "1c5b70a2",
    "metadata": {
     "editable": true
    },
@@ -2102,7 +2160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d7453c9",
+   "id": "29c129fe",
    "metadata": {
     "editable": true
    },
@@ -2124,7 +2182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc4a082e",
+   "id": "c2bc1aa3",
    "metadata": {
     "editable": true
    },
@@ -2142,7 +2200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1106ad9",
+   "id": "01ef7e17",
    "metadata": {
     "editable": true
    },
@@ -2188,7 +2246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b139ef7b",
+   "id": "8dd26bf6",
    "metadata": {
     "editable": true
    },
@@ -2209,7 +2267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70c95078",
+   "id": "3b42d5ef",
    "metadata": {
     "editable": true
    },
@@ -2270,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ca24031",
+   "id": "2f8de715",
    "metadata": {
     "editable": true
    },
@@ -2295,7 +2353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93648979",
+   "id": "9998cb2c",
    "metadata": {
     "editable": true
    },
@@ -2314,7 +2372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8806dcd",
+   "id": "45dfbeff",
    "metadata": {
     "editable": true
    },
@@ -2338,7 +2396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0edad40",
+   "id": "141b8f50",
    "metadata": {
     "editable": true
    },
@@ -2420,7 +2478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58fd18f9",
+   "id": "fbadc1e0",
    "metadata": {
     "editable": true
    },
@@ -2436,7 +2494,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "a604caa5",
+   "id": "9365f92f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2475,7 +2533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "545336c9",
+   "id": "55c2aa23",
    "metadata": {
     "editable": true
    },
@@ -2519,7 +2577,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "3ef3ed13",
+   "id": "b57a3099",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2602,7 +2660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a8b29b8",
+   "id": "0b519f6c",
    "metadata": {
     "editable": true
    },
@@ -2613,7 +2671,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d840beea",
+   "id": "82db8594",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2715,7 +2773,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e5b339f",
+   "id": "4711b0d4",
    "metadata": {
     "editable": true
    },
@@ -2737,7 +2795,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "370e799d",
+   "id": "22123a0f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2834,7 +2892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8f62fc4",
+   "id": "50cd4d20",
    "metadata": {
     "editable": true
    },
@@ -2860,7 +2918,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4ea36ea9",
+   "id": "5c3d5a53",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.py b/doc/LectureNotes/_build/jupyter_execute/week45.py
index 71cf54391..208275bf0 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.py
@@ -20,6 +20,8 @@
 # 
 #   * [Video of lab session from week 44](https://youtu.be/EajWMW__k0I)
 # 
+#   * [Video of lab session from week 45](https://youtu.be/tgkj0KAEtZo)
+# 
 #   * [See also whiteboard notes from lab session week 44](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/Exercisesweek44.pdf)
 # 
 #   
diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf
index 58b91e8ef..1f7f457e3 100644
Binary files a/doc/LectureNotes/gaussian.pdf and b/doc/LectureNotes/gaussian.pdf differ
diff --git a/doc/LectureNotes/week45.ipynb b/doc/LectureNotes/week45.ipynb
index c46d08607..f3cbde629 100644
--- a/doc/LectureNotes/week45.ipynb
+++ b/doc/LectureNotes/week45.ipynb
@@ -2,8 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "31a10e2b",
-   "metadata": {},
+   "id": "7e9869f2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "\n",
@@ -12,8 +14,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "019daa83",
-   "metadata": {},
+   "id": "fca263d7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "# Week 45,  Recurrent Neural Networks\n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
@@ -23,8 +27,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ecf1c2b",
-   "metadata": {},
+   "id": "8c3d9fea",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Plan for week 45\n",
     "\n",
@@ -36,6 +42,8 @@
     "\n",
     "  * [Video of lab session from week 44](https://youtu.be/EajWMW__k0I)\n",
     "\n",
+    "  * [Video of lab session from week 45](https://youtu.be/tgkj0KAEtZo)\n",
+    "\n",
     "  * [See also whiteboard notes from lab session week 44](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/Exercisesweek44.pdf)\n",
     "\n",
     "  \n",
@@ -61,16 +69,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d80a4db8",
-   "metadata": {},
+   "id": "d0107e8f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Material for the lab sessions, additional ways to present classification results and other practicalities"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1ded5c92",
-   "metadata": {},
+   "id": "44ed2bb7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Searching for Optimal Regularization Parameters $\\lambda$\n",
     "\n",
@@ -86,8 +98,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6dea0513",
-   "metadata": {},
+   "id": "7d202a1c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -140,8 +155,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "440bf579",
-   "metadata": {},
+   "id": "0fcc974f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here we have performed a rather data greedy calculation as function of the regularization parameter $\\lambda$. There is no resampling here. The latter can easily be added by employing the function **RidgeCV** instead of just calling the **Ridge** function. For **RidgeCV** we need to pass the array of $\\lambda$ values.\n",
     "By inspecting the figure we can in turn determine which is the optimal regularization parameter.\n",
@@ -150,8 +167,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "683b1da2",
-   "metadata": {},
+   "id": "693475c8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Grid Search\n",
     "\n",
@@ -163,8 +182,11 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "38be79e0",
-   "metadata": {},
+   "id": "cdd8cf73",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -213,8 +235,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "081fbe5c",
-   "metadata": {},
+   "id": "51ef3976",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "By default the grid search function includes cross validation with\n",
     "five folds. The [Scikit-Learn\n",
@@ -226,8 +250,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9798c07",
-   "metadata": {},
+   "id": "033bda9d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Randomized Grid Search\n",
     "\n",
@@ -244,8 +270,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0a7e4e2e",
-   "metadata": {},
+   "id": "0a320c52",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -295,8 +324,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dcd07cb8",
-   "metadata": {},
+   "id": "bcd8ea70",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Wisconsin Cancer Data\n",
     "\n",
@@ -308,8 +339,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "65061d95",
-   "metadata": {},
+   "id": "6fe70002",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -332,8 +366,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50492453",
-   "metadata": {},
+   "id": "c4440f93",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Using the correlation matrix\n",
     "\n",
@@ -343,35 +379,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "08a2ab17",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
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-     "metadata": {
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-     "output_type": "display_data"
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-    {
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\n",
-      "text/plain": [
-       "
    "
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 5,
+   "id": "96a74e80",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -411,8 +425,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5408b7dd",
-   "metadata": {},
+   "id": "98525677",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Discussing the correlation data\n",
     "\n",
@@ -434,8 +450,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "76c3d259",
-   "metadata": {},
+   "id": "d00d3339",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
@@ -443,8 +462,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83903231",
-   "metadata": {},
+   "id": "39d74647",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and then"
    ]
@@ -452,8 +473,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "58ccd6c3",
-   "metadata": {},
+   "id": "382459a0",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "correlation_matrix = cancerpd.corr().round(1)"
@@ -461,8 +485,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adda54cb",
-   "metadata": {},
+   "id": "06da1caf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Diagonalizing this matrix we can in turn say something about which\n",
     "features are of relevance and which are not. This leads  us to\n",
@@ -472,8 +498,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8799c034",
-   "metadata": {},
+   "id": "2d846128",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other ways of presenting a classification problem\n",
     "\n",
@@ -503,8 +531,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfbe0571",
-   "metadata": {},
+   "id": "23991e3b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Combinations of classification results\n",
     "\n",
@@ -515,8 +545,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65f7c63d",
-   "metadata": {},
+   "id": "4581b004",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {TPR} ={\\frac {\\mathrm {TP} }{\\mathrm {P} }}={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FN} }}=1-\\mathrm {FNR} }\n",
@@ -525,8 +557,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e674d10",
-   "metadata": {},
+   "id": "855fc1d5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The $TPR$ defines how many correct positive results occur among all positive samples available during the test\n",
     "\n",
@@ -535,8 +569,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65577837",
-   "metadata": {},
+   "id": "7b676376",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FNR} ={\\frac {\\mathrm {FN} }{\\mathrm {P} }}={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TP} }} }\n",
@@ -545,16 +581,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5e8802d",
-   "metadata": {},
+   "id": "64e4f58f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "07c61591",
-   "metadata": {},
+   "id": "6bef46ee",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {TNR} ={\\frac {\\mathrm {TN} }{\\mathrm {N} }}={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FP} }}=1-\\mathrm {FPR} }\n",
@@ -563,16 +603,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3f61bb3",
-   "metadata": {},
+   "id": "ed7cfd1e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "with the fall-out false positive rate"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6544105e",
-   "metadata": {},
+   "id": "307589a6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FPR} ={\\frac {\\mathrm {FP} }{\\mathrm {N} }}={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TN} }}=1-\\mathrm {TNR} }\n",
@@ -581,8 +625,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b571e85",
-   "metadata": {},
+   "id": "24f17f38",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The $FPR$ defines how many incorrect positive results occur among\n",
     "all negative samples available during the test."
@@ -590,8 +636,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17d6872a",
-   "metadata": {},
+   "id": "e825c026",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Positive and negative prediction values\n",
     "\n",
@@ -607,8 +655,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "122f7e6c",
-   "metadata": {},
+   "id": "6dcb34a4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {PPV} ={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FP} }}=1-\\mathrm {FDR} }\n",
@@ -617,16 +667,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f44a5e78",
-   "metadata": {},
+   "id": "a975cae8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**Negative predictive value $NPV$.**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "86fa9670",
-   "metadata": {},
+   "id": "d4971baf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {NPV} ={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FN} }}=1-\\mathrm {FOR} }\n",
@@ -635,8 +689,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a30e358d",
-   "metadata": {},
+   "id": "a4bd001d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other quantities\n",
     "\n",
@@ -645,8 +701,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f0de6de",
-   "metadata": {},
+   "id": "a8b1f114",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FDR} ={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TP} }}=1-\\mathrm {PPV} }\n",
@@ -655,16 +713,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b4cc93f",
-   "metadata": {},
+   "id": "959cdbff",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**False omission rate $FOR$.**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4866ec0a",
-   "metadata": {},
+   "id": "59ee7f47",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FOR} ={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TN} }}=1-\\mathrm {NPV} }\n",
@@ -673,8 +735,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b1c8c45",
-   "metadata": {},
+   "id": "5cdba722",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## $F_1$ score\n",
     "\n",
@@ -699,8 +763,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e651a1f",
-   "metadata": {},
+   "id": "35390ed4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {F} _{1}=2\\times {\\frac {\\mathrm {PPV} \\times \\mathrm {TPR} }{\\mathrm {PPV} +\\mathrm {TPR} }}={\\frac {2\\mathrm {TP} }{2\\mathrm {TP} +\\mathrm {FP} +\\mathrm {FN} }}}\n",
@@ -709,8 +775,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d83efe41",
-   "metadata": {},
+   "id": "b1651ea3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## ROC curve\n",
     "\n",
@@ -732,8 +800,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d088215b",
-   "metadata": {},
+   "id": "1bfac852",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Cumulative gain curve\n",
     "\n",
@@ -743,159 +813,28 @@
     "against Predictive Positive Rate, which represents \n",
     "the fraction of positively predicted examples.\n",
     "\n",
-    "The examples below show the confusion matrix (or error matrix), the ROC curve and the cumulative gain for the Wisconsin cancer data."
+    "The examples below show the confusion matrix, the ROC curve and the cumulative gain for the Wisconsin cancer data."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c0bcd1f",
-   "metadata": {},
+   "id": "f655bb24",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other measures in classification studies: Cancer Data  again"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "2a6f80df",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(426, 30)\n",
-      "(143, 30)\n",
-      "[1.         0.86666667 1.         0.92857143 1.         0.85714286\n",
-      " 1.         0.92857143 0.92857143 1.        ]\n",
-      "Test set accuracy with Logistic Regression: 0.94\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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\n",
-      "text/plain": [
-       "
    "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "
    "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 8,
+   "id": "31f4ee21",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -933,16 +872,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58e5b521",
-   "metadata": {},
+   "id": "098bfaec",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Material for Lecture Thursday November 9"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4ebe5a91",
-   "metadata": {},
+   "id": "55cc0e20",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Recurrent neural networks (RNNs): Overarching view\n",
     "\n",
@@ -966,8 +909,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "182f425e",
-   "metadata": {},
+   "id": "8860da9c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A simple example"
    ]
@@ -975,8 +920,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "8b3ca785",
-   "metadata": {},
+   "id": "7252c0b8",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Start importing packages\n",
@@ -1045,8 +993,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0c12b4c",
-   "metadata": {},
+   "id": "fcbca2f7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### RNNs\n",
     "\n",
@@ -1063,8 +1013,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d81020f6",
-   "metadata": {},
+   "id": "1c5b70a2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Basic layout\n",
     "\n",
@@ -1077,8 +1029,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d7453c9",
-   "metadata": {},
+   "id": "29c129fe",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### We need to specify the initial activity state of all the hidden and output units\n",
     "\n",
@@ -1097,8 +1051,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc4a082e",
-   "metadata": {},
+   "id": "c2bc1aa3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### We can specify inputs in several ways\n",
     "\n",
@@ -1113,8 +1069,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1106ad9",
-   "metadata": {},
+   "id": "01ef7e17",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### We can specify targets in several ways\n",
     "\n",
@@ -1157,8 +1115,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b139ef7b",
-   "metadata": {},
+   "id": "8dd26bf6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Backpropagation through time\n",
     "\n",
@@ -1176,8 +1136,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70c95078",
-   "metadata": {},
+   "id": "3b42d5ef",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### The backward pass is linear\n",
     "\n",
@@ -1235,8 +1197,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ca24031",
-   "metadata": {},
+   "id": "2f8de715",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The problem of exploding or vanishing gradients\n",
     "* What happens to the magnitude of the gradients as we backpropagate through many layers?\n",
@@ -1258,8 +1222,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93648979",
-   "metadata": {},
+   "id": "9998cb2c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Four effective ways to learn an RNN\n",
     "1. Long Short Term Memory Make the RNN out of little modules that are designed to remember values for a long time.\n",
@@ -1275,8 +1241,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8806dcd",
-   "metadata": {},
+   "id": "45dfbeff",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Long Short Term Memory (LSTM)\n",
     "\n",
@@ -1297,8 +1265,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0edad40",
-   "metadata": {},
+   "id": "141b8f50",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Implementing a memory cell in a neural network\n",
     "\n",
@@ -1377,8 +1347,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58fd18f9",
-   "metadata": {},
+   "id": "fbadc1e0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## An extrapolation example\n",
     "\n",
@@ -1391,8 +1363,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "a604caa5",
-   "metadata": {},
+   "id": "9365f92f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -1427,8 +1402,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "545336c9",
-   "metadata": {},
+   "id": "55c2aa23",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Formatting the Data\n",
     "\n",
@@ -1469,8 +1446,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "3ef3ed13",
-   "metadata": {},
+   "id": "b57a3099",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# FORMAT_DATA\n",
@@ -1549,8 +1529,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a8b29b8",
-   "metadata": {},
+   "id": "0b519f6c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Predicting New Points With A Trained Recurrent Neural Network"
    ]
@@ -1558,8 +1540,11 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d840beea",
-   "metadata": {},
+   "id": "82db8594",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def test_rnn (x1, y_test, plot_min, plot_max):\n",
@@ -1657,8 +1642,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e5b339f",
-   "metadata": {},
+   "id": "4711b0d4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other Things to Try\n",
     "\n",
@@ -1677,8 +1664,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "370e799d",
-   "metadata": {},
+   "id": "22123a0f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def rnn_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
@@ -1771,8 +1761,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8f62fc4",
-   "metadata": {},
+   "id": "50cd4d20",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other Types of Recurrent Neural Networks\n",
     "\n",
@@ -1795,8 +1787,11 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4ea36ea9",
-   "metadata": {},
+   "id": "5c3d5a53",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def lstm_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
@@ -1992,25 +1987,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }