diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot
index 7174d42eb..164b89f13 100644
--- a/doc/LectureNotes/DataFiles/cancer.dot
+++ b/doc/LectureNotes/DataFiles/cancer.dot
@@ -10,13 +10,13 @@ edge [fontname="helvetica"] ;
 2 -> 3 ;
 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
 3 -> 4 ;
-5 [label="mean radius <= 12.265\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
+5 [label="worst area <= 566.55\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
 3 -> 5 ;
 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
 5 -> 6 ;
 7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ;
 5 -> 7 ;
-8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ;
+8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ;
 2 -> 8 ;
 9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139"] ;
 8 -> 9 ;
@@ -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 area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
+15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
 14 -> 15 ;
 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
 15 -> 16 ;
-17 [label="worst texture <= 18.445\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
+17 [label="worst concave points <= 0.104\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 ;
@@ -48,10 +48,10 @@ edge [fontname="helvetica"] ;
 21 -> 22 ;
 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ;
 21 -> 23 ;
-24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
+24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
 20 -> 24 ;
-25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
+25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
 24 -> 25 ;
-26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
+26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 24 -> 26 ;
 }
\ No newline at end of file
diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png
index 4de674f5f..176bd8237 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 3ed3101d9..b4115ce1e 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/chapter10.doctree b/doc/LectureNotes/_build/.doctrees/chapter10.doctree
index efe414fa7..efdb9fe44 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/chapter11.doctree b/doc/LectureNotes/_build/.doctrees/chapter11.doctree
index af6872c92..f6d1892e6 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/chapter2.doctree b/doc/LectureNotes/_build/.doctrees/chapter2.doctree
index 310737fda..6c6f2bae0 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/chapter3.doctree b/doc/LectureNotes/_build/.doctrees/chapter3.doctree
index 8b32ed0c3..530b816a7 100644
Binary files a/doc/LectureNotes/_build/.doctrees/chapter3.doctree and b/doc/LectureNotes/_build/.doctrees/chapter3.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/chapter6.doctree b/doc/LectureNotes/_build/.doctrees/chapter6.doctree
index 93107583d..11bded661 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/chapter8.doctree b/doc/LectureNotes/_build/.doctrees/chapter8.doctree
index b2bc92020..8cd3b412d 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/exercisesweek41.doctree b/doc/LectureNotes/_build/.doctrees/exercisesweek41.doctree
index 29fdc772c..47eaaf1bf 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/linalg.doctree b/doc/LectureNotes/_build/.doctrees/linalg.doctree
index 9b3f5bc13..5b12d5cd5 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/statistics.doctree b/doc/LectureNotes/_build/.doctrees/statistics.doctree
index f849f3f22..c37c4c7de 100644
Binary files a/doc/LectureNotes/_build/.doctrees/statistics.doctree and b/doc/LectureNotes/_build/.doctrees/statistics.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/week34.doctree b/doc/LectureNotes/_build/.doctrees/week34.doctree
index bd25631c0..47127ca6f 100644
Binary files a/doc/LectureNotes/_build/.doctrees/week34.doctree and b/doc/LectureNotes/_build/.doctrees/week34.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/week35.doctree b/doc/LectureNotes/_build/.doctrees/week35.doctree
index 91b645df8..03cb3fad2 100644
Binary files a/doc/LectureNotes/_build/.doctrees/week35.doctree and b/doc/LectureNotes/_build/.doctrees/week35.doctree differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index d4be4ab07..62c5a8488 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -515,7 +515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -583,18 +583,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [2.07549007]\n",
+      " [2.06336262]\n",
       "Coefficient beta : \n",
-      " [[5.14029264]]\n",
-      "Mean squared error: 0.23\n",
-      "Variance score: 0.89\n",
+      " [[4.80453713]]\n",
+      "Mean squared error: 0.19\n",
+      "Variance score: 0.90\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.38\n"
+      "Mean absolute error: 0.35\n"
      ]
     },
     {
      "data": {
-      "image/png": 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e/SedJL3wgidDcTuNJDCBzezZs9W1a1eNHj3a76EAABBt3bunfu+uXVKIclh9r2MjWQnAs2fP1vjx49W6dWBiLQAAomnwYKm0NHnAsnNnqIIaKSCBzYIFC/T555/rZz/7md9DAQAg+goKpFmzEv/cMKQnnrDuC5lALI+MHDlSAcphBgAg+l55RXKae8vKpNtukyoqcj4kNwQisAEAwDOxmHUKaP16K7dk8OBQrkS4yml7acoU6ZRTQv/7IbABAERXVZU0aZK0du3310pLrW2YkK5IZKW6Who+PP56prsmAQwaA5FjAwCA66qqpMrKpkGNJK1bZ11PUl4kcgwjPqg5/fTMg5qqKqm83HrNceOsx/Jy33+vgSrQl4m6ujqVlJSotraWOjYAkK+arxwMHCjtt198UGMzDGvlpqbG9xUGz9XWSp06xV/PZvq3g8bmr2FvcT3+eNIVMa/mbwIbAEC4OW037bmntHFj8ucuXCgNG+bZ0HyX6Kh2NlN/LGatzGQZNHo1f7MVBQAIr0TbTakENZK1whNVTkHNt99mF9RI1spYoqBGsl5/zRrrPh8Q2AAAwikWs1Zqspmo06nAGxbDhjkHNaYpFRVl//qpBoM+BY2cigIAhFOylYOW2Nslgwe7Oya/OQU0//yndNRR7r1HqsGgT0EjKzYAgHDKdEXAnvxvuy06icN33JF4lcbNoEZK3o7BMKwifz4FjQQ2AIBwSnVFoEuXpt+XlqZ0aic0DEO69NL466Wl3hy9btyOoXlwE4CgkcAGABBOqa4crF1rnX6aO9d6rKmJRlDz6actN6j0sl5PRYUVHPbs2fR6AIJGjnsDAMLLPhUlNU0iTqOeSiil2nHb63o9WVQe5rg3AADNBXjlwBO7dqUe1EjeH70uKLBOYY0daz0GIGeJU1EAgHCrqJDGjAlczyLXpRPQNBflej3NENgAAMLPXjmIKqeg5uOPrfwhp6aWzUWxXk8CbEUBABBUEycmPsa9//6BP3rtBwIbAACCyDCku+5qeu1Pf2qaJB3wo9d+ILABACBI3ngj8SrN5Mnx1/MtgToJcmwAAAgKp4CmZ8/krSPyJYE6BQQ2AAD47dtvpQ4d4q+nU2ou6gnUKSKwAQDAT4kSf8NdP9c35NgAAOAXp6Bm0yaCmiwQ2AAAkGvHHZc4QXiPPXI/ngghsAEAIJcMwzr51Ngzz7BK4xICGwAAcuH//b/EqzSnnJL78UQUycMAAGQq1e7WTgHNqadKTz/t/RjzDIENAARJqhMl/FdVJU2a1LTGTGmpVQnYLoq3Zo20997xz2XbyTNsRQFAUFRVSeXlVlPDceOsx/Jy6zqCpapKqqyML5y3bp11varKWqUhqMk5wzTD/Ruuq6tTSUmJamtrVVxc7PdwACAz9kTZ/D/J9hZGHpbGD6xYzAo4E1UDNgzn4GXbNqmw0NOhhYlX8zcrNgDgt1jM2tJwmgzta5MnW/fBf4sXt9ziINHnSFCTEwQ2AOC3VCbKNWus++C/9etTv3fJEraecozkYQDwW6oTZToTqh/yJfG5e/fU7lu4UDr2WG/Hgjis2ACA31KdKFO9zw/5lPg8eLB1+ilRjydJKiuz7kPOEdgAgN+STZSGEeyJMpUTQlFSUCBdcIHzFpNhWF+33RbN1aoQILABAL/EYlJ1tfToo9LPf25NlM2DG/v7oE6U+Zj4bBjS9OnOPyst5QSbz8ixAQA/OBV369zZety06ftrpaVWUBPUiTKdxOdhw3I2LE9s2yYVFcVff/VV6csvo51XFCIENgCQa4lq1mzebD3OmCH17h2OiTLVhOannw53YJNom5ATT4FDYAMAuZRs68YwpPvvl2pqgh3Q2FJNaL7tNitIC+rKU0ucgpoPP5QOPDD3Y0FS5NgAQC5FrWZNKieEbGHLtbETgZszTYKaACOwAYBcikrNGltBgdX0MZUtmcYBm504PW+e9Ri0gMcpoLnkEraeQoDABgByKQo1a5qrqLBWY1Kxfn2wa97cfHPiVZrbb8/8dYMeyEUIgQ0A5FLYa9YkMmZMavd9/HFwa94YhvTrX8dfz3aVJsiBXAQR2ABALtlbN1L4ata0JJWArbRUuu++4NW8+eKLxKs0bgQ1QQ3kIorABgByraLCKuLWs2fT62Eu7pZKwPbznwcvcdow4j8HeyzZysfihQFAYAMAfqiokFatsholzp1rPdbUhDOosSUL2Hr3Tu11cpU47bRKs2mTewnCUTsBFxLUsQEAvxQUhLtonZOKCivfxqnL94wZqb2G14nTuSq2F7UTcCFBYAMAcJdTwDZ1qvTHP7b8PDsPx8vEaaeg5p57pAsvdP+9ongCLgQIbAAA3nrsseRBjc2rxOnRo6UXXoi/7mVdGjuhet26xJ3AvQ7k8hA5NgAA78Ri0sUXp3bv9One5BgZRnxQ06qV98X2onoCLuAIbAAA3lm8WNq4MbV7U00uTtVrryU+xp2rk0hRPAEXcGxFAQC8k05irJu5JkHqxt1SQjVcR2ADAPBOqsFKly7p55rEYvHBgmlKbdrE37t9u/P1XIniCbiAIrABAHjHTqBtqZ6LJN11V3orGFVVVvG7ZK8r0bgyz5BjAwDwjp1Am2hrSLL6M1VWpv6aidoUNPfKKwQ1eYjABgDgLTuBtrS06fUuXaRHH5Vuuin112qpTUFjO3dKP/xh+mNF6LEVBQDwnlsJtMnaFDS+j5yWvBSIFZt169bppz/9qTp37qz27dvrsMMO01tvveX3sAAAbrITaMeOtR4zORXkZ5uCWEyqrpbmzbMeaV4ZSL6v2Pz73//WoEGDNHz4cL344ovq2rWrPv30U3Xq1MnvoQEAgmbcuNTuc7tNgVOycmmplT9ELZpAMUzT38yqK664Qm+88YYWZ9jdtK6uTiUlJaqtrVVxcbHLowMABMLXX0tduya/z25TUFPjXp0YO1m5+XRpJ0RTaC8jXs3fvm9FPfPMM+rfv7/OOOMMde3aVYcffrjuu+++hPfX19errq6uyRcAIMIMwzmoyUWbgpaSle1rkyezLRUgvgc2n332me6++2717t1bL730kiZMmKBLL71Uf/3rXx3vnzlzpkpKShq+ysrKcjxiABkjRwHpcjom/q9/SU88kZs2BcmSlU1TWrPGug+B4PtWVNu2bdW/f3/9/e9/b7h26aWXatmyZVqyZEnc/fX19aqvr2/4vq6uTmVlZWxFAUFHjgLSkUpLBKfKw263KZg3L7W8nrlzraRopCyyW1Hdu3dXnz59mlw7+OCD9fnnnzveX1hYqOLi4iZfAAIuUUG1deus61VV/owLweQU1JxzTvx2kBunrJJJNQnZ7WRlZMz3wGbQoEH617/+1eTaRx99pF69evk0IgCuIkcBqRo5MnE37v/5n9yPR/q+JUQihiGVlaXf5wqe8T2w+dWvfqWlS5fqhhtu0CeffKK5c+fq3nvv1cSJE/0eGgA3kKOAVBiGNH9+/HW/WyI8/bT03XfOP/MiWRlZ8z2wOeqoo/Tkk09q3rx56tu3r66//nrddttt+slPfuL30AC4wc+Cagi+t95KvErjd1Bjb6Fu2uT88z324Kh3APleoE+STj75ZJ188sl+DwOAF8hRQCKpJAj7JZWeVEVFVpsIBIrvKzYAIs7OUUg0iZGjkH9M0/nvQ11dMIIaKbWeVGvXsoUaQAQ2ALxVUGAd6ZZyU1ANwWYYUiuHqcc0pd12y/14EmELNbQIbAB4r6LCykXIRUE1pC7XBROdVmnuvTc4qzSNsYUaWr4X6MsWvaKAEMlFQTWkJpcFE4OcS5NILCaVl1u1lpzG6UVPqjzj1fwdiORhAHnCLqgGfyVq6mgXTHRzFS2MQY30/RZqZaX1Z2g8XrZQA42tKADIJ7kqmHjHHcE9xi2ltg3HFmoosWIDAM1FecssnYKJma6uBX2VJp1tuIoK60h3VP8+RBCBDQA0FvVmnV6e9qmrk0pK4q/HYs4nofyQyTYcW6ihEpC/aQAQAPnQrNOr0z6G4RzUmGZwghr6luWFgPxtAwCf5cuk50XBRKfXWrAgOFtPNvqW5QUCGwCQ8mfSc7NgomEkThA+/vishukJN7fhcl0DCCkjsAEAyb1JLwwTnhunfZwCmsLC4K3SNObWNlxVlVXjZvhwadw467G8PBpblRFAYAMAkjuTXpgmvIoKadUqaeFCae5c67GmJnlQM3hw4lWabds8Gapr3NiGy4c8rJCj8jAASNlXmk102saeRKNQ9yTox7hTYX9OknPRvZY+J/vvSKItS6oRp8Wr+ZsVGwCQsss9iXri8XvvBbvYXjqy2YbLlzyskCOwAQBbppNeFCc8O1fIMKQf/CD+52ELaBrLdBuOjt+hQIE+AGgsk0qzUZvwnIoU2lavlvbeO/djclsmRffo+B0KBDYA0Fy6k54bE15Q2jhUVUmnn+78M8OQ3nwzGoFNJuzk42R5WOnUAILrSB4GEH5+BwVuJB771cah8e+ua1dpxIjE95Icm13yMZogeRgAnAThiHU2icd+Hh9u/rtrKaiRwpkr5DY6fgceKzYAwitoR6ydVl7Kyqygxmkcfh4fTvS7S8XcudLYse6OJ2z8XiWMAK/mbwIbAOEU1Joi6Ux41dXWakkyCxe621062e8u1+NBXvJq/iZ5GEA4pXPEOpeTcDqJx36dpkr2u0uE5FiEADk2AMIpCkes/Tg+vH17aqtEzaXbIBPwCYENgHCKQk0RN3oXpcMwrEaVmSA5FiHBVhSAcIpCTRH7NFVlpTVep+PDbq2QJAqeEt3bs6c0Z4701VckxyJUWLEBEE7ZHLEOEq+PDxuGc1DzxBPOP7O/nzVLOv546/TTsGHB/z0C/4fABkB4RaWmSKa9i5JpqRt3VH53QDMc9wYQftQUaWr0aOmFF+KvO/3nnt8dfMJxbwBIJJOGhlHV0iqNE353iBi2ogAgCtascQ5qTDOz6sJASLFiAwBhl+4qDRBhrNgACJdYzGpFMG+e9RiL+T0ifzkFNUuWENQgb7FiAyA8nJpMlpZaR5Pz7RQPqzSAI1ZsAISD3Y26eY+jdeus61VV/ozLD05BTWkpQQ0gAhsAYRCLWSs1ThO3fW3y5OhvSyUqtmc3/ARAYAMgBNLp5O2FIOT1sPUEpITABkDw+dnJu6pKKi+3OmKPG2c9lpfnbuvrxRc5xg2kgeRhAMHnVydvO6+neQBh5/V43XqAVRogbbRUABB8sZi1SpKsk3dNTWbtAJzaCkjWeybaAsv2PVtimlIrhwX1TZukPfZw970An9BSAUD+sjt5V1ZaAUXj4CbbTt6JjpD//Oep5/W42ZKAVRogK+TYAAgHL7pRt3SEfNq01F7Dzbwep6Bm8mSCGiANrNgACI+KCmnMGHe6UadyhDwVbuT1sEoDuIbABkC4uNWNOtkR8mTsHBs7Hyeb13FCUANkhK0oAPkpnS2k5sFHtnk9knTllRzjBjzAig2A/JTqFtKMGdJ998UnF992W+ZHvf1YpXE6+eX2aS4gADjuDSA/pXOEXHInKKitlTp1ch6L0/Fut9A8FAHEcW8AsLmx+pDuEfJs83r8yqXxu8ggkGPk2AAIFzdbHHhxhNyJU1Dz1FPeBzU0D0UeYisKQHgkWn2wA4dMgxGv8k/8PvFUXW0FfsksXOhukUEgBWxFAchvyVYfDMNafRgzJrNtKbcndr+DGsnf5qFBQuJ0XmErCkA4JKs707jFgZ8OOig4x7j9ah4aJH53Z0fOEdgACIcwrD4YhvSvf8Vf92vHf/BgK18o0eqRYUhlZdkXGQyqllpmVFYS3EQUgQ2AcAjy6sNbbwVnlaYx++SX5E2RwSAjcTpvEdgACIegrj4YhtS/f/z1oJzLyNXJr6AJy9YlXEfyMIBwSLfuTC44BVmffirtu6/3751OQqybzUPDIgxbl/CE7ys206dPl2EYTb722msvv4cFIIiCsvpgGIm3nnIR1GSSEGuf/Bo71nqMclAjBXvrEp4KxIrNIYccogULFjR8XxD1f3AAMuf36oNTQDNsmFULJheoJJwae+syWcuMqCZO57FABDatW7dmlQZA6ryoO5NMotyenTtzF1R5WcsnaoK4dYmc8H0rSpI+/vhj9ejRQ/vss49+/OMf67PPPvN7SADwvURBjWRtAT3+uFXld94869GrkzYkxKYnKFuXyCnfV2yOOeYY/fWvf9UBBxygDRs26He/+50GDhyolStXqnPnznH319fXq76+vuH7urq6XA4XQD6ZM0c677yW71m7VjrjjKbXvOqcTUJs+vzeukTOBa5X1DfffKP99ttPU6dO1ZQpU+J+Pn36dM2YMSPuOr2iALiqpVWaVJ/r9qoAvZ8QIV71igrEVlRjHTp00A9+8AN9/PHHjj+/8sorVVtb2/C1Zs2aHI8QQKRt355dUCN5VwAuqLV8gAAJXGBTX1+vDz74QN0THMErLCxUcXFxky8AcIVhSIWF8dfnzk3/tbzId8nnSsJAinwPbC6//HItWrRINTU1+sc//qHKykrV1dVp/Pjxfg8NQD5xWgW54w4rQMmm1onb+S4kxAIt8j15eO3atRo7dqw2btyoLl266Nhjj9XSpUvVq1cvv4cGIB8k2tZpnH6YrCZKS7woAOeUEDtwoPT3v1sns0iQRR4LXPJwurxKPgIQYXY7gkSJuE7/WbQL4yX6eXN2AbiaGu8DjKoqq75N46PgXp3MAlySN8nDAOCpqiqpY0fnoKalbtyJtoCc5DLfxQ64mte3sSsRt9RmAYggVmwA5I+qKun0051/Zhip5ag0bz759dfSlClNA4uyMiuo8Xq1JBazCgQmKtqXyqpROs00ARd5NX8T2ADID59+Ku2/f+KfZ7N15FdwkG1dG7aw4COv5m/fk4cBwHOp1KVpfDw73eJ2fvSukrKrREwzTUQUOTYAoi3dYnthakeQ6omr5vcla6YpuV9cEMgRAhsA0WQYmVUQ9uJ4tlcyrURMM01EGIENgOhxmujbto1eO4JMKxHTTBMRRmADIDoSrdKYplRfH812BJlUIs50CwsIAU5FAYiGVCoIS84ngXJ1PNtL6ZzMso+JJ6qknMvigshbHPdOgMAGyHNPPSWddlr89Zb+00btlsSVlO0AkVNR8BjHvQEEQ5CCglRXaZrz63i2X5w+M3sLy6mOTdhXr5DXCGwApC4oBd1MU2rlkCK4caPUuXPuxhEGyT6z5s0083H1CpHCVhSA1CQq6JbrrYtMV2ncEKTVqlQE5TMDHNAEE4B/glLQzSmo+cUvchPUVFVZCbfDh0vjxlmP5eXBbTIZlM8MyDECGwDJ+V3QraVj3Hfd5c17NhbGDtp+f2aATwhsACTnZ0G3XG89xWJWc8l586zH7dvDufJBET7kKQIbAJbmE3rjidqPgm5TpiRepfEqqHHabiotDefKB0X4kKc4FQUg+ckZuydRsoJubrUj8CNBOFGi7ddfp/b8oK185PozAwKCFRsg36WSP5JpT6J01dY6BzWxmLdBTUuJtqkK2spHrj4zIGAIbIB8luzkjGlKEyZYeSaZ9CRKh2FInTo5j8OpZo2bkiXatiTIzTO9/syAAGIrCshnqUzoX39tTYz33ONdQTenVZqnnrLeKxcy3UYKw8oHRfiQZwhsgHyW6oS+caO1LWX/v3y32hH4WWyvsVS3kfbc0/pd2MLSfiDfWkggrxHYAGGWbSXcdPNCJk+2/t+/G/9vPyhBjZR6ou0nn0h//zsrH0CAkWMDhJUblXDtCT1RkNGYW8eaDzww98e4k0k10bZtW2vlY+xY65GgBggcAhsgjNyqhNt4Qk9VNseaDUP66KP460FoWUeiLRAJNMEEwsQuonfmmdLmzc732NsmNTWpryhUVVmnn1Kp2bJwYdN8jVS2w5Yvl444Iv61gvifn7A1ugRCyqv5m8AGCAunInotaR6AJLN9u7Va0Tg5tjGngClZYT/7eU78+E8PQQsQGHT3BvJZoq2nlqS7ZdS2rXWku6WGk6efbgUGsVhq22FOr/PZZ7kPamIx6brrpK5dw9OdG0BGWLEBgi4WsybgdAvIpbtiY3NahSkoaNo7qmdPads2adOm9MeU69WSqirpwgudx2oHXuTQADnHVlQCBDaIvOpqa3UhVZnk2DRnb9k8/bR1Gihbhx9u5e+0tGXlhUT9nxpz4/cFIG1sRQH5Kp0tJbcq4RYUWCsqjz+e+WvYnnhCWrEi+xNc6Uq1/1NQu3MDyAiBDRB06RTRc/Nocjb9k2wLFrTci0qyiv413uZyS7rjD1p3bgAZIbABgi6VInqdO1tBRE2Ne1s72darKSuz/ndLwUXj1RL7KPu8edZjtsFOuuMPWnduABkhsAGCLllVXMOQ7r1XOv54d3NEMp3oG2+HffVVas95+un4Kspdu1onmTINcNIZf1C7cwNIG4ENEAZ+VMVNp91CojGlGlzcdlv8ys7mzdK0aVK3bpnl4aQz/iB35waQFk5FAWGS6wJz9qkiqeUk3ETHuO2j6i01l2zVKvmqjGFkFsAlG3/nztZqF0e9gZzjuHcCBDaAx1qqePzf/y1NnJj8+U7BhWGkV6ivrCyzI9lO4+/cWbr0Uunqq1mpAXxCYJMAgQ3gMTdaIjgFF2VlViXjdOrkZFp0kFYKQOB4NX+3du2VAESPW32eKiqkMWPig4vFi9MLbDI9qVVQkFlABCB0CGyAKMp2haKiQnryyfjr2SzwOgUXdoJvqvVmOJINIIm0TkWtWbPGq3EAcEtVVfzR6XSaPRqG+0FNIo2PsicbE0eyAaQgrcDmoIMO0m9/+1t98803Xo0HQDZS6bidyNq1ibt6e5mKV1FhtV3o3Nn55261iQCQF9IKbObPn6+XX35ZvXv31uzZs70aE4BMtNQbqaX2BbFY00rBTs/zWkWFtGGDNGOGtMceTX/mZa0eAJGTVmAzcOBA/eMf/9CNN96oa6+9Vocffriqq6s9GhqAtCTrjeTU7LGqSmrtkGp30025C2psBQXStdda1YoXLpTmzrUe3WwTASDyMqo8fM455+ijjz7SKaecotGjR+u0007TJ5984vbYAKQj1RND9n2GYR23bs4wpN/8xruu28nYScZjx1qPbD8BSEPGLRVM09TIkSN14YUX6plnnlHfvn112WWXaevWrW6OD0CqUj0x1L17y20GvO66DQAeSiuw+fOf/6zzzz9f/fr1U0lJiUaMGKE33nhDEydO1F133aUVK1aoT58+evPNN70aL4BEkvVGshtmDh+e/LWctq0AIATSqjxcVlamY489tuGrf//+KiwsbHLPDTfcoLlz5+q9995zfbBOqDwMNOJW+wLb3LnWlhAAuCw0LRU2bNigHj16KJajJWwCG6CZlno7pSvTFgYAkIRX83fGOTaJdO3aVa+++qrbLwsgVRUV0qpVVlCSyM6dybetKIgHIIRcD2wMw9DQoUPdflkA6WjVyjmX5t//trakGlf8bR7cUBAPQIi5HtgA8JlhWIFNc6Ypder0/fcVFVbhu549m95HQTwAIUYTTMBv2TasbCzR1lJpqZV7Ywcr9nvW10tz5ljXvvoq+/cHAJ8R2AB+ckr0LS21tonSWTFpqS6N9H2vqMcft75P9J4kCgMIOddPReUap6IQWvbR7Ob/BO0gJdXtoGRBTeP7dt9d2rw58WuwBQUgR0JzKgpACjJtWNnYzTenHtTYr+sU1KTzngAQcIEKbGbOnCnDMDR58mS/hwJ4K5OGlY0ZhvTrX7s7JqoNA4iAwAQ2y5Yt07333qt+/fr5PRTAe+k2rLRt3eq8SrNrV8t1a9KR6tgAIIACEdj85z//0U9+8hPdd9992n333f0eDuC9dBpW2gxDctqHNk3rZ8l6Rbk9NgAIoEAENhMnTtTo0aM1YsQIv4cC5EYqDSsbV/51um/hwqY5Oi0V3UtV2KoNx2JSdbU0b571SH4QkPd8P+798MMP6+2339ayZctSur++vl719fUN39fV1Xk1NMA7dhBSWRnfoLJx5d/WCf6JJjrMaBfdy7RXVJiqDbt1VB5ApPi6YrNmzRpNmjRJDz74oNq1a5fSc2bOnKmSkpKGr7KyMo9HCXgkWeXf00+Pf85++yXv0t24V9TcudKCBcm3qAoKpEcfDU9AYB+Vbx682fV6qqr8GRcA3/lax+app57SaaedpoJG/w8xFovJMAy1atVK9fX1TX4mOa/YlJWVUccG4dW88vBtt0lPPx1/Xzb/VO1AINHrPPbY9z8PulhMKi9PvCJlGFYgV1MTntUnIA95VcfG18Bm69atWr16dZNr5513ng466CD95je/Ud++fZO+BgX6ECmJVlXc+GfqtHVTVmYFUmFZqZGsXBqnBp/NLVxIJWUgwLyav33Nsdltt93igpcOHTqoc+fOKQU1QGR8+KF08MHx1938/x0VFdKYMe71pfJLpkflAeQF35OHgbzn5SpNcwUF4V/FyOSoPIC8EbjAprq62u8hALnjFNSsWyf16JH7sYSFfVR+3Trn4M/OsQnTsXUArglEHRsg7xiGc1BjmvkT1GRag6alej2Nj8qHbYsNgCsIbIBsZDI5OwU0F1zgzdZTUFVVWSebhg+Xxo2zHsvLUz+mneyofJiSoQG4ytdTUW7gVBR8k26BuFzm0gSZffS8+Z/b/v2kE5g0PyofxmRoIE9F8ri3Gwhs4It0J2eCGgs1aAD8H6/mb7aigHTFYtZKjVNQYl+bPNm675FHEufS5FtQI1mrKy21ejBNac0a6z4AyEDgTkUBgVddndrknG6fp3xADRoAHmPFBkhHVZV05pmZPbe+Pr+DGokaNAA8R2ADpMrOq9m8Of3nmqbUtq37YwobuwZNopwjw7DaPFCDBkCGCGyAVLSUV9OSBx9klaYxatAA8BiBDZCKZEmvTkxT+slPvBlPmFGDBoCHSB4GUpFuMqtpUmOlJVFpyAkgcAhsgFSkmsy6YIF0/PHpF+/LR1FoyAkgcNiKgvsy7QEUZMmSXiUr6XXYsO+TjJtvXa1bZ11PtW0AACBtBDZwV7Y9gIKqoECaPj1xN2nDsJJepdSL9yUTxQARADxGYAP3RHmlwjCsRpVOGie9ulVZN6oBIgB4jBwbuCNZmwHDsFYqxowJX4Ko0/bT7NlSYWF80qsblXUT9aGyA0RODgFAQgQ2cEc6KxVhSRjNpHFltpV1oxwgAkAOsBUFd0StB5BTUDNqVPJie9lW1qVJJABkhcAG7ohKD6C+fRN3437++eTPz7aybtQCRADIMQIbuCMKPYAMQ1q5Mv56ui0RsqmsG5UAEQB8QmADd4S5B9AbbyRepcm0z1NFhbRqlbRwoTR3rvVYU5M86TcKASIA+IjABu4JYw8gw5COOy7+ul+NK8McIAJAABimGe7Ww3V1dSopKVFtba2Ki4v9Hg6kcPRIMk2plUNcX1srufH3KNuWCk7PLyuzgpogBogAkCav5m8CG+SfTI5xpyNRHRr7fVNdvQpDgAgAGSKwSYDABmlxCmpuuUWaMsWd14/FrArBiY5sG4a1clNTQ5ACIK95NX9ToA/5wetVGlsUCxUCQIiQPIzoy1VQI1GHBgB8RmCD6Lr9dvePcSdDHRoA8BVbUYimXK7SNGbXoVm3zvm97Bwb6tAAgCdYsUG0bN3qHNTs2pWb2jTp1qGJxaTqamnePOsxFvN+jAAQYQQ2iA7DcK5BY3fFzpVUCxVWVVknqIYPl8aNsx7Ly63rAICMcNwb0eAUuCxaJA0Zkvux2FqqQ+NWrRsACCnq2CRAYJPn/MqlyQa1bgCAOjZAHKegZr/9pE8+afl5flT0bfyeGzZQ6wYAPEJg4zfK5qfvwgul++6Lv57KKk22PZwy4fSeqaDWDQCkjcDGT35MsmGXzdZToryWdeus617ktSR6z1RQ6wYA0kaOjV9IHk3Pp59K++8ffz3Vv75+5LUke89EyLEBkAe8mr857u2HWMxaqXGalO1rkydT08RmGNkFNVJ6PZzckuw9nTjVugEApIzAxg9+TLJh5bT19OWX6W/t+NHDKZPXal7rBgCQFnJs/ECjxOTcPsbtRw+nVF/rT3+SunUjeRwAXMCKjR9olNgyp6Bm+vTsatPYPZwSBUyGIZWVudvDKdX3vOQSaexY62g3QQ0AZIXAxg9+TLJh0K9f4m7c06Zl99rp9nBygx/vCQB5jsDGD0x48QxDevfd+OtuHtpLtYeTm/x4TwDIYxz39pNTHZuyMiuoyZcJ75VXpBEj4q97+dfS78rD5NIAAL2iEgl1YCPl94SXyz5P+fx7BoAAoldUUGU7YRYU5F8/oJ07pTZt4q9v3+58PVtUeAaAvEFgkw0mzPSlukrj1gqLH20UAAC+IXk4U/aE2bzQnj1hVlX5My63xGJSdbU0b5716EYVZKeg5qWX4oOOqiqrFcHw4dK4cdZjeXn6v1MqPANA3iGwyUTUJ0y3AgubYSQ+xj1yZPx7uxUwUuEZAPIOgU0mojxhur0S5RTQHHigc1DodsBIhWcAyDsENpmI6oTpZmBx662JV2k+/ND5OW4HjFR4BoC8Q/JwJqI6YaYTWLR0kivTY9xuB4x2hed165zf2zCsn+dbhWcAiDBWbDIR1ZYI2QYWmzcnXqVJpTZNpgFjokRnKjwDQN4hsMlEVCfMbFaiDEPq3Dn+ejrF9gYPjm890Pw9mgeMyRKdaWkAAHmFwCZTUZwwM12Jcrq/pib9CsJPPy1t25b4vaWmAWNVlXT66ckTnSsqpFWrpIULpblzrceamnB+RgCAFtFSIVN2Abl166Svv5a6dLGCnFyX6ne7VYB9KkpqGpjYgUXjoM3NlgiJCunZOneW7r33+/eOxaRu3aRNm5zvt/NnamrCt3IGAHnAq/mbFZtMNN7++OlPpV/9SrriCivHJJeTqNv1ZqTUV6KcgpqpUzMLalo6jWUrKpLGjPn++9//PnFQI4X7yD0AIGO+BzZ33323+vXrp+LiYhUXF2vAgAF68cUX/R5WYkGpOOzlOFrauhk/PnGC8B/+kNn7JTuNJVk/t4OUWOz7HKdkwnbkHgCQFd+Pe5eWlurGG2/U/vvvL0n6n//5H40ZM0bLly/XIYcc4vPomklW58UwrDovY8Z4u3KTi3E4Nef0qht3uqexFi+2VsdSEbYj9wCArPi+YnPKKado1KhROuCAA3TAAQfo97//vTp27KilS5f6PbR4Qak4nOtxvPtudse4k0n3NFaqgdAee4TvyD0AICu+r9g0FovF9Nhjj+mbb77RgAEDHO+pr69XfX19w/d1dXW5Gl5wKg7nchxerdI0lm4hvVQDoUmTSBwGgDzj+4qNJL377rvq2LGjCgsLNWHCBD355JPq06eP470zZ85USUlJw1dZWVnuBhqUisO5GIe9pdXcf/7jblAjpV8XKNmxdMk6RXX11e6OEwAQeIE47r19+3Z9/vnn2rJli5544gndf//9WrRokWNw47RiU1ZWlpvj3rGYdeoo2cqC10eMvRxHLCa1TrCQ5/Vflaoqa5Wl8TZbWZkV1DSvOZPoWLpk/fnDWksIAPJEpI97t23bVvvvv7/69++vmTNn6tBDD9WsBKdeCgsLG05Q2V85E5SKw16No6rKOaiZMsX7oEZKr5BeomPpZWUENQCQxwKxYtPc8ccfr7KyMs2ZMyfpvb4U6EtnZSEs4+jTR/rgg8Q/f+KJYAYLbhcoBADkhFfzt++BzVVXXaWTTjpJZWVl2rp1qx5++GHdeOON+tvf/qYTTjgh6fN9rzzs94TqxjhaylWxde4sbdhA0AAAcIVX87fvp6I2bNigs88+W+vXr1dJSYn69euXclDjK6c6L37IZhwvvyydeGJq927aZFX7vfbazN4LAIAc8H3FJluerdgEZUXGK6ms0jTHqg0AwCWRTh4OHC96MMViUnW1NG+e9RiLuTPWdG3blllQI1mrNvReAgAEGIFNc170YPIiUMqEYVjNJJvbudOq0psKei8BAAKMwKaxZD2YJKsHUzqrLUFpmum0SvPBB9afq6DA+nOngt5LAIAAI8emsepqazUlmYULU0vYtQvpJerrlIuCfqm2RIjFpG7drO2mRK+Ti+KDAIC8QI5NLrjdg8nvpplOQc1llzmvSBUUSPfe6/ycXBYfBAAgCwQ2jbndg8mvppl/+lPibtw335z4eXY139LSptdLS6nmCwAIBd/r2ARKul2mk/GjaWa23bgrKqQxY6J91B0AEFkENo3ZPZgqK60AoXEwkMl2jNuBUku++CK+b5KUWY+noBQfBAAgTWxFNZeouWIm2zG5apppGO4FNQAAhBinohJxs/Kwl00znbaeNm+Wdt89u9cFAMBDkW2CmS3fmmCmy+0WDdnm0gAA4KPINsHMG27mrTgFNY89ZuUGAQCQx8ixCZOzz058jJugBgAAVmxCwymgOfRQacWKnA+lRVHvig4ACLRoBzZRmGTffFM66qj460HMpXFKki4ttU6GUdwPAJAD0Q1sojDJhilB2G722XxsdrNPKhcDAHIgmjk2QemonalYzDmo2bEjmEGNF13RAQDIQPQCm7BPsoYhtXZYSDNN5+tB4HezTwAA/k/0ApswT7JOqzRLlwZzlaYxv5p9AgDQTECXALIQxkm2Vy/p88/jrwc9oLH50ewTAAAH0VuxCeIkG4tJ1dXSvHnWY+NtMMOID2p+/OPwBDXS980+EyU7G4bVQsKNZp8AALQgeis2ueyonYpEp7MqK61eUc2FKaCxud0VHQCADEVvxSZXHbVTkeh01tq10QlqbG52RQcAIEPRbYLpZUftVMRiUnl5y4nMtnB/BE1FoSgiAMBzNMFMxs5jsSfUMWOsL78m2WSns2wLF3o/llxys9knAABpik5g07ev9MUX33/vd5XhMJ7OAgAg5KKTY9M4qJH8rzIcxNNZAABEXHQCm+b8rjI8fHjLP+cINAAArotuYCP5U2X4979PXM/FxhFoAAA8EZ0cm5bkKo/FKaDp2FHq1Cm+jk2uTmcBAJBH8iOw8TqP5dNPpf33j79ub4dxBBoAgJyIdmCTiyrDibadGtem4Qg0AAA5Ed3AJlkeS7arKKYptXJIUfruO6ldu4yGDAAAshOd5OEePZp+31Ip/6oqqyrw8OHSuHHWY3l56kfDDcM5qDFNghoAAHwUnRWb996T3nkn+QqM3b+peRsDu+5Nsr5GTltPb7whDRyY3fgBAEDWotsrykmy/k12Tk5NTXxQNHWq9Mc/xj8n3L8+AAB84VWvqOhsRaUiWf+mRHVvDCM+qLnsMoIaAAACJjpbUalIt3/Te+9JP/hB/M8JaAAACKT8CmzS6d+UyjFuAAAQKPm1FTV4sJVDkyhosXNsnPo87dpFUAMAQMDlV2BTUCDNmmX97+bBjWFYgYtTDo5pJu//BAAAfJdfgY1kHeV+/HGpZ8+m151WY776ilUaAABCJP8CG8kKblatkhYulE4+2fke05S6dMnpsAAAQHbyK3m4sYIC51ya55+XRo3K/XgAAEDW8jOwWbFCOvzw+OtsOwEAEGr5txVlGPFBzXnnEdQAABAB+bNi8803UseO8dcJaAAAiIz8WLEZPjw+qNl7b4IaAAAiJvorNk71Z7ZtkwoLcz8WAADgqeiu2KxY4RzUmCZBDQAAERXNwGbQoPgE4VWr2HoCACDiorUV9eWX8Y0ux42THnrIn/G4LRaTFi+2uo937271vioo8HtUAAAERnQCm7PPlp55pum1zz6T9tnHn/G4rapKmjSpaS+r0lKr91VFhX/jAgAgQKKzFdU4qOnVy9p2ilJQU1kZ36Bz3TrrelWVP+MCACBgohPYNHbrrX6PwD2xmLVS45QfZF+bPNm6DwCAPBe9wMYwojXRL14cv1LTmGlKa9ZY9wEAkOd8D2xmzpypo446Srvttpu6du2qH/3oR/rXv/6V+QtGbaJfv97d+wAAiDDfA5tFixZp4sSJWrp0qebPn6+dO3dq5MiR+uabb7J74ahM9M1PeWV7HwAAEWaYZrCKu3z99dfq2rWrFi1apCFDhiS9v66uTiUlJaqVVNz4BwsXSsOGZT8gv49Yx2JSebmVKOz0URmGdTqqpoaj3wCA0GiYv2trVVxcnPwJKfJ9xaa52tpaSdIee+zh+PP6+nrV1dU1+WrCMKSyMisAyVZVlRVUDB9u1cMZPtz6PpenkAoKrCPdUnwlZfv7224jqAEAQAELbEzT1JQpU3Tcccepb9++jvfMnDlTJSUlDV9lZWXf/9DNiT5IR6wrKqTHH5d69mx6vbTUuk4dGwAAJAVsK2rixIl6/vnn9frrr6u0tNTxnvr6etXX1zd8X1dXp7KyMmsrqqzMCmqynejt7Z9Ep5H82v7xe1sMAACXeLUVFZjKw5dccomeeeYZvfbaawmDGkkqLCxUoVMTy+eek/7rv9yZ6NM5Yu1GHk+qCgpy+34AAISM74GNaZq65JJL9OSTT6q6ulr7ZFot2M3VC45YAwAQSr4HNhMnTtTcuXP19NNPa7fddtOXX34pSSopKVFRUZE/g+KINQAAoeR7jo3R/KTP/5k9e7bOPffcpM/3ZI+OI9YAAHgqsjk2Acpd/p59xLqy0gpiGo+RI9YAAARWoI57BwpHrAEACB3fV2wCraJCGjOGI9ap4Cg6ACAACGyS4Yh1clVV0qRJTY/Il5Za23msbAEAcoitKGQnSBWaAQB5j8AGmYvFrJUapwRw+9rkydZ9AADkAIENMpdOhWYAAHIgf3JsSG51HxWaAQABkx+BDcmt3qBCMwAgYKK/FUVyq3cGD7YCxATVo2UYUlmZdR8AADkQ7cCG5FZv2RWapfjghgrNAAAfRDuwIbnVe1RoBgAESLRzbEhuzQ0qNAMAAiLagQ3JrblDhWYAQABEeyuK5FYAAPJKtAMbklsBAMgr0Q5sJJJbAQDII9HOsbGR3AoAQF7Ij8BGIrkVAIA8EP2tKAAAkDcIbAAAQGQQ2AAAgMggsAEAAJFBYAMAACKDwAYAAEQGgQ0AAIgMAhsAABAZoS/QZ5qmJKmurs7nkQAAgFTZ87Y9j7sl9IHNpk2bJEllZWU+jwQAAKRr06ZNKikpce31Qh/Y7LHHHpKkzz//3NVfDNJXV1ensrIyrVmzRsXFxX4PJ+/xeQQHn0Vw8FkER21trfbee++GedwtoQ9sWrWy0oRKSkr4SxoQxcXFfBYBwucRHHwWwcFnERz2PO7a67n6agAAAD4isAEAAJER+sCmsLBQ06ZNU2Fhod9DyXt8FsHC5xEcfBbBwWcRHF59Fobp9jkrAAAAn4R+xQYAAMBGYAMAACKDwAYAAEQGgQ0AAIiMUAQ2d911l/bZZx+1a9dORx55pBYvXtzi/YsWLdKRRx6pdu3aad9999Wf//znHI00+tL5LKqqqnTCCSeoS5cuKi4u1oABA/TSSy/lcLTRlu6/C9sbb7yh1q1b67DDDvN2gHkm3c+jvr5eV199tXr16qXCwkLtt99+euCBB3I02mhL97N46KGHdOihh6p9+/bq3r27zjvvvIZ2Pcjca6+9plNOOUU9evSQYRh66qmnkj7HlfnbDLiHH37YbNOmjXnfffeZ77//vjlp0iSzQ4cO5urVqx3v/+yzz8z27dubkyZNMt9//33zvvvuM9u0aWM+/vjjOR559KT7WUyaNMn8wx/+YP7zn/80P/roI/PKK68027RpY7799ts5Hnn0pPtZ2LZs2WLuu+++5siRI81DDz00N4PNA5l8Hqeeeqp5zDHHmPPnzzdramrMf/zjH+Ybb7yRw1FHU7qfxeLFi81WrVqZs2bNMj/77DNz8eLF5iGHHGL+6Ec/yvHIo+eFF14wr776avOJJ54wJZlPPvlki/e7NX8HPrA5+uijzQkTJjS5dtBBB5lXXHGF4/1Tp041DzrooCbXLrroIvPYY4/1bIz5It3PwkmfPn3MGTNmuD20vJPpZ3HWWWeZ11xzjTlt2jQCGxel+3m8+OKLZklJiblp06ZcDC+vpPtZ/PGPfzT33XffJtduv/12s7S01LMx5qNUAhu35u9Ab0Vt375db731lkaOHNnk+siRI/X3v//d8TlLliyJu//EE0/Um2++qR07dng21qjL5LNobteuXdq6davrDc/yTaafxezZs/Xpp59q2rRpXg8xr2TyeTzzzDPq37+/brrpJvXs2VMHHHCALr/8cn333Xe5GHJkZfJZDBw4UGvXrtULL7wg0zS1YcMGPf744xo9enQuhoxG3Jq/A90Ec+PGjYrFYurWrVuT6926ddOXX37p+Jwvv/zS8f6dO3dq48aN6t69u2fjjbJMPovmbrnlFn3zzTc688wzvRhi3sjks/j44491xRVXaPHixWrdOtD/7EMnk8/js88+0+uvv6527drpySef1MaNG3XxxRdr8+bN5NlkIZPPYuDAgXrooYd01llnadu2bdq5c6dOPfVU3XHHHbkYMhpxa/4O9IqNzTCMJt+bphl3Ldn9TteRvnQ/C9u8efM0ffp0PfLII+ratatXw8srqX4WsVhM48aN04wZM3TAAQfkanh5J51/G7t27ZJhGHrooYd09NFHa9SoUbr11ls1Z84cVm1ckM5n8f777+vSSy/Vtddeq7feekt/+9vfVFNTowkTJuRiqGjGjfk70P/Xbc8991RBQUFcpP3VV1/FRXW2vfbay/H+1q1bq3Pnzp6NNeoy+SxsjzzyiM4//3w99thjGjFihJfDzAvpfhZbt27Vm2++qeXLl+uXv/ylJGtiNU1TrVu31ssvv6wf/vCHORl7FGXyb6N79+7q2bOnSkpKGq4dfPDBMk1Ta9euVe/evT0dc1Rl8lnMnDlTgwYN0q9//WtJUr9+/dShQwcNHjxYv/vd71jlzyG35u9Ar9i0bdtWRx55pObPn9/k+vz58zVw4EDH5wwYMCDu/pdffln9+/dXmzZtPBtr1GXyWUjWSs25556ruXPnsmftknQ/i+LiYr377rtasWJFw9eECRN04IEHasWKFTrmmGNyNfRIyuTfxqBBg/TFF1/oP//5T8O1jz76SK1atVJpaamn442yTD6Lb7/9Vq1aNZ0KCwoKJH2/WoDccG3+TivV2Af20b2//OUv5vvvv29OnjzZ7NChg7lq1SrTNE3ziiuuMM8+++yG++3jYr/61a/M999/3/zLX/7CcW+XpPtZzJ0712zdurV55513muvXr2/42rJli19/hMhI97NojlNR7kr389i6datZWlpqVlZWmitXrjQXLVpk9u7d27zgggv8+iNERrqfxezZs83WrVubd911l/npp5+ar7/+utm/f3/z6KOP9uuPEBlbt241ly9fbi5fvtyUZN56663m8uXLG47eezV/Bz6wMU3TvPPOO81evXqZbdu2NY844ghz0aJFDT8bP368OXTo0Cb3V1dXm4cffrjZtm1bs7y83Lz77rtzPOLoSuezGDp0qCkp7mv8+PG5H3gEpfvvojECG/el+3l88MEH5ogRI8yioiKztLTUnDJlivntt9/meNTRlO5ncfvtt5t9+vQxi4qKzO7du5s/+clPzLVr1+Z41NGzcOHCFucAr+ZvwzRZawMAANEQ6BwbAACAdBDYAACAyCCwAQAAkUFgAwAAIoPABgAARAaBDQAAiAwCGwAAEBkENgAAIDIIbAAAQGQQ2AAAgMggsAEQOPPmzVO7du20bt26hmsXXHCB+vXrp9raWh9HBiDo6BUFIHBM09Rhhx2mwYMH67//+781Y8YM3X///Vq6dKl69uzp9/AABFhrvwcAAM0ZhqHf//73qqysVI8ePTRr1iwtXryYoAZAUqzYAAisI444QitXrtTLL7+soUOH+j0cACFAjg2AQHrppZf04YcfKhaLqVu3bn4PB0BIsGIDIHDefvttDRs2THfeeacefvhhtW/fXo899pjfwwIQAuTYAAiUVatWafTo0briiit09tlnq0+fPjrqqKP01ltv6cgjj/R7eAACjhUbAIGxefNmDRo0SEOGDNE999zTcH3MmDGqr6/X3/72Nx9HByAMCGwAAEBkkDwMAAAig8AGAABEBoENAACIDAIbAAAQGQQ2AAAgMghsAABAZBDYAACAyCCwAQAAkUFgAwAAIoPABgAARAaBDQAAiAwCGwAAEBn/H3kC89IcnZsGAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -822,7 +822,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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5Zw4UERER8Tun1cXjK+riEdDfWkRqof0rYeMjcHgDxLSFtKnOFY39hFe7eERERMQH9q+EJd0BA4wiyM2AjCXQa7lfJSlVpcUCRURE/MHGRyhJTuD4vXF8e+BRgiIiIuIPDm84kZwUM4qc2wOQEhQRERF/ENMWLGUGk1hszu0BSAmKiIiIP0ibClhOJCkWm/Nx2n2+jMprlKCIiIj4g/iuzoLYxN4Q1tB532sFxHfxdWReoVE8IiIi/iK+K/RY5OsoaoRaUExk9OjRWCwWLBYLwcHBJCQk0Lt3b1555RUcDkeVjzNv3jxiYmK8F6iIiIiXKUExmYsvvpi9e/fy+++/s2jRInr06MH48eMZMGAAhYWFvg5PRESkRihBMRm73U5iYiINGzbk7LPP5u9//zsffvghixYtKllK4Mknn6Rt27ZERESQkpLC2LFjOXr0KADLly9nzJgxZGZmlrTGTJs2DYDXX3+d9PR0IiMjSUxMZOTIkezbt89Hn1RERKRiSlAqs38lLOsHC5Kd9/tX+iSMiy66iPbt2/P+++8DYLVamT17Nhs3bmT+/Pl8/vnnTJo0CYAuXbrw1FNPERUVxd69e9m7dy933XUXAPn5+Tz88MP88MMPfPDBB2zfvp3Ro0f75DOJiIhURkWyFTHZlMKtWrXixx9/BGDChAkl21NTU3n44Yf529/+xpw5cwgJCSE6OhqLxUJiYmKpY1x33XUl/920aVNmz55Np06dOHr0KHXq1KmRzyEiIlIVakGpiMmmFDYMA4vFAsCyZcvo3bs3DRs2JDIykmuvvZaDBw9y7NixSo+xbt06Bg8eTOPGjYmMjKR79+4A/PHHH94OX0RExC1KUCpisimFN2/eTGpqKjt27OCSSy4hLS2N9957jzVr1vDss88CUFBQUOHrjx07Rp8+fahTpw6vv/4633//PQsWLACcXT8iIiJmoi6eisS0dXbrnJyk+GhK4c8//5wNGzZwxx13sHr1agoLC3niiSewWp355TvvvFNq/5CQEIqKSidXP//8MwcOHOCxxx4jJSUFgNWrV9fMBxAREXGTWlAq4qMphfPy8sjIyGD37t2sXbuW6dOnM3jwYAYMGMC1115Ls2bNKCws5Omnn2bbtm289tprPPfcc6WO0aRJE44ePcrSpUs5cOAA2dnZNGrUiJCQkJLXLVy4kIcfftirn0VERKS6lKBUxEdTCn/yySc0aNCAJk2acPHFF7Ns2TJmz57Nhx9+iM1mo0OHDjz55JPMnDmTtLQ03njjDWbMmFHqGF26dOGWW25hxIgRxMfHM2vWLOLj45k3bx7//e9/ad26NY899hiPP/64Vz+LiIhIdVkMwzB8HYS7srKyiI6OJjMzk6ioqFLP5ebmsn37dlJTUwkNDfVRhFIT9LcWEfEvlV2/y1ILioiIiJiOEhQRERExHSUoIiIiYjpKUERERMR0lKCIiIiI6QRsguKHg5PETfobi4gEroBLUIKDgwHIzs72cSTibcV/4+K/uYiIBI6Am+reZrMRExPDvn37AAgPDy9ZZE8Cg2EYZGdns2/fPmJiYrDZbL4OSUREPCzgEhSAxMREgJIkRQJTTExMyd9aREQCS0AmKBaLhQYNGlC/fv1KV/gV/xUcHKyWExGRABaQCUoxm82mi5iIiIgfCrgiWREREfF/SlBERETEdJSgiIiIiOkoQRERERHTUYIiIiJS0/avhGX9YEGy837/Sl9HZDpuJyhffPEFAwcOJCkpCYvFwgcffFDyXEFBAffccw9t27YlIiKCpKQkrr32Wvbs2VPqGHl5edx+++3Uq1ePiIgIBg0axK5du077w4iIiJje/pWwpDtkLIac3c77Jd2VpJThdoJy7Ngx2rdvzzPPPFPuuezsbNauXct9993H2rVref/999myZQuDBg0qtd+ECRNYsGABb731Fl999RVHjx5lwIABFBUVVf+TiIiI+IONjwAGGMeveUaR8/HGR3wZlelYjNNYcc1isbBgwQKGDBlS4T7ff/89nTp1YseOHTRq1IjMzEzi4+N57bXXGDFiBAB79uwhJSWFjz/+mL59+57yfbOysoiOjiYzM5OoqKjqhi8iIlLzFiQ7W07KCmsIlwZ2b4I712+v16BkZmZisViIiYkBYM2aNRQUFNCnT5+SfZKSkkhLS2PVqlUuj5GXl0dWVlapm4iIiF+KaQuWMpOIWmzO7VLCqwlKbm4u9957LyNHjizJlDIyMggJCaFu3bql9k1ISCAjI8PlcWbMmEF0dHTJLSUlxZthi4iIeE/aVMByIkmx2JyP0+7zZVSm47UEpaCggCuuuAKHw8GcOXNOub9hGBWuOjx58mQyMzNLbjt37vR0uCIiIjUjviv0Wg6JvZ3dOom9odcKiO/i68hMxStr8RQUFDB8+HC2b9/O559/XqqfKTExkfz8fA4dOlSqFWXfvn106eL6j2O327Hb7d4IVUREpObFd4Uei3wdhal5vAWlODn59ddfWbJkCXFxcaWe79ixI8HBwSxevLhk2969e9m4cWOFCYqIiIjULm63oBw9epTffvut5PH27dtZv349sbGxJCUlMWzYMNauXcv//d//UVRUVFJXEhsbS0hICNHR0Vx//fXceeedxMXFERsby1133UXbtm3p1auX5z6ZiIiI+C23hxkvX76cHj16lNs+atQopk2bRmpqqsvXLVu2jO7duwPO4tm7776bN998k5ycHHr27MmcOXOqXPyqYcYiIiL+x53r92nNg+IrSlBERET8j6nmQRERERFxlxIUERGRk2khP1PwyjBjERERv1S8kF/xWjm5GZCxxDlvSXxXHwfnAftXOtf8ObzBOXNt2lTTfi61oIiIiBQL5IX8/GwVZSUoIiIixQ5vOJGcFDOKnNv9nZ8lX0pQREREigXyQn5+lnwpQRERESkWyAv5+VnypQRFRESkWCAv5OdnyZdG8YiIiJwsUBfyK06+So3iuc+0yZcSFBERkdrCj5IvdfGIiIiI6ShBEREREdNRgiIiIiKmowRFRERETEcJioiIiJiOEhQRERFP0CrIHqVhxiIiIqcr0FdB9gG1oIiIiJwuP1uIzx8oQRERETldfrYQnz9QgiIiInK6/GwhPn+gBEVEROR0+dlCfP5ACYqIiMjpCuRVkH1Eo3hEREQ8wY8W4vMHakERERER01GCIiIiIqajBEVERERMRwmKiIiImI4SFBERETEdJSgiIiJiOkpQRETEv2kV4YCkeVBERMR/aRXhgKUWFBER8V9aRThgKUERERH/pVWEA5bbCcoXX3zBwIEDSUpKwmKx8MEHH5R63jAMpk2bRlJSEmFhYXTv3p2ffvqp1D55eXncfvvt1KtXj4iICAYNGsSuXbtO64OIiEgtpFWEA5bbCcqxY8do3749zzzzjMvnZ82axZNPPskzzzzD999/T2JiIr179+bIkSMl+0yYMIEFCxbw1ltv8dVXX3H06FEGDBhAUVGRy2OKiIi4pFWEA5bFMAyj2i+2WFiwYAFDhgwBnK0nSUlJTJgwgXvuuQdwtpYkJCQwc+ZMbr75ZjIzM4mPj+e1115jxIgRAOzZs4eUlBQ+/vhj+vbte8r3zcrKIjo6mszMTKKioqobvoiIBIL9K501J4c3OFtO0u7TKsIm5c7126M1KNu3bycjI4M+ffqUbLPb7XTr1o1Vq1YBsGbNGgoKCkrtk5SURFpaWsk+ZeXl5ZGVlVXqJiIiApxYRfjSXc57JScBwaMJSkZGBgAJCQmltickJJQ8l5GRQUhICHXr1q1wn7JmzJhBdHR0yS0lJcWTYYuIiIjJeGUUj8ViKfXYMIxy28qqbJ/JkyeTmZlZctu5c6fHYhURERHz8WiCkpiYCFCuJWTfvn0lrSqJiYnk5+dz6NChCvcpy263ExUVVeomIiIigcujCUpqaiqJiYksXry4ZFt+fj4rVqygSxdnn2DHjh0JDg4utc/evXvZuHFjyT4iIiJSu7k91f3Ro0f57bffSh5v376d9evXExsbS6NGjZgwYQLTp0+nefPmNG/enOnTpxMeHs7IkSMBiI6O5vrrr+fOO+8kLi6O2NhY7rrrLtq2bUuvXr0898lERETEb7mdoKxevZoePXqUPJ44cSIAo0aNYt68eUyaNImcnBzGjh3LoUOH6Ny5M5999hmRkZElr/nnP/9JUFAQw4cPJycnh549ezJv3jxsNlu59xMREfGqcsOUp5pvHR9/iNHDTmseFF/RPCgiIuIRZRcbLJ7ozUyLDfpDjFXks3lQRERE/IoZFxvcvxKW9YMFyc77tXeaL8Ya4HYXj4iISMAw22KDZVtLcjPKxwe1YkFEtaCIiEjtZbbFBl226LhQCxZEVIIiIiK1l9kWG3TVolPMLDHWECUoIiJSe8V3dRabJvaGsIbO+14rfLeeT0UtOnGdazZGh8N7x64i1aCIiEjtVrzYoBmkTYWMJc6k5OQRO2c/WXNJU04ORRdeiO266+CWW+AUS9V4i1pQREREzMIELTp54yZgW72aY3+/j/z9B2vsfctSC4qIiIiZ1GSLTpkJ4IytXbC/9AIA9w+7hwejYwipmUjKUYIiIiJSG5Ud0vzHXgomLyEEeO68y7n2/hupY/ddmqAuHhERkdro5CHNBZD7LxshOYWsSWqF5aGHaJ8S49PwlKCIiIjURicNaS56w0Lo9gIOhUYy76rLuPGilj4OTgmKiIhI7VQ8pPlbsC12Lst3/6BbmNLjZ6xW34zcOZkSFBEREU8qu5bO/pW+jsi1tKmQAYUvOFOBOecOY0CPVSSmT/RxYE4qkhUREfEUV2vpZCwx58rDdc6m8KWmBOX+yrfJbdh7ZQfGXnaH7yapK0MtKCIiIp5ixtWRK2CMH0/Qpl85EB7Nk9dN4++33Gua5ASUoIiIiHiO2VZHrsgbb2B58UUcWLhr8N08cHMfwkJsp35dDVKCIiIi4ilmWx3Zlc2bcdx0MwBPd7mCC8eOpHVSlI+DKk8JioiIiKeYYXXkyop0jx3DMexyrNnHWNm4HT9cdztjujapudjcoARFREQEPDP6xtdr6RQX6WYshpzdzvsl3U98lttuw7rpJ/ZF1OWRkVN5/IqOWHy0GOCpaBSPiIiIJ0ff+HJ1ZFdFuhabc/vvw2HePIosVsYPuptpN1xEbISvVto5NbWgiIiI+NHom0pVVKT7w1ocY8cC8MQFV3Pe9cPo3DTOBwFWnRIUERERfxl9cyquinSzrTj+cRRrbi7LUzvyw8ibubXHGb6Jzw1KUERERPxh9E1VlC3SNawwx8C6J5tdUfE8POJe/nnl2dhMMJX9qShBERERMcPoG08oW6S7tCmsM8izBfO3oVOYdl036keF+jrKKlGCIiIi4uvRN55UXKQb+iLGvK0ATOl7Kxde0ZcLmsf7OLiq0ygeERER8O3oG0/buhVj5EgshsHrHfrx+4DLeaxXC19H5RYlKCIiIoEkOxuGDsVy+DBrk1oye+BYPrjyLIJs/tVp4l/RioiISMUMA266CX78kf3hMYy9dDKzrupEUkyYryNzmxIUERERX/LEDLbFnnkG3niDQouV2wbfw/DB59G9ZX3PxVqD1MUjIiLiK56cwfbLLzEmTsQCTO9xPcEX9WB8z+aej7mGqAVFRETMwZMtCf7CUzPY7tmDcfnlWAoLWXjmhXx80XD+dUUHv5jvpCJqQREREd/zZEuCP/HEDLb5+XD55Vj+/JOf6zVmyiXjefWqs4mrY/dsrDVMLSgiIuJ7gbIWjrs8MYPthAmwahVZ9ghuHjqF8YM7kN4k1qNh+oISFBER8b1AWQvHXac7g+3cuTB3Lg6LhQkD7qT1BWdz/fmp3oq2Rnk8QSksLGTq1KmkpqYSFhZG06ZNeeihh3A4HCX7GIbBtGnTSEpKIiwsjO7du/PTTz95OhQREfEXgbIWjrtOZwbb5csxxo0D4PELrmH7uT2YNawdFov/1p2czOM1KDNnzuS5555j/vz5tGnThtWrVzNmzBiio6MZP348ALNmzeLJJ59k3rx5tGjRgkceeYTevXvzyy+/EBkZ6emQRETE7NKmOmtOLDZny4m/roVTHdWZwXb7dhg2DEthIR+e2Y1XLhjBgqvOJjI02Dsx+oDHW1C+/vprBg8eTP/+/WnSpAnDhg2jT58+rF69GnC2njz11FNMmTKFoUOHkpaWxvz588nOzubNN9/0dDgiIuJLVR2ZE0hr4XjbkSMwaBAcPMiPiWcwqd84Zg5rz5kNonwdmUd5PEE5//zzWbp0KVu2bAHghx9+4KuvvuKSSy4BYPv27WRkZNCnT5+S19jtdrp168aqVatcHjMvL4+srKxSNxERMbnikTkZiyFnt/N+SffKk5Qei+DSXc57JSflORxw7bWwcSP769TlpkunclX3Vgzu0NDXkXmcx7t47rnnHjIzM2nVqhU2m42ioiIeffRRrrzySgAyMjIASEhIKPW6hIQEduzY4fKYM2bM4MEHH/R0qCIi4k2uRuZYbM7tgbIoX02bNg0++IB8WzA3DZlC4/YtmHxJK19H5RUeb0F5++23ef3113nzzTdZu3Yt8+fP5/HHH2f+/Pml9itbxGMYRoWFPZMnTyYzM7PktnPnTk+HLSIinlbTI3MCfaK3d96Bhx8GYHLf29h7Zgeevepsgv1sEcCq8ngLyt133829997LFVdcAUDbtm3ZsWMHM2bMYNSoUSQmJgLOlpQGDRqUvG7fvn3lWlWK2e127Hb/nnBGRKTWiWnrnHDt5CTFWyNzAn2it3XrYPRoAF4451L+16E371zTkXp+PhlbZTyedmVnZ2O1lj6szWYrGWacmppKYmIiixcvLnk+Pz+fFStW0KWL+htFRALG6c7x4Y5Anujtzz9h8GDIyWF504481n00Dw1uQ4eUGF9H5lUeb0EZOHAgjz76KI0aNaJNmzasW7eOJ598kuuuuw5wdu1MmDCB6dOn07x5c5o3b8706dMJDw9n5MiRng5HRER8pXhkzsZHnN06MW2dyYk3il8DdaK3vDwYOhR27uT3uGTGDbybEeemckWnRr6OzOs8nqA8/fTT3HfffYwdO5Z9+/aRlJTEzTffzP3331+yz6RJk8jJyWHs2LEcOnSIzp0789lnn2kOFBERf7J/ZZnkY2r57pTqzPFRHTXZnVRTDANuuQVWreJoaB2uu3QqTZsnM21Qa19HViMshmEYvg7CXVlZWURHR5OZmUlUVGCN+xYR8Qtlaz6Ku298VfNRYTx+PJfK9OkwZQoOi5Uxwx5gc/suLLztfBKjQ30dWbW5c/0OzNJfERHxLrPVfATaRG9vvw1TpgBwf6+b+brFOTx/TUe/Tk7c5fEuHhERqQXMWPPhbndSVbqofOHrr2HUKABeTh/M62f354lL23JWo7o+DqxmKUERERH3+XvNh1mHJW/b5hyxk5fH0hbn8miP67jxglQu65jsu5h8RF08IiLivpocQuwNZuuiAjh0CPr3h/37+SXpDG7vfyfnt0rk3n5n+i4mH1KCIiIi7vP3mg+zdVHl58OwYfDzzxyMiefaIVNJSKrH01echc3qepb1QKcuHhERqZ6aGkLsDWbqojIM+Nvf4PPPyQsN45oh95FdL5E3rk0nOjy45uMxCbWgiIhI7WOmLqqZM+GVV3BYrdwyYBKbE5sy+8qzOKN+nZqPxUSUoIiISO1jli6q//4XJk8G4MGeN7Ks2TlM7teKHq3q12wcJqQuHhERqZ183UX17bdw7bUAvNZpMPPPHsiI9BRuvKCp72IyEbWgiIiI1LRff4UBAyA3l69ancsD3a7jvKZxPDwkDYuldhbFlqUERUREpCb9+Sf07QsHDvBbSktu6ncnTepH8dzVHQkJ0mW5mM6EiIhITTlyBC65BLZvZ3/9hlwxeCohMVG8PPqcmh2xs38lLOsHC5Kd9/tX1tx7V5FqUERERGpCQYFzrpO1a8mOrsvlg+8nMyqW167uSGq9iJqLw6yz6JahFhQREfEffvDL3yXDgBtugM8+ozA0jCsHTeX32IY8emlbzm0aV7OxmHEWXRfUgiIiIv7BT375u/T3v8O//41hs/G3QffyQ1JLbunWjOHpKTUfi9lm0a2AWlBERMRzvNnC4Se//Mt55hl47DEA7u8/nsWpHemXlsikvi19E09M2xMT1BUz4UKPSlBERMQzils4MhZDzm7n/ZLunktS/OSXfynvvQfjxgHwQu8xvHbmRZzdKIZ/juiA1Vdr7JhpFt1KKEERERHP8HYLh5/88i/x5Zdw1VVgGHzcdTDTzxpKar0IXhp1DqHBtlO/3lvMMovuKagGRUREPMPbLRxpU501Jxab87gm/eUPwE8/waBBkJfH2rMu5LYu1xFXx868MecQGxHi6+h8P4tuFagFRUREPMPbLRx+8suf3393TsR2+DC/t+zAlT3GE2IP5qVR6TSOq8HhxH5OLSgiIuIZNdHCYfZf/hkZ0KsX7N7NX43PYEjfSeSH2HnuirM4q1FdX0fnV9SCIiIinuEvLRzecugQ9OkDW7dyLCmFfv2ncjgsigcGtKZvm0RfR+d31IIiIiKeY/YWDm85dgz694cNG8iPr8+gwQ/wZ2Q9bjg/ldFdU30dnV9SC4qIiMjpyMuDSy+Fr7+mMDqGyy+dxtaoRAa0a8DfLznT19H5LSUoIiIi1VVU5BxKvHgxjvAIrh/+ID/UbcT5Z9TjieHtfTfXSQBQgiIiIlIdhgE33wzvvYcREsLEKx9gRWwz2jaM5rlrOmIP8uFcJwFACYqIiIi7DAPuvhtefhnDauWRq+7jg3qtaRIXzqtjzqGOXSWep0sJioiIiLumT4cnngBg7tWTebn+WdSrY+ff13WmXh27j4MLDEpQRERE3PHsszB1KgDvjJzIrAbnUccexPzrzqFRXLiPgwscSlBERESq6uWX4bbbAFh6+c1MSrmIEJuVF67tSJukaB8HF1iUoIiIiFTF66/DjTcC8N3ga7k+dQBWCzx1RQe6NKvn4+ACjxIUERGRU3nnHRg1CgyDHweNZHjLy8FiYcbQtlzStoGvowtISlBEREQq88EHMHIkOBxsGTCcwa2uAIuFqf3PZMQ5jXwdXcDySoKye/durr76auLi4ggPD6dDhw6sWbOm5HnDMJg2bRpJSUmEhYXRvXt3fvrpJ2+EIiIiUn0ffwzDh0NRETsuuYyLz7wKw2JlXM/m3HBBU19HF9A8nqAcOnSIrl27EhwczKJFi9i0aRNPPPEEMTExJfvMmjWLJ598kmeeeYbvv/+exMREevfuzZEjRzwdjoiISPUsXgxDh0JBAXv7DqJX2rU4rDbGdG3CHb2a+zq6gGcxDMPw5AHvvfdeVq5cyZdffunyecMwSEpKYsKECdxzzz0A5OXlkZCQwMyZM7n55ptP+R5ZWVlER0eTmZlJVFSUJ8MXERGBFSugXz/IyeFAr35c2PFvZGPl8o7JzLysnaawryZ3rt8eb0FZuHAh6enpXH755dSvX5+zzjqLF198seT57du3k5GRQZ8+fUq22e12unXrxqpVq1weMy8vj6ysrFI3ERERr1i+HC65BHJyONyjNxel30I2VvqlJTJjaFslJzXE4wnKtm3bmDt3Ls2bN+fTTz/llltuYdy4cfz73/8GICMjA4CEhIRSr0tISCh5rqwZM2YQHR1dcktJSfF02CIi4k37V8KyfrAg2Xm/f6WvI3Jt2TLo3x+ys8nq1pOLzrudLMPGhS3ieeqKDgTZNLakpnh8sQCHw0F6ejrTp08H4KyzzuKnn35i7ty5XHvttSX7WSylM1DDMMptKzZ58mQmTpxY8jgrK0tJioiIv9i/EpZ0BwwwiiA3AzKWQK/lEN/Vx8Gd5PPPYcAAyMnhSI/e9OhyO38VWjmvaRzPX63F/2qax1PBBg0a0Lp161LbzjzzTP744w8AEhMTAcq1luzbt69cq0oxu91OVFRUqZuIiPiJjY9QkpzA8Xvj+HaTWLr0RHJyUR96dh3HwUIr6Y3r8tKodMJClJzUNI8nKF27duWXX34ptW3Lli00btwYgNTUVBITE1m8eHHJ8/n5+axYsYIuXbp4OhwREfG1wxtOJCfFjCLndjNYsqQkOTnWqy+9zh/HvgILHVJieHXMOURoZWKf8HiCcscdd/DNN98wffp0fvvtN958801eeOEFbr31VsDZtTNhwgSmT5/OggUL2LhxI6NHjyY8PJyRI0d6OhwREalITdWFxLQFS5kWCIvNud3XFi+GgQMhN5djfS6mV9dx/JkHbRtGM/+6TkSGBvs6wqrzlzqfKvL4MGOA//u//2Py5Mn8+uuvpKamMnHiRG48vn4BOOtNHnzwQZ5//nkOHTpE586defbZZ0lLS6vS8TXMWETkNJWtC7HYAIt36kIqfK8VEO/DlvNPP4UhQyA3l+y+l9Cny63syjZolRjJWzedS0x4iO9ic1dN/j1PgzvXb68kKN6mBEVE5DQt6wcZi0t3vVhskNgbeizy/PvtX+msOTm8wdlyknafb5OTDz6AESMgP5+ciy+hX9fb+f1oEc3r1+Gtm84lro7dd7FVR03/PavJneu3OtZERGqjmq4Lie9qngvlf/4D11wDRUUcGzSEfuk388fRIprWi+CNGzv7X3IC5q/zqQYN6BYRqY1c1YUAOPL8vnahUi+/DFddBUVFHB0+kr7n/I0/jhXRND6Ct246l/qRob6OsHrMXOdTTUpQRERqo7SpgIVyl4G8v5y1DIGYpMyeDTfcAIZB1qjr6dV2FLuOFtCsODmJ8tPkBE78PYuTlOIalLT7fBnVaVGCIiJSG8V3dRZQ2mPLPOHAdHOUeMKMGTB+PACH/3Y7vc4YQcbRguM1J+f5b8tJseK/Z2JvCGvovPd1EfJpUg2KiEhtFd8VrC7qLfyldqFc4e3U8iNWDAOmToXjs5v/dde99I2+iP1H82mZEMkbN3amnj/WnLhipjofD1CCIiJSm8W0dU49X3b0h9lqF8omI8mDYPU4Kp0+3zDgjjvgX/9yHuK+h+kXdDYHjubTKnwnb7T+lLicu6COeYbhygnq4hERqc38oXaheI6PjMWQs9t5//1YMBwVT59fVAQ33VSSnOx99HEutp3NgWyD1qHbeLPJPcT99b/ArbcJAEpQRERqM3+oXXC5lg8462VOUtw1lZ8PV18NL70EVis7Hn+GvvlpHMwxaBO2lTeaTiE2KMucawJ5g5/OMKsuHhGR2s7stQuu5vhwxWKDkDOd6+osXgzBwWx5fA6XHkjmWH4hHev8xiuNpxBtO3biNf5Sb1Nd/rKStAtqQRERkYqZ4de3yzlbrJTrmsoCpu5yJicREfww93UG7mvIsfwiujQK4t8t/lE6OSl+ndnqbTzJH1aSroASFBERcc1V7YcvajZc1clYrHDOnBNdU3SFfyTB+p8hLo6vX3yHYdvqkFfo4KKmQbwSfSkRhXvLHPh4kmOmehtP8+MZZpWgiIiIa2b59V1RnUzzW5xdU2csgrt+g607oVEjFr/4HldvgIIig/5tG/Bcs9mEWvMpV7Nij3W/3sYMLUru8OMZZlWDIiIirpnp13dFdTJffQUDB8Lhw9CmDe/PeJk7Vx3AMGBYx2RmXtYO24c/uK5hsdrdT078rZ4jbaozRout9CrHftBqpBYUERFxzey/vv/3P+jdGw4fxujShRcfeZWJK53JybXnNWbWZe2wWS2e+xxmaVFyhz+M0qqAWlBERMQ1M//6njfPua5OURFG/wFMH/0AL37zJwC39mjGXX1aYrFYnPt66nOYqUXJHWYfpVUBtaCIiIhrZvz1bRjOdXXGjIGiIhyjRnHXlffz4mpncnL/gNbc3bfVieQEPPc5zN6iFGAshmEYvg7CXVlZWURHR5OZmUlUVJSvwxERkZpQUAC33govvuh8eOdd3Nh6GMu3HCDIauEfl7fj0rOSvff+ZWtQiltifJ20+RF3rt/q4hEREfPLyoLhw+HTT8FqJfsfT3B16Dms3XKA0GArc6/qSI9W9b0bQ3FLTKkFCu9TcuIlSlBERMTcdu2C/v3hxx8hPJxDL/+bEXvrseWPw0SFBvHK6HNIbxJbM7G4U89RldWWpUJKUERExLzWr3cmJ3v2QGIiO+a/zZVrCtiTeZT6kXb+fX0nWiWasKu/bHdQzl7Y+wnY60FsupKVKlCRrIiImNOiRXDBBc7kpHVr1ry9iAGrctiTmUvTehG897cu5kxOoPyQ5OJJ4vIO+G5GXj+jBEVERMznhRecE7AdPQoXXcTHz73LlZ/u5UhuIR0b1+W9v3UhJTbc11FWrLIFDv1h/hQTUIIiIiLm4XDAvffCzTc75zgZNYqX/v4MYz/aRn6Rg4vbJPLGDZ2pGxHi60gr53KBw5P4w/wpPqYaFBERMYdjx2D0aHj3XQAc06bxUIfLmLd4GwBjujZhav/Wztlhza7s5HBlaf6UU1ILioiIlFfTi+Lt3OmsN3n3XQgOJv/lV/lb0/7M+3oHAFP7n8kDA9v4Ljlx93ycPDlcSD3AQskl10wz8pqYJmoTEZHSKpyQbLl3Rp58/TVcein8+SfEx3Po9f8weoudH3ZlEmKz8uSI9gxol+T5960qT5yPckOOXcyfUguGJbtz/VaCIiIipS3r5xxpcnLXhMXmbA3w9Jou8+fDTTdBfj60b8+vL77OqCV/siczl5jwYJ6/uiOdm8Z59j3dVRPno6aTQh9x5/qtLh4RESmtJhbFKyqCu+5y1pzk58Oll7Li5fcZ8r9dJcOIF4zt6vvkBGrmfPjjSslepiJZEREpLaYt5GaUbzHwVFFnZiaMHAkffwyAMXUq8/qM5uF3N+Mw4Lymccy9+mxiwk0yUsfb5wP8d6VkL1ILiohIIPBkUWvaVMByYpisJ4s6f/sNzjvPmZyEhlL05pvc33EED370Mw4DRqSnMP+6TuZJTsC756OYVkouRwmKiIi/K65fyFgMObtPf6bSk0eghDV03ntixd6lS6FTJ9i8GRo25MiS5YzJacZr3+zAYoHJ/Vrx2GVtCQky2aXJW+fjZDWRBPkZFcmKiPi7mixqrQ7DgFmz4O9/d07E1qkT2196g+s/3cW2A8cIC7bx1BUd6Nsm0deR+lZVRvr4OXeu36pBERHxd2auX8jKgjFj4P33nY9Hj2b5hGnc/u7PHMkrJCk6lBeuTSetYbRv4zQDd1ZKrgW83o42Y8YMLBYLEyZMKNlmGAbTpk0jKSmJsLAwunfvzk8//eTtUEREApNZ6xc2b3Z26bz/PgQHY8ydy5xr/86YtzZyJK+Qc5rUZeHt5ys5EZe8mqB8//33vPDCC7Rr167U9lmzZvHkk0/yzDPP8P3335OYmEjv3r05cuSIN8MREQlMZqxfePddZ3Lyyy+QnEzu58sZH92ZWZ9uwTDgyk6NeOOGc6lXx+67GMXUvJagHD16lKuuuooXX3yRunXrlmw3DIOnnnqKKVOmMHToUNLS0pg/fz7Z2dm8+eab3gpHRCRw1UQRZ1UVFsKkSXD55c6ViHv0IOPzrxi2roiFP+whyGrhkSFpzBhqwmJYMRWv/eu49dZb6d+/P7169Sq1ffv27WRkZNCnT5+SbXa7nW7durFq1SpvhSMiEtiK6xcu3eW890Vysm8f9OkD//iH8/GkSaya+yYD3t7Cxt1ZxEaE8MYNnbn63MY1H5v4Ha8Uyb711lusXbuW77//vtxzGRkZACQkJJTanpCQwI4dO1weLy8vj7y8vJLHWVlZHoxWRERO27ffwrBhsGsX1KmD8eqrvBB/FjPnrcFhQOsGUbxwbUeS64b7OlLxEx5vQdm5cyfjx4/n9ddfJzQ0tML9LJbSK1IahlFuW7EZM2YQHR1dcktJSfFozCIiUk2GAU88Aeef70xOWrXi2FerGJubyoxFzsnXLjs7mff+1kXJibjF4wnKmjVr2LdvHx07diQoKIigoCBWrFjB7NmzCQoKKmk5KW5JKbZv375yrSrFJk+eTGZmZslt586dng5bRETcdfAgDBrkXFOnsBCGD2frR0sZtOQgizZmEGxz1ps8fnk7wkJspz6eyEk83sXTs2dPNmwoPfZ+zJgxtGrVinvuuYemTZuSmJjI4sWLOeusswDIz89nxYoVzJw50+Ux7XY7drsqvUVETOOrr+DKK52tJnY7/OtffHTuQCbN+5Fj+UU0iA5lzlVnc1ajuqc+logLHk9QIiMjSUtLK7UtIiKCuLi4ku0TJkxg+vTpNG/enObNmzN9+nTCw8MZOXKkp8MRERFPcjhg5ky47z7nisQtWlDw5ls8ttfOy/9ZBzgX+3t65FkaQiynxSczyU6aNImcnBzGjh3LoUOH6Ny5M5999hmRkZG+CEdERKpi3z645hr47DPn46uvZvf0J7h14a+s37kHgFu6NeOuPi0IsmkIsZwercUjIiKntmwZjBwJGRkQFgbPPstn51zMXe/+SFZuIVGhQTx+eXv61Pb1dKRS7ly/leKKiEjFCgvhgQegVy9nctK6NQVff8vD8Z256fW1ZOUW0iElho/GXRCYycn+lc7FGBckO++ru0K0uE2LBYqIiGu//QZXX+2c4wTguuvY9eBMbv3wF37YeRiAG85PZdLFrQJzVtj9K2FJd8BwLr6YmwEZS5yz9sZ39XFwgU8JioiIlGYY8MorMH48HDsG0dEwdy6ftO3OpJfWkJVbSHRYMI9f3p7erV1PDxEQNj5CSXICznuLzbldqw57nRIUERE54cABuOkmWLDA+bhbN3JeeoWHfjzKf15fC0CHlBieGXlW4E+8dnjDieSkmFHk3C5epwRFRMST9q90/sI+vAFi2jpXGvaX7oDPPoPRo2HvXggOhkceYeMVNzDuvz+ybf8xLBa4+cJmTOzdIjC7dMqKaevs1jk5SbHYnNvF65SgiIh4ir/WLOTmwj33wOzZzsetWuF47XVezq7LrOe/oaDIICHKzj+Hd6DLGfV8G2tNSpvq/PtZbCe6d7BA2n2+jqxWqAUpsIhIDXFVs4BxfLtJ/fADpKefSE5uvZV9y1Yy6odCHv14MwVFBn1aJ/DJ+AtrV3ICzqSy13JI7A1hDZ33vVb4ZqXoWkgtKCIinuJuzYIvu4MKCuCxx+Dhh53/nZAAr7zCp43PZvKLa/jrWD6hwVbuH9CGKzulVLiYa8CL76qCWB9RgiIi4inu1Cz4sjvoxx9hzBhY6yx6ZcgQjsx+lmlf7+e919YA0LpBFLOv7MAZ9TXDt/iGEhQREU9xp2ahpoawntxKU6cNLG0ET853tprExsLTT7PqnN7c9fqP7MnMxWqBm5qs4Y56r2D/6Uyw+FGRrwQUJSgiIp5SXLNQqtvmPtc1CzUxhPXkVpodRfD8bvj9+HODB5P79LPMXH+YV1/+DoBG0VaejJ9EesRmyCuCjF3+UeQrnmWSkWhKUEREPKmqNQs1MYR14yNQ6ICFDlgAFAF1gPHt+OGmV5n4zg9s3X8MgKs6N+LvdaYQcWCzJiarzUw0Ek2jeEREfCFtKmA53g2Ed4awrl8L9zvgXZzJSUfInRHErPoXMPS5r9m6/xj1I+28OuYcHr20LRFH12pistrORCPR1IIiIuIL7nQHuSsnxzk6Z9b+E60m18Ka9i2ZtHsCW/NSAIOB7ZN4eHAbYsJDnK8LxInJTNJd4TdMNHuuEhQREV/xxhDWJUvglltg61bn43Ms5Iyy83jO1byybRAGVuIjLDx86dlcnFZm9eFAm5jMRN0VfsNESaq6eEREAsH+/XDNNdC7tzM5adgQFizg6+c+5+IDL/HygSEYWLmsTQiL7+xVPjkBc05Mtn8lLOsHC5Kd9/tXVv21Juqu8Bs10fVYRRbDMIwaf9fTlJWVRXR0NJmZmURFRfk6HBER3zEMmD8f7rwT/voLLBa47Taypj7AzJV7eOPbPwBoEB3K9KFt6dGyvo8DdqGibpiyLSDFF8uqtoAsSIac3eW3hzWES3d59jNUlT90OZWL0UNdj7h3/VYXj4jUbv5wwajIli3O7pxly5yP27XDeP55Pg5vzLQX17H/SB4AIzs3YnK/VkSGBvsw2ApU1g1zunPFmKi7AvCfLieTzJ6rLh4Rqb2KLxgZi52/tDMWOx+7043gCzs/h+uaQ1pLZ3ISZoeZM9m1+Auu/9nGrW+uZf+RPFLrRfDmjZ2ZfmlbcyYnUHk3zOkWbJqouwJQl5OblKCISO3ljxeMN/8B5/SEV3+DAqAdFE538GLbjvR++ms+/3kfwTYL43o2Z9H4C+jSzOQL/FWWhMS0PZFcFHOnBcRsNTUmGiHjD9TFIyK1lz8t7rd1K0yYAP/3f87HdYGRsL59c/6++3Y2rcgFoFNqLNMvbcsZ9evUTFynq7JuGE+MKjJJdwVgvi4nk1OCIiK1lz8s7pedDTNmwD/+AXl5YAP6wV8DIpmVOZq3t/bGwEq07RhThpzHsI7JWK1+tPJwZUlIfBfvzRVzuqqTrAbaMG4v0ygeEam9Khwl4qIbYFk/Z41K2WQmsbfzF7qnW1cMAxYsgDvugD+cI3Ho3Zuikbn850gU/8i4mswi50rDQ+t+zt/bb6fexe9X//18yYujRrzidEYX+dtn9TB3rt9KUETEP7mTEFS2b1UvGJUNWT3/7dMbDlvWzz/DuHGweLHzcaNG8NRTrD27G/e/9x0b9zmTpDNDt/Fw8vOkR/zi+/lKapNTJatSIQ0zFpHA5k53y6n29cTifqc7HLbYgQPw4IPw3HNQWAh2O0yaxIHb7mDWF3/wztyvAYi0w52pX3B11H8IqtsG0l5QclKTVOxaI5SgiIj/cSch8FTyUFn9wFfDT++ClZcHs2fDo49CZqZz28CB5M16nFf/tPHss99xJK8QgGEdk7nn4lbER/YHZlZ8TH+e38XsVOxaI5SgiIj/cecXrKd+7Va2uF91L1iGAe+8A/feC7//7tzWoQPG44/zcXxrHlu4mZ1/5QDQtmE00wa1pmPj2FPH6i8TgvkrFbvWCCUoIuJ/3EkIPPlrt6LuoOpcsL7+GiZOhG++cT5OSoJHH+XHHgN5eNEvfL94LQAJUXYm9W3FpWc1rProHE+1Golr3lyJWkooQRER8yvbXZE8qOoJQU382nXngrVtm7PF5L//dT4OD4d77iHjhrHM+nIX7891JiyhwVZuvrAZN3drSniIm1/VqpHwPjPNrxKglKCIiLlV1F2RPht2LTx1QuCNX7sV1XdUdsHKyHDWmDz/PBQUOBf1u+46su97gOe35PD83O/JLXAAMPSshtx9cUsaRIdVLz7VSEgAUIIiIr51qmLOirordi2s+i9YT/7adbe+49Ah5yRr//qXc9I1gN69KZgxk//k12X2a79w4KhzUb/0xnW5b0Br2qfEnF6MqpGQAKAERUR8pyoXe7N1V1S1vuPYMefInFmz4PBh57ZOnSh6dDoL41ry5OIt7PxrDwCNYsO55+JWXNI2EYvFA7PAqkZCAoASFBHxnapc7M3WXXGqhCk/H154AR55BP7807mtTRuMRx5hafNzeXzxFn7O+AGA+Eg74y46gxHnNCIkyMNrt5q5RkJDoKUKlKCIiO9UpXXEbN0VFSVMkW1g/nyYNu3EkOHUVHjoIb7t3IdZS35jzTdrAIgMDeKWbs0Y07VJ+QLYmrh4+zJB0BBoqSIPp+wwY8YMzjnnHCIjI6lfvz5Dhgzhl19+KbWPYRhMmzaNpKQkwsLC6N69Oz/99JOnQxERs9m/0jlN+IJk53148vGE4yRlW0eKuysSezunlU/s7dtp3dOmApYTcRdZYbkBN/4Eo0c7k5MGDWDOHDYu+ZbRBS0Y8fL3rNlxiNBgK7d0a8aXk3pwa48zXCcnS7o7p1HP2e28X9Ldud2Vsuezov1O5z08zVWrGcbx7SIneHwtnosvvpgrrriCc845h8LCQqZMmcKGDRvYtGkTERERAMycOZNHH32UefPm0aJFCx555BG++OILfvnlFyIjI0/5HlqLR8QPuVpgzQCKSy5OtVifmexfCesegg++gwV5kOGcTI169eDuu/lxyNU8tWo3n/+8D4Agq4UR56QwrmdzEqJCKz6uqzVeAOz14MIPSrcwVHfBOl+vI1PZmkaX7vL++4tP+XQtnk8++aTU41dffZX69euzZs0aLrzwQgzD4KmnnmLKlCkMHToUgPnz55OQkMCbb77JzTff7OmQRMQMKqo3iU2HkLq+L+Ys7vY4uPpE0hSbXr77Iy8P3v0RZmyGnYed2+rXh7vvZv2AK3jq6z0sf2UdAFYLDO7QkPE9m9OkXsSpY3DV5QWQd8CZjJycfFR3MjZPFB2fTheR2WqKxLS8XoOSeXxdidhY5/TM27dvJyMjgz59+pTsY7fb6datG6tWrXKZoOTl5ZGXl1fyOCsry8tRi4jHVXRhzN4Ffb/xTUzFilsjDAfgOLF972cn6iPqnA0vvQQzZ8Lu4y0AiYlwzz2s7TuMf67azZfznMWvNquFIR0acmuPZjSNr1P1OFxdvEsYpZOP6iYap5sgnG4NidlqisS0PF6DcjLDMJg4cSLnn38+aWlpAGRkZACQkJBQat+EhISS58qaMWMG0dHRJbeUlBRvhi0i3hDT9tT1Jr5S3BpxcnICzsfHHDDlOmjaFMaNcyYnDRvC00/z3dLvuarOuQyd/wNf/noAm9XC5R2TWTqxG08Mb+9ecgIn6ltcKZt8VPd8lq2hcTdBON0aErPVFIlpebUF5bbbbuPHH3/kq6++Kvdc2bH+hmFUOP5/8uTJTJw4seRxVlaWkhQRf2PmX86uWiMOAp8CSx2Qu8W5rVEjHPfcy4rz+zN31W6++7ezxSTIamFYx2TGdj+DRnHh1Y+j+OL9xRBnt87JyiYf1T2fpztHiie6iMw8BFpMw2sJyu23387ChQv54osvSE5OLtmemJgIOFtSGjRoULJ937595VpVitntdux2u7dCFRF3VLf+wMyTh53c7bEL+AhYCRRfh5vUofC+2XzYuhvPf72TLW86L8bBNguXp6fwt27NSImtJDFx55zFd3UWxLoqgD05+Tid83k6CYJqSKSGeHwUj2EY3H777SxYsIDly5fTvHnzcs8nJSVxxx13MGnSJADy8/OpX78+M2fOrFKRrEbxiPhIdUeOmN2+r+CZ7vC/Ilh/0vZWkN0/jDd6LOTlHyxkZOUCUMcexMjOjRjTtQkN8tdWnnxU95yVS2pMksxV+HnUTSOn5tNRPLfeeitvvvkmH374IZGRkSV1JdHR0YSFhWGxWJgwYQLTp0+nefPmNG/enOnTpxMeHs7IkSM9HY6IeFJ1R4644uvJwjY+Agd+hA3x8HEBrDn+mSxApxD+uiSeFxNu4vU96Rz50lmkXz/SznXnpzKycyOiQoOrVjBa3XNm1m6QmmoJ02yztZ7HW1AqqiN59dVXGT16NOBsRXnwwQd5/vnnOXToEJ07d+bZZ58tKaQ9FbWgiPiIp+aw8GVLzP6V8H43+NwBiw04dHy7PQRGj+HXq27kxT+D+GDdHvKLnEWzzeIjuPnCZgw+Kwl70EmFqVWZU0TzfrgvUFvqxLctKFXJdywWC9OmTWPatGmefnsR8aaKhsE68pwXlapePDzZElNWZb+8162DySPg8yIoOL5/NDh6Wvm810hesIziu49OjCZMb1yXm7s1o2er+litLn58VaVgVDUb7vPmvw/xG1qLR0SqrnjkCFZKDcnN+6v8RGKVqejC/ucyZ4tDdZv0XXW57F4M+dPg1U/h5BGFTeFor1Bea34J/84cyN4d8cBfBFkt9GvbgNFdmtCxcd3K368qyYeZRy+ZldlWsBafUIIiIlVX4TBYB2Cp+i/cylpicnZXfwG5k395/wWsKIJlwMHjyUBQEFwYz7ZzbcyNHMqHmd3JPxACQFxINled346rzm1c+XT0J6tK8mHm0UtmpVYnQQmKSO11OsOFrS6G/bvzC7fshd3VsarTpH/wR1hXBEuBdTjnXgOIslJw22Q+u2AIr245wOo9RXDY+VTbsN8YXe//GDB0GvYGLav+XlD15MOsBa9m5W+tTiro9QqPF8nWBBXJipym0y1C9MSCcyd/qecdcLaelFW2kNTVhQBg2VRYsAaWHIMDJ3U9tYLd59fjlaZDeedYX44UOhOrICv0q7+F0bELODs5AktbtWiYjlmHWZelgl63uHP9VoIiUhudboLh6bkwqhJP2fc0rPADztE46zhRElMHCs63saRjJ54PuYz12a1KDpkc8idXdmrM5Rd2pX5Vu3FEKuPr1aH9jE9H8YiIHzjdIkRP11VUpUm/uL5kZxF8Cax0nBgiDBgtYdf59Xkp9VLeO9aLo4VhUAhBFNIn+huujP2ErpEbsUb0gqiep45JzfZSFSro9RolKCK1kSeKED1ZV3GqhOfgQfjP17C0CLad9Lo6cLRLKP87+wJeCRnCr3mN4YjzqSb2DK6ou4jL6i4lPvjwiddU5cJxuiv2Su2hgl6vUYIiUhuZsQixbMJTUAALF8L8+fC//zkfA9igsJ2V1R1b81zDy1iR2xHDsEIe2C159In6hivjPuHciI1YLRZKDYeu6oVD83BIVZnx/6UAoQRFpDZw1V1RlS6amu7mcDjg66/hrbfg7bdh//6Sp4y05mxvncO8FgN4t6gn2Y4wcC6NQ6eIDVxW93P6Ra8kypZ9/BVWwKjehUPN9lJVGkbuNUpQRAJdZd0VlbUG1FQ3h2HA2rUnkpKdO088lZDAvoGX8X7bnsw7GsWfWXklM8A2CT/M0A6JXNo6mJTV01yMAnJASD2IS3f/wuHtZnvVtwQWDSP3CiUoIoHE1YXPVXcFwNKekNCj4oujt7s5Nm1yJiVvvQW//lqy2YiM5FCf/nzWtjvP2lLZebQA9gDkERUaxMD2SQw9O5mzG8WcWPtrZw/XIyni0qsXqzeb7VXfIlIlGmYsEigqGvobElNm1teTVDZnQ0WL3Fnt0HOp+xdTw4CffoL334d334UNJ7pLjLAwsnpdzNL2PXg2tDlbj5xINCJCbPRuncCAdklc0KJe6cX6TvXZqzvsufiY3mi2r2hYamw6hNRVq4oENM2DIlIbVXThC6kL+Ydcz9havI+rORtcHa/kNUFV+8XvcMB33zmTkgUL4LffSp4ygoM53K0ny8+6iOej2vDz0RNfReEhNnqemUD/tg3o3jKe0GAXSUlZ/jKxV0WJH5RvsVGrigQYzYMiUhtVVNhpAFgqn1beVfFnycKArhgVd/UUFMCKFc6E5IMPYM+eE6+y2zlwXjc+P7MLc2LassNxfMr8owahwVYualWf/m2TuKhVfcJCqpCUnMxf6gAqWocINGpI5CRKUEQCRUWFnXHpJ2pR/lxWvpi0ouLP4tEJS3uWf03ZpOavv+CTT+Cjj2DRIjh0YgY1IzKSnV0uYtEZ5/J8RAv+shxPShwQHRZMz1b16d06gQtbxBNhrwVfSa7qW9xJHEVqiVrwbSBSS1RW2BnfxflLvKJajYqKP+O7Ogtpy3X1WOFgE5gxAz74D6zeUGq6kYK4evzc+SIWND6HNyLOIM8WXPJcw5gwerdOoE/rBM5JjSXYZvX4qTA1V8NS8w/BX6s12ZeUVstHe6kGRcTfVPalVZU6DHdrNYqTmlwHbHTAegusN+Cv0rtlJkXwXfM03krpw7IG6TisJ7pozmwQRZ/WCfRpk0DrBlEnRt+4+/kClTeKfMW/BegihCqSFQlUNfmlVVgIq1fD4sWw6H34/gcoPPF1URQaxu/Nk/msUXteb9SP3VEJJc9FWrM5P24H3et8z4WNimiQfkfV4gvQL+Uq8VSRb21M8AJRgC5CqARFJFB580vLMJyjbBYvhiVL4PPPITOz1C6ZDZL5ttW5vJPQji+T2pAXbC95rnXoVrpHrqF71BrOCv+ZYEvRifiqmmSY9UvZXy76tTnBCzQVjfYKawiX7qr5eDxEo3hEApUnp2A3DPj9d+eImy++cCYkO3aU2iUvMpoNLTvycUIbFie3Z2dMYslz8ZF2utT5ka5Bn9K9zvfUDz6ES+6MSDHjFPNmmljtVImSr9YQ8pcEzp9oEUIlKCLV5osv5dP50jIM54ytK1acSEpOmlYeoCg4mC3N2rE4qS1LktuxMaFZSS1JVGgQfZrG0fWMenRpFscZ9etgORAGS+4Bo/AU713FJMOMX8pmWTiwKomSLxI8MyVwJ8fk7wmTFiFUgiJSLZ74Uq7Ol6g7X1oFBfDDD87F9776ypmQZGSU2qUoKJhfG7ViRWIrVjZM4/vkNuSEhALOydK6Nq5L1zPq0bVZPVonRWGzliluLR6R8sWQimerhaonGWb8UjZLq05VEiVfJHhmSeCKmTFhqg4tQqgERaRaqvqlXFESUvZLNGcv7P0E7PWcU55XlKxU9qX155/OZOTrr+GLT2HdBshzlHp5QXAIP6WcyYrE1nyb0oa1DVuRG+xMSOrVCaF7k1jSm8TSqUksZzaIJKgqQ4Dju8KFH5T+PGUZRc6htPtXVn6RMOOXsicv+qfzy74qiZIvEjyzJHDFzJYwnQ5/mXzQS5SgiFTHqb6U96+EtXfCwW9PPH/yL7myX6LFk4jkHXAWibr6xXfyxS20NYRMhQ358NKz8PXVsH17uTCPhIWxtsGZfN+wNd+lpPFDgxbkBYUAkFovgkFN6pLeJJZzmsTSJC688uG/lSmbWIQnQ8ERyNp0Yp+D38Li8yGuM5z9RMUXZrN9KXvqon+6v+yrkij5IsEzW7ec2RImX/Lzri6N4hGpqpP/Z3fkQd5flJqdrHi0SdpU54XIVV1G8T6HN1S8HsvJ+/VYBDk5sPzf8PZY2G7ANgN2c3wK+xMcFgtb4xvzfYOWrEtqxdqGrdgW2xDDYiXGlkX7uofo0L4XHVJiaJccTVwdu8u39hhPrOVjli9YTwwBPt0RSmadK8VscZl1JFhNM+mILg0zFvG0sv+zl3Xyl/LGhyu+MINzmGBMW9f7ZAI7j992h8PBM5wrABeVP9a+iLr8mHgGPzZowdqkVvyQ1IIj9ghCLAW0DttKh7AtdAjfQofwX2gcshdLeA0PTzzVoninumCY9Au22jwxbNSsCyKaKS6zJUy+YtJETcOMRTytXJdMGZEtITgSVgyG/DItKycrbvo+4274ZjH8YYE/DPgDZ1JSatqRbOBHAPaHx7Ah8Qw2JDZnQ+IZ/Jh4Bvsi44i0ZdO6UTJtkqK5NCmKNg2jaPbTlQTv+9T3Te6nWhTvVE3ugVRLAJ7pCjFb91cxM8VlxjomXwiAri4lKCJV4ep/9hJWyNoMWCiXmOQBe4/f9gB7HBiHfobf+mBx0SriwMKOuon8HJ/Kz/FN2Fw/lR8Tm5MRGUeyfR8t7DtoHbaNy8KW0ibsd1IatcPSdipsvBP+2ABZbaHRINj3WdVrJrzVjVLZashVuTB74wvWl11G1a1lMUs3lzd467OZKWHyFbPVBlWDunhEqqKyegoDZ8vHnuO3vWDsBsceC7aDFf/vdTi0TkkS8nN8E36Ob8KWeo2pG1VA88S6tEhJoUVCJC0SIjnDupGIL3pQqtnaAKJali5ELb7opc+GXQtP/QvS290oroqFq9rk7ukmajN0GVV3HSQzdHN5Opkw02cLRCbt6lINioin7V0Bb/eEfUWwD4w/ofBPG4V/2gjaX0RwTkWtK3AoNJLf4lLYGpfMttiGbKnXmF8TGhES5yA1KpfURi1p0aQFzRMiaV6/DpGhwa4PdPIFIjy5/Oq3xdy5iFeUBMSmQ0hdz16M3G1y9/QXbE33yXvigm6WOgJvJBNm+WyBzEy1QccpQRH/cbpf4sWvP7ja2cMCzotr8qAyLQinOG5hIezd65xZdedOCrZuI/eX33Bs3UrQjt8J27sLq4sumWJFFit/xCSwNTaZrXEpbI1NZntcEjmJduom2EltcQFN4iJIDdlB6qZraBiyj2Dyq/9FX1mLDlS98PJUhay+/mXryS/YmlzbxFMXdLOsx+KNZMIsn01qlIpkxXcqSzjKtgCUnScjZ7dzsrJTzZNx8nst6Q6Gg1K1H3s/dR4Hq3P7sT2w+RM4Ek1BbjOyi84nf5+B448/sO7ahT1jDxEH92FznPjyDT5+O1meLZg/YhJL3Q7Vq0thcgNs0XtIDD9Iw+A/aR7yBz1DviMlZB92m+P4L/4050GWjQX73ooLP6uasFVWE+NOP/OpClldxViTPFlLUJN98p4q8DVLHYE36oHM8tnEtJSgyKm5umiC620n/2osnogqfTZsm1+6DqGyOUAOfus8TmW/Ng0Dvp9G3h4bR/8KJ/evEPIPBVN02IZxGIIyi7BnFRB+JJfIY9nYHM5CkWDWEs1al4cssNr4s04ce6LqsTuqPrvrJpDVoCG5jc6gqFkzIhsnkxwXQcO6YVxQN4ykmDDCQ4JKn6NSLTkXlf/FX9kXvTsTeVWWWLgziZirws3qjroxu5qcZdVTF3SzTP1fnWTiVMm2WT6bmJZPu3jmzJnDP/7xD/bu3UubNm146qmnuOCCC075OnXxnIbKpl4/uXUD4MhWMAqgILOCgx1voShmCXIxOZkVZzWn639mhYaVY4WhHM0JJzfLTn5WMAVZQRQeDaIoNw6OWLAcyiQo20JItkF41iEijh0jMvsooYUFVf7YDizsq1OXvZHxZETFcSg6kmMxYRTEBUEc2Nt0IzL4R+oXriEx6CCJwQeJDspxzqxaruC0mvUY+1e6XrOmuKkcXHTbWMEeC1Z75VPlF4vrDGc/6V43SNl/E/mHyte3BEptQE31yXuyS8QMdQTu1gNVtYvLDJ9NapRf1KC8/fbbXHPNNcyZM4euXbvy/PPP89JLL7Fp0yYaNWpU6Wu9lqCU/RXsKADr8Yb+Os2c90e2ln6uKvuc7us9uU9BFuWGw9oioOhYhafFMKDACCLHsJPrsJPrCCHHYSfHEUJeXggFuUEU5ARRmBdEUa4Vsi0Y2WBkgyUbrDkGtpwignOLsOfkY8/NJyw3j/DcXOrkZROZl02I4xSr4VbgWHAo++vU5VCdSLLq1OFYVCj5UcE4oq1YYhwE1y0krG4+dWKOUc+eSb2gw0TZjuH+jO7Hk7Hq1hJU1B2FFSxW5xf9V8NPPbvsye9dlS93d2p8Tv73n3+Qkn8nJqn+9ysmHUFxWjVf7iQTKoCVCvhFgtK5c2fOPvts5s6dW7LtzDPPZMiQIcyYMaPS13olQanwAuIZhgGF2Cg0gigwnPeFho2CMveFRhAF2Cgwgit+3mGlyBFEUZGFwkIbjiIbRQ4LjiIrjiIrRYVWjCIrFDobQCwFBpZCoMDAWmBgLTSwFDqO/7cDW4EDW6EDW2ERtsIiggqLsBU6CMkvICS/AHt+AWEFeYQX5JbcwgryCCvIw2Z47lzl2YLJDI/gSHgE2eGh5NYJoSAiCEcdK0akBWsdB0FRhdijCgiLzqVOdA7REceIsOZUI+E4DdX5oq2oqNVeDy780PlFf6rCV3ff251CzXItMscTspLFC/XL1m1max2oyWG9KoCVCpi+SDY/P581a9Zw7733ltrep08fVq1aVW7/vLw88vLySh5nZWV5PqiNj3DwzzocfCESw7BgYMEwAMPifFz83xRvo6TnwsBy4r+L0z3DcqJXw3D+p9UwsBgGFjh+73zM8cdBjiLsRj5BDgc2RxFBjqIT94aj5HGwo5ILmI/kBwWRHxxMnj2YvNAQ8kKDKQwLojDUihFuhTCwRBhYwxwERRQRElFASEQ+oRH5hEXkERJVhN1eQH3LYepzuBoRWHA5UZo3VKeWoKKiVqv9xEWrsonNqvPe7hRqulq8sHi4sX7xVo/ZJguryZl5VQArHuCTBOXAgQMUFRWRkJBQantCQgIZGRnl9p8xYwYPPvigd4M6vIFj2SG02LzTu+/jZUVWCw6rFYfNgsNmoyjISlGwFcfxeyPIgiPIghF8/BYERrDl+LAVCwQZWEKAIAOb3UFQaJHzFlZIiL2AoFAHllDAXvoWYiskhELqkFO1QKNaO6eGz97l/NJKHgxrJjgX4XNXZTUcxRfZo1vL135UV3W+aN1ZiXZpz4rPgzvv7U6hZgBMiy2nUJN/YxXAigf4dBRP2aXdDcNwudz75MmTmThxYsnjrKwsUlJSPBtMTFsi475m0eXnYbM4sFoMrJYi539bnY9tlqIT91YDm8VxfN8ibFYHtuL9LQ6CrI6S19ssjhPPWx0EWQqPH7f4RBy/2XC2rNvK/HfZ+4q2WcBmMbBR/CVUvbqOqilTIOvyORf7nNylUdauD0/dxeHyvYq/+AzXX4pnP+l8zlVRqbuq+0Vb1S/s+K6Q0KOS8+DGe7vzK1a/eANfTf6NtR6OeIBPEpR69ephs9nKtZbs27evXKsKgN1ux2738tLwaVOpm9GdfkO+pUa6CXzGQkUjakoUt24UF9nC8cnPBjuTiJO/cDAqmChtMKy+3fl+J1+QK0pOoPxFvLIkKDjaWfhbtj6isi/Fss+VxFgmaYlqDUd+cf63p+ox3PnCLncejnN3hI47v2L1izfw1fTf2GxdXOJ3fFok27FjR+bMmVOyrXXr1gwePNg3RbIQ+KN44MQFtqLEwpO/cqo7vXnZ1xTH6o1fYhXF6OsCR0+9vzvH8fVnFu/T31h8zC9G8RQPM37uuec477zzeOGFF3jxxRf56aefaNy4caWv1TwoIiIi/sf0o3gARowYwcGDB3nooYfYu3cvaWlpfPzxx6dMTkRERCTwabFAERERqRHuXL+tlT4rIiIi4gNKUERERMR0lKCIiIiI6ShBEREREdNRgiIiIiKmowRFRERETEcJioiIiJiOEhQRERExHSUoIiIiYjo+m+r+dBRPfpuVleXjSERERKSqiq/bVZnE3i8TlCNHjgCQkpLi40hERETEXUeOHCE6OrrSffxyLR6Hw8GePXuIjIzEYrFU6TVZWVmkpKSwc+dOrd9Tg3TefUPn3Td03n1D5903qnPeDcPgyJEjJCUlYbVWXmXily0oVquV5OTkar02KipK/4B9QOfdN3TefUPn3Td03n3D3fN+qpaTYiqSFREREdNRgiIiIiKmU2sSFLvdzgMPPIDdbvd1KLWKzrtv6Lz7hs67b+i8+4a3z7tfFsmKiIhIYKs1LSgiIiLiP5SgiIiIiOkoQRERERHTUYIiIiIiphNQCcqcOXNITU0lNDSUjh078uWXX1a6/4oVK+jYsSOhoaE0bdqU5557roYiDSzunPf333+f3r17Ex8fT1RUFOeddx6ffvppDUYbONz9915s5cqVBAUF0aFDB+8GGKDcPe95eXlMmTKFxo0bY7fbadasGa+88koNRRs43D3vb7zxBu3btyc8PJwGDRowZswYDh48WEPRBoYvvviCgQMHkpSUhMVi4YMPPjjlazx6XTUCxFtvvWUEBwcbL774orFp0yZj/PjxRkREhLFjxw6X+2/bts0IDw83xo8fb2zatMl48cUXjeDgYOPdd9+t4cj9m7vnffz48cbMmTON7777ztiyZYsxefJkIzg42Fi7dm0NR+7f3D3vxQ4fPmw0bdrU6NOnj9G+ffuaCTaAVOe8Dxo0yOjcubOxePFiY/v27ca3335rrFy5sgaj9n/unvcvv/zSsFqtxr/+9S9j27Ztxpdffmm0adPGGDJkSA1H7t8+/vhjY8qUKcZ7771nAMaCBQsq3d/T19WASVA6depk3HLLLaW2tWrVyrj33ntd7j9p0iSjVatWpbbdfPPNxrnnnuu1GAORu+fdldatWxsPPvigp0MLaNU97yNGjDCmTp1qPPDAA0pQqsHd875o0SIjOjraOHjwYE2EF7DcPe//+Mc/jKZNm5baNnv2bCM5OdlrMQa6qiQonr6uBkQXT35+PmvWrKFPnz6ltvfp04dVq1a5fM3XX39dbv++ffuyevVqCgoKvBZrIKnOeS/L4XBw5MgRYmNjvRFiQKrueX/11VfZunUrDzzwgLdDDEjVOe8LFy4kPT2dWbNm0bBhQ1q0aMFdd91FTk5OTYQcEKpz3rt06cKuXbv4+OOPMQyDP//8k3fffZf+/fvXRMi1lqevq365WGBZBw4coKioiISEhFLbExISyMjIcPmajIwMl/sXFhZy4MABGjRo4LV4A0V1zntZTzzxBMeOHWP48OHeCDEgVee8//rrr9x77718+eWXBAUFxP/2Na46533btm189dVXhIaGsmDBAg4cOMDYsWP566+/VIdSRdU57126dOGNN95gxIgR5ObmUlhYyKBBg3j66adrIuRay9PX1YBoQSlmsVhKPTYMo9y2U+3vartUzt3zXuw///kP06ZN4+2336Z+/freCi9gVfW8FxUVMXLkSB588EFatGhRU+EFLHf+vTscDiwWC2+88QadOnXikksu4cknn2TevHlqRXGTO+d906ZNjBs3jvvvv581a9bwySefsH37dm655ZaaCLVW8+R1NSB+StWrVw+bzVYum963b1+5bK5YYmKiy/2DgoKIi4vzWqyBpDrnvdjbb7/N9ddfz3//+1969erlzTADjrvn/ciRI6xevZp169Zx2223Ac4Lp2EYBAUF8dlnn3HRRRfVSOz+rDr/3hs0aEDDhg1LLS9/5plnYhgGu3btonnz5l6NORBU57zPmDGDrl27cvfddwPQrl07IiIiuOCCC3jkkUfUQu4lnr6uBkQLSkhICB07dmTx4sWlti9evJguXbq4fM15551Xbv/PPvuM9PR0goODvRZrIKnOeQdny8no0aN588031SdcDe6e96ioKDZs2MD69etLbrfccgstW7Zk/fr1dO7cuaZC92vV+ffetWtX9uzZw9GjR0u2bdmyBavVSnJyslfjDRTVOe/Z2dlYraUvbzabDTjxi148z+PX1WqV1ppQ8TC0l19+2di0aZMxYcIEIyIiwvj9998NwzCMe++917jmmmtK9i8eDnXHHXcYmzZtMl5++WUNM64Gd8/7m2++aQQFBRnPPvussXfv3pLb4cOHffUR/JK7570sjeKpHnfP+5EjR4zk5GRj2LBhxk8//WSsWLHCaN68uXHDDTf46iP4JXfP+6uvvmoEBQUZc+bMMbZu3Wp89dVXRnp6utGpUydffQS/dOTIEWPdunXGunXrDMB48sknjXXr1pUM7/b2dTVgEhTDMIxnn33WaNy4sRESEmKcffbZxooVK0qeGzVqlNGtW7dS+y9fvtw466yzjJCQEKNJkybG3LlzazjiwODOee/WrZsBlLuNGjWq5gP3c+7+ez+ZEpTqc/e8b9682ejVq5cRFhZmJCcnGxMnTjSys7NrOGr/5+55nz17ttG6dWsjLCzMaNCggXHVVVcZu3btquGo/duyZcsq/b729nXVYhhq7xIRERFzCYgaFBEREQksSlBERETEdJSgiIiIiOkoQRERERHTUYIiIiIipqMERURERExHCYqIiIiYjhIUERERMR0lKCIiImI6SlBERETEdJSgiIiIiOkoQRERERHT+X/c4mAogG/e9gAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -838,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.004999999999999987\n"
+      "0.004999999999999996\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index ee16dc42e..45cef3d7e 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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.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_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1763,21 +1763,322 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m      9\u001b[0m     dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m     10\u001b[0m                         n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m     15\u001b[0m     test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
-      "Cell \u001b[0;32mIn[6], line 98\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",
-      "Cell \u001b[0;32mIn[6], line 38\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  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09166666666666666\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "  exp_term = np.exp(self.z_o)\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
      ]
     }
    ],
@@ -1822,7 +2123,52 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1 + np.exp(-x))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_1.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "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",
@@ -1889,7 +2235,590 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.18333333333333332\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.18611111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.13055555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.24444444444444444\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.23333333333333334\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.12777777777777777\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.1527777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9111111111111111\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8305555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.8944444444444445\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.975\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9777777777777777\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9777777777777777\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9444444444444444\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9527777777777777\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9027777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8583333333333333\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9055555555555556\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.08611111111111111\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "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",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.11666666666666667\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.1388888888888889\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09444444444444444\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "# store models for later use\n",
@@ -1927,7 +2856,36 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_0.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "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",
@@ -2017,7 +2975,16 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2259440937.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    conda create -n tf tensorflow\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "conda create -n tf tensorflow\n",
     "conda activate tf"
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
index a20b59798..991ce44fb 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
@@ -2994,19 +2994,20 @@
       "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.<locals>.nary_operator.<locals>.nary_f.<locals>.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.<locals>.nary_operator.<locals>.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:64\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m     63\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp.<locals>.vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m     20\u001b[0m     outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m     23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp.<locals>.vjp_argnums.<locals>.<lambda>\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m     66\u001b[0m     vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m     69\u001b[0m     argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:423\u001b[0m, in \u001b[0;36mmatmul_vjp_1.<locals>.<lambda>\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m    421\u001b[0m A_ndim \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mndim(A)\n\u001b[1;32m    422\u001b[0m B_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(B)\n\u001b[0;32m--> 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43mmatmul_adjoint_1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mA_ndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB_meta\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:413\u001b[0m, in \u001b[0;36mmatmul_adjoint_1\u001b[0;34m(A, G, A_ndim, B_meta)\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m B_is_vec:\n\u001b[1;32m    412\u001b[0m     result \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msqueeze(result, anp\u001b[38;5;241m.\u001b[39mndim(G) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB_meta\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:653\u001b[0m, in \u001b[0;36munbroadcast\u001b[0;34m(x, target_meta, broadcast_idx)\u001b[0m\n\u001b[1;32m    651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, size \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(target_shape):\n\u001b[1;32m    652\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 653\u001b[0m         x \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m anp\u001b[38;5;241m.\u001b[39miscomplexobj(x) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m target_iscomplex:\n\u001b[1;32m    655\u001b[0m     x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mreal(x)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m     52\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;124;03m    (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m     62\u001b[0m     jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[9], line 61\u001b[0m, in \u001b[0;36mg_trial\u001b[0;34m(point, P)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mg_trial\u001b[39m(point,P):\n\u001b[1;32m     60\u001b[0m     x,t \u001b[38;5;241m=\u001b[39m point\n\u001b[0;32m---> 61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39mt)\u001b[38;5;241m*\u001b[39mu(x) \u001b[38;5;241m+\u001b[39m x\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mx\u001b[49m)\u001b[38;5;241m*\u001b[39mt\u001b[38;5;241m*\u001b[39mdeep_neural_network(P,point)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:34\u001b[0m, in \u001b[0;36mArrayBox.__rsub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__rsub__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubtract\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive.<locals>.f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m     argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum    \u001b[38;5;28;01mfor\u001b[39;00m argnum, _   \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m     44\u001b[0m     ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m     node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m     33\u001b[0m     fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     35\u001b[0m                               \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66\u001b[0m, in \u001b[0;36mdefvjp.<locals>.vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[0;32m---> 66\u001b[0m     vjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (vjp(g),)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:297\u001b[0m, in \u001b[0;36mgrad_np_sum\u001b[0;34m(ans, x, axis, keepdims, dtype)\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: anp\u001b[38;5;241m.\u001b[39msum(g, axis\u001b[38;5;241m=\u001b[39mbroadcast_axes, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    295\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mbroadcast_to, grad_broadcast_to)\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad_np_sum\u001b[39m(ans, x, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    298\u001b[0m     shape, dtype \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mshape(x), anp\u001b[38;5;241m.\u001b[39mresult_type(x)\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: repeat_to_match_shape(g, shape, dtype, axis, keepdims)[\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:37\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(ans, x, y)\u001b[0m\n\u001b[1;32m     32\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39madd,         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m     33\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g))\n\u001b[1;32m     34\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmultiply,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: y \u001b[38;5;241m*\u001b[39m g),\n\u001b[1;32m     35\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: x \u001b[38;5;241m*\u001b[39m g))\n\u001b[1;32m     36\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msubtract,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[0;32m---> 37\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : \u001b[43munbroadcast_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     38\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mdivide,      \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g:   g \u001b[38;5;241m/\u001b[39m y),\n\u001b[1;32m     39\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m x \u001b[38;5;241m/\u001b[39m y\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m     40\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmaximum,     \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m balanced_eq(x, ans, y)),\n\u001b[1;32m     41\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m balanced_eq(y, ans, x)))\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:659\u001b[0m, in \u001b[0;36munbroadcast_f\u001b[0;34m(target, f)\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[0;32m--> 659\u001b[0m     target_meta \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    660\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: unbroadcast(f(g), target_meta)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:61\u001b[0m, in \u001b[0;36mnotrace_primitive.<locals>.f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f_raw)\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf_wrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     60\u001b[0m     argvals \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(getval, args)\n\u001b[0;32m---> 61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\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",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:148\u001b[0m, in \u001b[0;36mmetadata\u001b[0;34m(A)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@notrace_primitive\u001b[39m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmetadata\u001b[39m(A):\n\u001b[0;32m--> 148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_np\u001b[49m\u001b[38;5;241m.\u001b[39mshape(A), _np\u001b[38;5;241m.\u001b[39mndim(A), _np\u001b[38;5;241m.\u001b[39mresult_type(A), _np\u001b[38;5;241m.\u001b[39miscomplexobj(A)\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_19_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_19_1.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
index 5f543df79..a286efc9a 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": [
-      "0.04718566894028431\n",
-      "4.11080997912276\n",
-      "[[ 1.10517643  3.48455788]\n",
-      " [ 3.48455788 12.00216162]]\n"
+      "-0.014394967608286841\n",
+      "4.011594819155615\n",
+      "[[ 1.24508783  3.8595836 ]\n",
+      " [ 3.8595836  12.92663007]]\n"
      ]
     }
    ],
@@ -1845,10 +1845,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.07836997022107646\n",
-      "1.1378267322316808\n",
-      "[[1.         0.63980097]\n",
-      " [0.63980097 1.        ]]\n"
+      "0.07630326327869198\n",
+      "1.6893421391051477\n",
+      "[[1.        0.6373454]\n",
+      " [0.6373454 1.       ]]\n"
      ]
     }
    ],
@@ -1905,30 +1905,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.34931214  3.06139439]\n",
-      " [-0.44476964 -2.60794187]\n",
-      " [ 0.02225493  0.16388664]\n",
-      " [-1.91193672 -3.82324216]\n",
-      " [-0.2044881  -1.56027537]\n",
-      " [-1.15572395 -3.25982474]\n",
-      " [ 0.94217756  1.49888671]\n",
-      " [ 0.28472162  2.92474572]\n",
-      " [ 2.38943     7.14118216]\n",
-      " [-1.27097785 -3.5388115 ]]\n",
+      "[[ 0.20396326  0.93053605]\n",
+      " [ 0.51936974  2.03440431]\n",
+      " [-0.53851084 -1.2527027 ]\n",
+      " [-0.50429483  0.72966563]\n",
+      " [ 0.71314288  1.60421343]\n",
+      " [-0.29995377 -2.31356849]\n",
+      " [-0.14484831 -2.19932442]\n",
+      " [-0.00570826 -0.32643011]\n",
+      " [-0.22821607  0.56775345]\n",
+      " [ 0.28505619  0.22545286]]\n",
       "          0         1\n",
-      "0  1.349312  3.061394\n",
-      "1 -0.444770 -2.607942\n",
-      "2  0.022255  0.163887\n",
-      "3 -1.911937 -3.823242\n",
-      "4 -0.204488 -1.560275\n",
-      "5 -1.155724 -3.259825\n",
-      "6  0.942178  1.498887\n",
-      "7  0.284722  2.924746\n",
-      "8  2.389430  7.141182\n",
-      "9 -1.270978 -3.538811\n",
-      "          0         1\n",
-      "0  1.000000  0.950873\n",
-      "1  0.950873  1.000000\n"
+      "0  0.203963  0.930536\n",
+      "1  0.519370  2.034404\n",
+      "2 -0.538511 -1.252703\n",
+      "3 -0.504295  0.729666\n",
+      "4  0.713143  1.604213\n",
+      "5 -0.299954 -2.313568\n",
+      "6 -0.144848 -2.199324\n",
+      "7 -0.005708 -0.326430\n",
+      "8 -0.228216  0.567753\n",
+      "9  0.285056  0.225453\n",
+      "        0       1\n",
+      "0  1.0000  0.6373\n",
+      "1  0.6373  1.0000\n"
      ]
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    ],
@@ -1974,37 +1974,44 @@
      "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  0.074334  0.080585  0.077061  0.078751  0.080220  0.070657  0.071406   \n",
-      "2   0.0  0.080585  0.088425  0.082009  0.084289  0.086338  0.074009  0.075052   \n",
-      "3   0.0  0.077061  0.082009  0.085147  0.086339  0.087297  0.081324  0.081796   \n",
-      "4   0.0  0.078751  0.084289  0.086339  0.087789  0.089007  0.081926  0.082537   \n",
-      "5   0.0  0.080220  0.086338  0.087297  0.089007  0.090492  0.082307  0.083061   \n",
-      "6   0.0  0.070657  0.074009  0.081324  0.081926  0.082307  0.079874  0.080032   \n",
-      "7   0.0  0.071406  0.075052  0.081796  0.082537  0.083061  0.080032  0.080271   \n",
-      "8   0.0  0.072148  0.076101  0.082240  0.083128  0.083801  0.080150  0.080474   \n",
-      "9   0.0  0.072902  0.077180  0.082670  0.083714  0.084548  0.080237  0.080651   \n",
-      "10  0.0  0.063646  0.065859  0.075320  0.075498  0.075472  0.075478  0.075409   \n",
-      "11  0.0  0.064071  0.066452  0.075576  0.075838  0.075897  0.075542  0.075525   \n",
-      "12  0.0  0.064514  0.067074  0.075838  0.076189  0.076340  0.075602  0.075639   \n",
-      "13  0.0  0.064980  0.067731  0.076108  0.076555  0.076803  0.075658  0.075753   \n",
-      "14  0.0  0.065472  0.068429  0.076389  0.076938  0.077292  0.075711  0.075868   \n",
+      "1   0.0  0.072835  0.076616  0.071253  0.074193  0.077337  0.063283  0.065305   \n",
+      "2   0.0  0.076616  0.082149  0.073794  0.077643  0.081811  0.064373  0.066929   \n",
+      "3   0.0  0.071253  0.073794  0.075423  0.077534  0.079745  0.070414  0.071978   \n",
+      "4   0.0  0.074193  0.077643  0.077534  0.080199  0.083034  0.071575  0.073483   \n",
+      "5   0.0  0.077337  0.081811  0.079745  0.083034  0.086576  0.072733  0.075028   \n",
+      "6   0.0  0.063283  0.064373  0.070414  0.071575  0.072733  0.068008  0.068984   \n",
+      "7   0.0  0.065305  0.066929  0.071978  0.073483  0.075028  0.068984  0.070181   \n",
+      "8   0.0  0.067523  0.069754  0.073667  0.075563  0.077550  0.070010  0.071459   \n",
+      "9   0.0  0.069964  0.072888  0.075496  0.077839  0.080330  0.071092  0.072826   \n",
+      "10  0.0  0.055866  0.055925  0.064324  0.064790  0.065183  0.063660  0.064182   \n",
+      "11  0.0  0.057298  0.057682  0.065508  0.066192  0.066830  0.064471  0.065139   \n",
+      "12  0.0  0.058866  0.059624  0.066786  0.067719  0.068637  0.065330  0.066162   \n",
+      "13  0.0  0.060590  0.061775  0.068171  0.069388  0.070625  0.066241  0.067260   \n",
+      "14  0.0  0.062489  0.064164  0.069675  0.071217  0.072820  0.067209  0.068440   \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   0.072148  0.072902  0.063646  0.064071  0.064514  0.064980  0.065472  \n",
-      "2   0.076101  0.077180  0.065859  0.066452  0.067074  0.067731  0.068429  \n",
-      "3   0.082240  0.082670  0.075320  0.075576  0.075838  0.076108  0.076389  \n",
-      "4   0.083128  0.083714  0.075498  0.075838  0.076189  0.076555  0.076938  \n",
-      "5   0.083801  0.084548  0.075472  0.075897  0.076340  0.076803  0.077292  \n",
-      "6   0.080150  0.080237  0.075478  0.075542  0.075602  0.075658  0.075711  \n",
-      "7   0.080474  0.080651  0.075409  0.075525  0.075639  0.075753  0.075868  \n",
-      "8   0.080766  0.081038  0.075293  0.075463  0.075634  0.075809  0.075988  \n",
-      "9   0.081038  0.081411  0.075136  0.075363  0.075595  0.075834  0.076082  \n",
-      "10  0.075293  0.075136  0.072406  0.072329  0.072240  0.072140  0.072028  \n",
-      "11  0.075463  0.075363  0.072329  0.072286  0.072234  0.072173  0.072101  \n",
-      "12  0.075634  0.075595  0.072240  0.072234  0.072220  0.072199  0.072171  \n",
-      "13  0.075809  0.075834  0.072140  0.072173  0.072199  0.072221  0.072238  \n",
-      "14  0.075988  0.076082  0.072028  0.072101  0.072171  0.072238  0.072303  \n"
+      "1   0.067523  0.069964  0.055866  0.057298  0.058866  0.060590  0.062489  \n",
+      "2   0.069754  0.072888  0.055925  0.057682  0.059624  0.061775  0.064164  \n",
+      "3   0.073667  0.075496  0.064324  0.065508  0.066786  0.068171  0.069675  \n",
+      "4   0.075563  0.077839  0.064790  0.066192  0.067719  0.069388  0.071217  \n",
+      "5   0.077550  0.080330  0.065183  0.066830  0.068637  0.070625  0.072820  \n",
+      "6   0.070010  0.071092  0.063660  0.064471  0.065330  0.066241  0.067209  \n",
+      "7   0.071459  0.072826  0.064182  0.065139  0.066162  0.067260  0.068440  \n",
+      "8   0.073021  0.074713  0.064703  0.065823  0.067032  0.068341  0.069761  \n",
+      "9   0.074713  0.076776  0.065219  0.066524  0.067943  0.069492  0.071186  \n",
+      "10  0.064703  0.065219  0.060678  0.061187  0.061707  0.062241  0.062786  \n",
+      "11  0.065823  0.066524  0.061187  0.061793  0.062424  0.063082  0.063768  \n",
+      "12  0.067032  0.067943  0.061707  0.062424  0.063180  0.063978  0.064823  \n",
+      "13  0.068341  0.069492  0.062241  0.063082  0.063978  0.064935  0.065960  \n",
+      "14  0.069761  0.071186  0.062786  0.063768  0.064823  0.065960  0.067192  "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
@@ -3557,14 +3564,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2. 2.]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
+      "[2. 2.]\n",
       "Training MSE for OLS\n",
       "3.0\n"
      ]
@@ -3578,7 +3578,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter2_252_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter2_252_1.png"
       }
      },
      "output_type": "display_data"
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index 5520c1cf4..e9cfe0aa9 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: 0.154751 sec\n",
+      "Runtime: 0.136066 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      " 99.9896        99.9796        0.149524\n"
+      " 99.8762        99.8662        0.150735\n"
      ]
     }
    ],
@@ -917,7 +917,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.307  14.9693        100.309        0.149416\n"
+      " 99.9623  14.9594        99.9606         0.14934\n"
      ]
     }
    ],
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1277,13 +1277,7 @@
       "Error: 0.08426840630693411\n",
       "Bias^2: 0.0796891867672603\n",
       "Var: 0.004579219539673834\n",
-      "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n",
       "Polynomial degree: 2\n",
       "Error: 0.10398646080125035\n",
       "Bias^2: 0.1007711427354898\n",
@@ -1314,13 +1308,7 @@
       "Error: 0.037813671417389005\n",
       "Bias^2: 0.033657685071527665\n",
       "Var: 0.00415598634586135\n",
-      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n",
       "Polynomial degree: 7\n",
       "Error: 0.02760977349102253\n",
       "Bias^2: 0.022999498260366312\n",
@@ -1351,13 +1339,7 @@
       "Error: 0.07160048164233104\n",
       "Bias^2: 0.014436800088904942\n",
       "Var: 0.05716368155342608\n",
-      "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n",
       "Polynomial degree: 12\n",
       "Error: 0.11547777218872497\n",
       "Bias^2: 0.01628578269596628\n",
@@ -1379,7 +1361,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_6.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_3.png"
       }
      },
      "output_type": "display_data"
@@ -1706,16 +1688,16 @@
       "Mean squared error on test data: 877.21517262\n",
       "Degree of polynomial:  23\n",
       "Mean squared error on training data: 0.00085892\n",
-      "Mean squared error on test data: 5567.04664255\n"
+      "Mean squared error on test data: 5567.04664255\n",
+      "Degree of polynomial:  24\n",
+      "Mean squared error on training data: 0.00084707\n",
+      "Mean squared error on test data: 1325.26124692\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  24\n",
-      "Mean squared error on training data: 0.00084707\n",
-      "Mean squared error on test data: 1325.26124692\n",
       "Degree of polynomial:  25\n",
       "Mean squared error on training data: 0.00079125\n",
       "Mean squared error on test data: 129012.83870189\n",
@@ -1724,16 +1706,16 @@
       "Mean squared error on test data: 18388.59354079\n",
       "Degree of polynomial:  27\n",
       "Mean squared error on training data: 0.00069123\n",
-      "Mean squared error on test data: 2351.97979891\n"
+      "Mean squared error on test data: 2351.97979891\n",
+      "Degree of polynomial:  28\n",
+      "Mean squared error on training data: 0.00062592\n",
+      "Mean squared error on test data: 3983.63037846\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  28\n",
-      "Mean squared error on training data: 0.00062592\n",
-      "Mean squared error on test data: 3983.63037846\n",
       "Degree of polynomial:  29\n",
       "Mean squared error on training data: 0.00060704\n",
       "Mean squared error on test data: 3262.26814548\n"
@@ -1743,9 +1725,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/626635268.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_57183/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2087,7 +2069,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
@@ -3761,7 +3743,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4071,7 +4053,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4148,7 +4130,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4233,7 +4215,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4294,7 +4276,7 @@
      "output_type": "stream",
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+      "  0%|                                                                                                                   | 0/10 [00:00<?, ?it/s]"
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     {
@@ -4304,7 +4286,7 @@
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00\n",
       "  model = cd_fast.enet_coordinate_descent(\n",
       "\r",
-      " 10%|█████████████▍                                                                                                                        | 1/10 [00:00<00:07,  1.14it/s]"
+      " 10%|██████████▋                                                                                                | 1/10 [00:00<00:07,  1.23it/s]"
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+      " 20%|█████████████████████▍                                                                                     | 2/10 [00:01<00:06,  1.15it/s]"
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-      " 40%|█████████████████████████████████████████████████████▌                                                                                | 4/10 [00:03<00:04,  1.39it/s]"
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-      " 60%|████████████████████████████████████████████████████████████████████████████████▍                                                     | 6/10 [00:04<00:02,  1.44it/s]"
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       "\r",
-      " 70%|█████████████████████████████████████████████████████████████████████████████████████████████▊                                        | 7/10 [00:05<00:02,  1.45it/s]"
+      " 70%|██████████████████████████████████████████████████████████████████████████▉                                | 7/10 [00:04<00:01,  1.64it/s]"
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-      " 80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▏                          | 8/10 [00:05<00:01,  1.55it/s]"
+      " 80%|█████████████████████████████████████████████████████████████████████████████████████▌                     | 8/10 [00:05<00:01,  1.64it/s]"
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-      " 90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌             | 9/10 [00:06<00:00,  1.54it/s]"
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+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00,  1.54it/s]"
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png
index ae6e2dab3..1aec97a82 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png and b/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
index 1f64a36af..f9e272e40 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
@@ -86,14 +86,14 @@
      "output_type": "stream",
      "text": [
       "2nd degree coefficients:\n",
-      "zero power:  -0.22158725223474995\n",
-      "first power:  0.24121476598003452\n",
-      "second power:  -0.0009532583857747255\n"
+      "zero power:  4.58641507967355\n",
+      "first power:  -0.2107546970521115\n",
+      "second power:  0.0003919793592273513\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -107,7 +107,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1330,15 +1330,22 @@
      "output_type": "stream",
      "text": [
       "(426, 30)\n",
-      "(143, 30)\n"
+      "(143, 30)\n",
+      "Test set accuracy with Logistic Regression: 0.94\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test set accuracy with Logistic Regression: 0.94\n",
-      "Test set accuracy with SVM: 0.63\n",
+      "Test set accuracy with SVM: 0.63"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       "Test set accuracy with Decision Trees: 0.90\n",
       "Test set accuracy Logistic Regression with scaled data: 0.96\n",
       "Test set accuracy SVM with scaled data: 0.96\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png
index 4154c917d..bc967a3c0 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 d3ad5e54e..76f059ffd 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 fb312e3e3..970bd51e1 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": [
-      "0.1001408041761458\n",
-      "4.2807716628772665\n",
-      "[[ 1.15654145  3.54867722]\n",
-      " [ 3.54867722 11.70485195]]\n"
+      "0.05665875086534638\n",
+      "4.128393685824704\n",
+      "[[ 0.93987367  2.98650457]\n",
+      " [ 2.98650457 10.47544463]]\n"
      ]
     }
    ],
@@ -340,10 +340,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.09543871010617433\n",
-      "1.6888043337746685\n",
-      "[[1.        0.7167077]\n",
-      " [0.7167077 1.       ]]\n"
+      "0.07617734331359052\n",
+      "1.6957182489166325\n",
+      "[[1.         0.68029423]\n",
+      " [0.68029423 1.        ]]\n"
      ]
     }
    ],
@@ -397,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.20575734  0.01384583]\n",
-      " [-0.89876098 -3.04065686]\n",
-      " [-0.76289128 -3.17080691]\n",
-      " [-0.0334136   0.16124569]\n",
-      " [ 2.73970542  9.28885103]\n",
-      " [ 0.75413023  2.98474769]\n",
-      " [-1.87894459 -5.48121459]\n",
-      " [-1.26814205 -2.4848097 ]\n",
-      " [ 0.18114057 -0.9889962 ]\n",
-      " [ 1.37293361  2.71779401]]\n",
+      "[[-0.29087396  0.13244119]\n",
+      " [-1.21617146 -2.69678073]\n",
+      " [-1.37024276 -3.76728511]\n",
+      " [ 0.49342785  1.50638863]\n",
+      " [ 0.4155974  -0.07435812]\n",
+      " [ 0.64813145  1.94455739]\n",
+      " [-0.48364163 -2.62178739]\n",
+      " [-0.3807176  -1.25007671]\n",
+      " [ 1.73036439  5.00709577]\n",
+      " [ 0.45412633  1.81980509]]\n",
       "          0         1\n",
-      "0 -0.205757  0.013846\n",
-      "1 -0.898761 -3.040657\n",
-      "2 -0.762891 -3.170807\n",
-      "3 -0.033414  0.161246\n",
-      "4  2.739705  9.288851\n",
-      "5  0.754130  2.984748\n",
-      "6 -1.878945 -5.481215\n",
-      "7 -1.268142 -2.484810\n",
-      "8  0.181141 -0.988996\n",
-      "9  1.372934  2.717794\n",
+      "0 -0.290874  0.132441\n",
+      "1 -1.216171 -2.696781\n",
+      "2 -1.370243 -3.767285\n",
+      "3  0.493428  1.506389\n",
+      "4  0.415597 -0.074358\n",
+      "5  0.648131  1.944557\n",
+      "6 -0.483642 -2.621787\n",
+      "7 -0.380718 -1.250077\n",
+      "8  1.730364  5.007096\n",
+      "9  0.454126  1.819805\n",
       "          0         1\n",
-      "0  1.000000  0.970965\n",
-      "1  0.970965  1.000000\n"
+      "0  1.000000  0.961042\n",
+      "1  0.961042  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  0.090565  0.089065  0.093226  0.091674  0.090154  0.086287  0.084847   \n",
-      "2   0.0  0.089065  0.087946  0.092421  0.091068  0.089735  0.085988  0.084674   \n",
-      "3   0.0  0.093226  0.092421  0.102147  0.100816  0.099494  0.098144  0.096731   \n",
-      "4   0.0  0.091674  0.091068  0.100816  0.099622  0.098431  0.097115  0.095803   \n",
-      "5   0.0  0.090154  0.089735  0.099494  0.098431  0.097365  0.096077  0.094862   \n",
-      "6   0.0  0.086287  0.085988  0.098144  0.097115  0.096077  0.096630  0.095395   \n",
-      "7   0.0  0.084847  0.084674  0.096731  0.095803  0.094862  0.095395  0.094243   \n",
-      "8   0.0  0.083459  0.083405  0.095358  0.094527  0.093680  0.094189  0.093115   \n",
-      "9   0.0  0.082121  0.082180  0.094027  0.093288  0.092530  0.093011  0.092013   \n",
-      "10  0.0  0.078708  0.078711  0.091732  0.090935  0.090118  0.091871  0.090804   \n",
-      "11  0.0  0.077431  0.077523  0.090387  0.089668  0.088926  0.090626  0.089626   \n",
-      "12  0.0  0.076203  0.076378  0.089086  0.088441  0.087772  0.089417  0.088481   \n",
-      "13  0.0  0.075021  0.075274  0.087828  0.087255  0.086655  0.088242  0.087369   \n",
-      "14  0.0  0.073883  0.074212  0.086611  0.086107  0.085573  0.087102  0.086289   \n",
+      "1   0.0  0.068131  0.073423  0.069509  0.072262  0.074935  0.062568  0.064540   \n",
+      "2   0.0  0.073423  0.079638  0.075544  0.078741  0.081836  0.068222  0.070480   \n",
+      "3   0.0  0.069509  0.075544  0.075689  0.079013  0.082264  0.070991  0.073416   \n",
+      "4   0.0  0.072262  0.078741  0.079013  0.082596  0.086101  0.074265  0.076876   \n",
+      "5   0.0  0.074935  0.081836  0.082264  0.086101  0.089859  0.077488  0.080287   \n",
+      "6   0.0  0.062568  0.068222  0.070991  0.074265  0.077488  0.068479  0.070924   \n",
+      "7   0.0  0.064540  0.070480  0.073416  0.076876  0.080287  0.070924  0.073514   \n",
+      "8   0.0  0.066545  0.072773  0.075883  0.079535  0.083138  0.073418  0.076157   \n",
+      "9   0.0  0.068597  0.075114  0.078409  0.082256  0.086058  0.075974  0.078867   \n",
+      "10  0.0  0.055105  0.060160  0.064322  0.067367  0.070380  0.063327  0.065650   \n",
+      "11  0.0  0.056730  0.062004  0.066330  0.069526  0.072694  0.065375  0.067819   \n",
+      "12  0.0  0.058409  0.063910  0.068406  0.071759  0.075086  0.067492  0.070063   \n",
+      "13  0.0  0.060149  0.065884  0.070556  0.074073  0.077566  0.069686  0.072388   \n",
+      "14  0.0  0.061956  0.067932  0.072787  0.076473  0.080139  0.071962  0.074802   \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   0.083459  0.082121  0.078708  0.077431  0.076203  0.075021  0.073883  \n",
-      "2   0.083405  0.082180  0.078711  0.077523  0.076378  0.075274  0.074212  \n",
-      "3   0.095358  0.094027  0.091732  0.090387  0.089086  0.087828  0.086611  \n",
-      "4   0.094527  0.093288  0.090935  0.089668  0.088441  0.087255  0.086107  \n",
-      "5   0.093680  0.092530  0.090118  0.088926  0.087772  0.086655  0.085573  \n",
-      "6   0.094189  0.093011  0.091871  0.090626  0.089417  0.088242  0.087102  \n",
-      "7   0.093115  0.092013  0.090804  0.089626  0.088481  0.087369  0.086289  \n",
-      "8   0.092064  0.091034  0.089755  0.088642  0.087560  0.086508  0.085486  \n",
-      "9   0.091034  0.090075  0.088726  0.087675  0.086653  0.085659  0.084694  \n",
-      "10  0.089755  0.088726  0.088455  0.087327  0.086227  0.085155  0.084112  \n",
-      "11  0.088642  0.087675  0.087327  0.086256  0.085212  0.084195  0.083203  \n",
-      "12  0.087560  0.086653  0.086227  0.085212  0.084222  0.083257  0.082316  \n",
-      "13  0.086508  0.085659  0.085155  0.084195  0.083257  0.082342  0.081450  \n",
-      "14  0.085486  0.084694  0.084112  0.083203  0.082316  0.081450  0.080605  \n"
+      "1   0.066545  0.068597  0.055105  0.056730  0.058409  0.060149  0.061956  \n",
+      "2   0.072773  0.075114  0.060160  0.062004  0.063910  0.065884  0.067932  \n",
+      "3   0.075883  0.078409  0.064322  0.066330  0.068406  0.070556  0.072787  \n",
+      "4   0.079535  0.082256  0.067367  0.069526  0.071759  0.074073  0.076473  \n",
+      "5   0.083138  0.086058  0.070380  0.072694  0.075086  0.077566  0.080139  \n",
+      "6   0.073418  0.075974  0.063327  0.065375  0.067492  0.069686  0.071962  \n",
+      "7   0.076157  0.078867  0.065650  0.067819  0.070063  0.072388  0.074802  \n",
+      "8   0.078953  0.081823  0.068024  0.070318  0.072693  0.075155  0.077711  \n",
+      "9   0.081823  0.084858  0.070460  0.072885  0.075396  0.078000  0.080704  \n",
+      "10  0.068024  0.070460  0.059480  0.061447  0.063482  0.065591  0.067780  \n",
+      "11  0.070318  0.072885  0.061447  0.063517  0.065661  0.067883  0.070190  \n",
+      "12  0.072693  0.075396  0.063482  0.065661  0.067917  0.070258  0.072688  \n",
+      "13  0.075155  0.078000  0.065591  0.067883  0.070258  0.072721  0.075281  \n",
+      "14  0.077711  0.080704  0.067780  0.070190  0.072688  0.075281  0.077975  \n"
      ]
     }
    ],
@@ -916,17 +916,10 @@
      "output_type": "stream",
      "text": [
       "          0         1\n",
-      "0  4.114499  2.071143\n",
-      "1  2.071143  2.061388"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[[4.11449851 2.07114326]\n",
-      " [2.07114326 2.0613875 ]]\n"
+      "0  4.060824  2.038890\n",
+      "1  2.038890  2.029664\n",
+      "[[4.06082419 2.03888998]\n",
+      " [2.03888998 2.02966421]]\n"
      ]
     }
    ],
@@ -956,13 +949,13 @@
      "output_type": "stream",
      "text": [
       "Centered covariance using own code\n",
-      "[[4.11449851 2.07114326]\n",
-      " [2.07114326 2.0613875 ]]\n"
+      "[[4.06082419 2.03888998]\n",
+      " [2.03888998 2.02966421]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1051,12 +1044,12 @@
      "output_type": "stream",
      "text": [
       "Eigenvalues of Covariance matrix\n",
-      "5.399533503407795\n",
-      "0.776352510835556\n",
+      "5.323066643161066\n",
+      "0.7674217625964539\n",
       "First eigenvector\n",
-      "[0.84973247 0.52721412]\n",
+      "[0.85025162 0.52637646]\n",
       "Second eigenvector\n",
-      "[-0.52721412  0.84973247]\n"
+      "[-0.52637646  0.85025162]\n"
      ]
     },
     {
@@ -1064,7 +1057,7 @@
      "output_type": "stream",
      "text": [
       "Eigenvector of largest eigenvalue\n",
-      "[-0.84973247 -0.52721412]\n"
+      "[-0.85025162 -0.52637646]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png
index ec7c0c4d0..87b3e0255 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/exercisesweek41.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
index 0e1d2dc6e..586f28218 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
@@ -103,20 +103,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.1729993]\n",
-      " [3.0170097]]\n",
-      "Eigenvalues of Hessian Matrix:[0.28192769 4.68753434]\n",
+      "[[3.99556258]\n",
+      " [2.99418621]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29411652 4.64408368]\n",
       "theta from own gd\n",
-      "[[4.1729993]\n",
-      " [3.0170097]]\n",
+      "[[3.99556258]\n",
+      " [2.99418621]]\n",
       "theta from own sdg\n",
-      "[[4.14043884]\n",
-      " [2.99071523]]\n"
+      "[[4.03364384]\n",
+      " [2.99870462]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -338,9 +338,9 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.03696458]\n",
-      " [3.0324793 ]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30959659 4.4150026 ]\n"
+      "[[3.94033034]\n",
+      " [3.07147868]]\n",
+      "Eigenvalues of Hessian Matrix:[0.26240613 4.76140219]\n"
      ]
     },
     {
@@ -348,13 +348,13 @@
      "output_type": "stream",
      "text": [
       "theta from own gd\n",
-      "[[4.03696458]\n",
-      " [3.0324793 ]]\n"
+      "[[3.94033034]\n",
+      " [3.07147868]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -440,73 +440,73 @@
       "Own inversion\n",
       "[[4.]\n",
       " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.23469347 4.90407685]\n",
-      "0 [-18.02987043] [-22.42501752]\n",
-      "1 [-0.32329897] [0.25206371]\n",
-      "2 [-0.30782691] [0.24000075]\n",
-      "3 [-0.2930953] [0.22851508]\n",
-      "4 [-0.27906869] [0.21757908]\n",
-      "5 [-0.26571335] [0.20716644]\n",
-      "6 [-0.25299716] [0.19725211]\n",
-      "7 [-0.24088952] [0.18781225]\n",
-      "8 [-0.22936132] [0.17882416]\n",
-      "9 [-0.21838482] [0.17026621]\n",
-      "10 [-0.20793362] [0.16211781]\n",
-      "11 [-0.19798258] [0.15435937]\n",
-      "12 [-0.18850776] [0.14697222]\n",
-      "13 [-0.17948638] [0.1399386]\n",
-      "14 [-0.17089674] [0.13324158]\n",
-      "15 [-0.16271817] [0.12686507]\n",
-      "16 [-0.15493099] [0.12079371]\n",
-      "17 [-0.14751649] [0.11501291]\n",
-      "18 [-0.14045682] [0.10950876]\n",
-      "19 [-0.13373501] [0.10426802]\n",
-      "20 [-0.12733488] [0.09927808]\n",
-      "21 [-0.12124104] [0.09452695]\n",
-      "22 [-0.11543883] [0.09000319]\n",
-      "23 [-0.10991429] [0.08569593]\n",
-      "24 [-0.10465415] [0.08159479]\n",
-      "25 [-0.09964573] [0.07768993]\n",
-      "26 [-0.094877] [0.07397193]\n",
-      "27 [-0.09033649] [0.07043187]\n",
-      "28 [-0.08601328] [0.06706123]\n",
-      "29 [-0.08189696] [0.06385189]\n",
+      "Eigenvalues of Hessian Matrix:[0.30332201 4.25820894]\n",
+      "0 [-14.184119] [-15.76632434]\n",
+      "1 [-0.27465664] [0.23807169]\n",
+      "2 [-0.25509222] [0.2211133]\n",
+      "3 [-0.23692142] [0.20536289]\n",
+      "4 [-0.22004496] [0.19073442]\n",
+      "5 [-0.20437065] [0.17714797]\n",
+      "6 [-0.18981286] [0.16452931]\n",
+      "7 [-0.17629205] [0.15280951]\n",
+      "8 [-0.16373437] [0.14192454]\n",
+      "9 [-0.15207119] [0.13181493]\n",
+      "10 [-0.14123881] [0.12242545]\n",
+      "11 [-0.13117805] [0.1137048]\n",
+      "12 [-0.12183393] [0.10560535]\n",
+      "13 [-0.11315542] [0.09808284]\n",
+      "14 [-0.1050951] [0.09109617]\n",
+      "15 [-0.09760893] [0.08460718]\n",
+      "16 [-0.09065603] [0.07858042]\n",
+      "17 [-0.08419839] [0.07298295]\n",
+      "18 [-0.07820074] [0.06778421]\n",
+      "19 [-0.07263033] [0.06295579]\n",
+      "20 [-0.0674567] [0.0584713]\n",
+      "21 [-0.06265161] [0.05430625]\n",
+      "22 [-0.05818879] [0.0504379]\n",
+      "23 [-0.05404387] [0.04684509]\n",
+      "24 [-0.0501942] [0.04350821]\n",
+      "25 [-0.04661875] [0.04040902]\n",
+      "26 [-0.04329799] [0.03753059]\n",
+      "27 [-0.04021377] [0.0348572]\n",
+      "28 [-0.03734925] [0.03237424]\n",
+      "29 [-0.03468878] [0.03006815]\n",
       "theta from own gd\n",
-      "[[3.66774691]\n",
-      " [3.25904489]]\n",
-      "0 [-0.07797763] [0.06079614]\n",
-      "1 [-0.07424587] [0.05788664]\n",
-      "2 [-0.06957317] [0.05424351]\n",
-      "3 [-0.06484181] [0.05055465]\n",
-      "4 [-0.06031928] [0.04702861]\n",
-      "5 [-0.05607583] [0.04372016]\n",
-      "6 [-0.05211919] [0.04063532]\n",
-      "7 [-0.04843794] [0.03776519]\n",
-      "8 [-0.04501548] [0.03509683]\n",
-      "9 [-0.04183444] [0.0326167]\n",
-      "10 [-0.03887807] [0.03031173]\n",
-      "11 [-0.03613058] [0.02816961]\n",
-      "12 [-0.03357723] [0.02617887]\n",
-      "13 [-0.03120433] [0.02432881]\n",
-      "14 [-0.02899912] [0.02260949]\n",
-      "15 [-0.02694975] [0.02101168]\n",
-      "16 [-0.02504521] [0.01952679]\n",
-      "17 [-0.02327527] [0.01814683]\n",
-      "18 [-0.0216304] [0.01686439]\n",
-      "19 [-0.02010178] [0.01567258]\n",
-      "20 [-0.01868119] [0.014565]\n",
-      "21 [-0.01736099] [0.01353569]\n",
-      "22 [-0.01613409] [0.01257912]\n",
-      "23 [-0.01499389] [0.01169016]\n",
-      "24 [-0.01393427] [0.01086401]\n",
-      "25 [-0.01294954] [0.01009625]\n",
-      "26 [-0.01203439] [0.00938275]\n",
-      "27 [-0.01118392] [0.00871967]\n",
-      "28 [-0.01039355] [0.00810345]\n",
-      "29 [-0.00965904] [0.00753078]\n",
+      "[[3.89378344]\n",
+      " [3.09206825]]\n",
+      "0 [-0.03221782] [0.02792633]\n",
+      "1 [-0.02992287] [0.02593707]\n",
+      "2 [-0.02710291] [0.02349274]\n",
+      "3 [-0.02432632] [0.02108599]\n",
+      "4 [-0.02176052] [0.01886197]\n",
+      "5 [-0.01944073] [0.01685118]\n",
+      "6 [-0.01735999] [0.01504759]\n",
+      "7 [-0.01549917] [0.01343464]\n",
+      "8 [-0.01383689] [0.01199378]\n",
+      "9 [-0.01235257] [0.01070717]\n",
+      "10 [-0.01102737] [0.0095585]\n",
+      "11 [-0.0098443] [0.00853302]\n",
+      "12 [-0.00878815] [0.00761755]\n",
+      "13 [-0.00784531] [0.00680029]\n",
+      "14 [-0.00700361] [0.00607072]\n",
+      "15 [-0.00625222] [0.00541941]\n",
+      "16 [-0.00558145] [0.00483798]\n",
+      "17 [-0.00498263] [0.00431893]\n",
+      "18 [-0.00444806] [0.00385557]\n",
+      "19 [-0.00397085] [0.00344192]\n",
+      "20 [-0.00354483] [0.00307265]\n",
+      "21 [-0.00316452] [0.002743]\n",
+      "22 [-0.00282501] [0.00244871]\n",
+      "23 [-0.00252192] [0.002186]\n",
+      "24 [-0.00225136] [0.00195147]\n",
+      "25 [-0.00200982] [0.0017421]\n",
+      "26 [-0.00179419] [0.0015552]\n",
+      "27 [-0.0016017] [0.00138835]\n",
+      "28 [-0.00142986] [0.0012394]\n",
+      "29 [-0.00127645] [0.00110643]\n",
       "theta from own gd wth momentum\n",
-      "[[3.96175251]\n",
-      " [3.02982009]]\n"
+      "[[3.99624324]\n",
+      " [3.00325635]]\n"
      ]
     }
    ],
@@ -585,17 +585,17 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.15451852]\n",
-      " [2.83230774]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30616802 4.24299211]\n",
-      "0 [-10.57502449] [-11.57610367]\n",
-      "1 [-4.47905601e-15] [-4.07372439e-16]\n",
-      "2 [-6.9388939e-16] [-7.21432413e-16]\n",
-      "3 [-6.9388939e-16] [-7.21432413e-16]\n",
-      "4 [-6.9388939e-16] [-7.21432413e-16]\n",
+      "[[3.86346614]\n",
+      " [3.11457699]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29561686 4.57324367]\n",
+      "0 [-14.07510754] [-17.31973052]\n",
+      "1 [1.77982629e-15] [1.66047885e-15]\n",
+      "2 [1.66533454e-16] [2.95162248e-16]\n",
+      "3 [1.66533454e-16] [2.95162248e-16]\n",
+      "4 [1.66533454e-16] [2.95162248e-16]\n",
       "beta from own Newton code\n",
-      "[[4.15451852]\n",
-      " [2.83230774]]\n"
+      "[[3.86346614]\n",
+      " [3.11457699]]\n"
      ]
     }
    ],
@@ -660,30 +660,24 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.04601419]\n",
-      " [3.12204312]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33604208 4.45709724]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "[[4.08540984]\n",
+      " [2.92466997]]\n",
+      "Eigenvalues of Hessian Matrix:[0.32128064 4.10407019]\n",
       "theta from own gd\n",
-      "[[4.04601419]\n",
-      " [3.12204312]]\n"
+      "[[4.08540984]\n",
+      " [2.92466997]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png"
       }
      },
      "output_type": "display_data"
@@ -693,8 +687,8 @@
      "output_type": "stream",
      "text": [
       "theta from own sdg\n",
-      "[[4.02781444]\n",
-      " [3.13976073]]\n"
+      "[[4.02171224]\n",
+      " [2.94008244]]\n"
      ]
     }
    ],
@@ -793,15 +787,21 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.96417888]\n",
-      " [3.06634473]]\n",
-      "Eigenvalues of Hessian Matrix:[0.32962444 4.18715465]\n",
+      "[[3.72066289]\n",
+      " [3.08799729]]\n",
+      "Eigenvalues of Hessian Matrix:[0.28726748 4.35313779]\n",
       "theta from own gd\n",
-      "[[3.9639885]\n",
-      " [3.0665111]]\n",
+      "[[3.72055288]\n",
+      " [3.08809115]]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "theta from own sdg with momentum\n",
-      "[[4.00842216]\n",
-      " [3.14285244]]\n"
+      "[[3.72183526]\n",
+      " [3.12143852]]\n"
      ]
     }
    ],
@@ -918,9 +918,9 @@
      "output_type": "stream",
      "text": [
       "theta from own AdaGrad\n",
-      "[[1.99969895]\n",
-      " [3.00167058]\n",
-      " [3.99835872]]\n"
+      "[[2.00001365]\n",
+      " [2.99991971]\n",
+      " [4.00007964]]\n"
      ]
     }
    ],
@@ -1026,9 +1026,9 @@
      "output_type": "stream",
      "text": [
       "theta from own RMSprop\n",
-      "[[1.99852187]\n",
-      " [3.03868311]\n",
-      " [3.95744254]]\n"
+      "[[2.00264795]\n",
+      " [3.00057362]\n",
+      " [3.99959681]]\n"
      ]
     }
    ],
@@ -1132,9 +1132,9 @@
      "output_type": "stream",
      "text": [
       "theta from own ADAM\n",
-      "[[1.99996471]\n",
-      " [3.00026784]\n",
-      " [3.99973141]]\n"
+      "[[1.99992477]\n",
+      " [3.00056337]\n",
+      " [3.99950283]]\n"
      ]
     }
    ],
@@ -1262,7 +1262,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11892e7f0>]"
+       "[<matplotlib.lines.Line2D at 0x1522ef130>]"
       ]
      },
      "execution_count": 11,
@@ -1338,7 +1338,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x118995f70>"
+       "<matplotlib.collections.PathCollection at 0x11c3bd700>"
       ]
      },
      "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
index 6f7fb7a86..a77ce551d 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png
index 7f7f05923..36944eb1d 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 8579745d2..a7adc73f7 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
index 98aa12925..beb1908fb 100644
--- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
@@ -225,8 +225,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.34851141  0.11232457 -0.59560497  0.79558423 -0.74632311  0.97068015\n",
-      "  1.45921927  0.39225935 -0.87171492  0.44129766]\n"
+      "[-0.03609183  1.12393064 -0.46049816  0.86567539  2.03530044  0.18872206\n",
+      "  0.67747547 -1.387672    0.30136959 -0.51164347]\n"
      ]
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    ],
@@ -662,36 +662,26 @@
      "name": "stdout",
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-      "  4.21786054e-01 4.13799037e-02]]\n"
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+      "  0.50861923 0.79443618 0.17728993 0.29414225]]\n"
      ]
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    ],
@@ -810,13 +800,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.11406297261341168\n",
-      "4.669908227073521\n",
-      "0.2806901924685401\n",
-      "[[ 0.98292239  2.97664837  2.73565966]\n",
-      " [ 2.97664837 10.21905519  8.21690241]\n",
-      " [ 2.73565966  8.21690241 13.03354844]]\n",
-      "[20.77597019  0.09165112  3.36790471]\n"
+      "0.0684990216069429\n",
+      "4.3391965479837245\n",
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+      "[[0.81091776 2.49361065 1.78904605]\n",
+      " [2.49361065 8.68609856 5.49742477]\n",
+      " [1.78904605 5.49742477 6.25106936]]\n",
+      "[13.8211787   0.07429213  1.85261484]\n"
      ]
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    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index 2c9f3c5ce..eecd21d66 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": [
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-      " [ 7.54804438  5.70334935  4.01386236  3.14002608  8.3440539   9.32320616\n",
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-      " [ 5.31210861  4.01386236  2.8248473   2.20986506  5.87231849  6.56141925\n",
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-      "   2.22377412  6.02604362  2.86070736  3.02031789]\n",
-      " [11.04286013  8.3440539   5.87231849  4.59388777 12.20742956 13.63993855\n",
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-      " [ 5.34554342  4.0391259   2.8426271   2.22377412  5.9092793   6.60271731\n",
-      "   2.8605188   7.7515117   3.67982841  3.88514106]\n",
-      " [14.48549903 10.94533329  7.70302827  6.02604362 16.01312585 17.89222305\n",
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-      " [ 6.87661362  5.19601208  3.65681217  2.86070736  7.60181469  8.49386718\n",
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-      "   3.88514106 10.52806096  4.99792291  5.27677742]]\n"
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+      " [ 7.01266855 11.52761872 10.03896708  5.01278044  9.21407599 11.84338322\n",
+      "   5.52757272  4.54910928 12.53911457  5.64690121]\n",
+      " [ 5.77132083  9.48705697  8.26191905  4.12544297  7.58304607  9.74692641\n",
+      "   4.54910928  3.74384858 10.31950286  4.64731484]\n",
+      " [15.90800497 26.15001903 22.77306239 11.37132543 20.90182442 26.86632028\n",
+      "  12.53911457 10.31950286 28.44456367 12.80980728]\n",
+      " [ 7.16405708 11.77647539 10.25568695  5.12099567  9.41298822 12.09905657\n",
+      "   5.64690121  4.64731484 12.80980728  5.76880575]]\n"
      ]
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    ],
@@ -2001,15 +2001,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.05818561278267464\n",
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-      " [ 3.45170202 10.2936657  16.22773898]]\n",
-      "[25.73550536  0.06057711  3.84984612]\n"
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+      "[[ 1.22707295  3.71007076  5.46068896]\n",
+      " [ 3.71007076 12.29747685 17.51069748]\n",
+      " [ 5.46068896 17.51069748 41.22846786]]\n",
+      "[50.34205649  0.0976598   4.31330137]\n"
      ]
     }
    ],
@@ -2638,7 +2638,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2764,12 +2764,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.03974553487733608 1.0433282860079154\n"
+      "-0.006352536528464608 1.0383613556521865\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png
index 7771c0877..29874c9e2 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png
index 9c63acba6..e72c56e68 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 50e9301e2..6df840703 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -874,8 +874,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.81737781  0.45847011  0.53332849  1.04937026  0.53235952  2.24033384\n",
-      " -0.05605892 -0.02193078 -0.37343957  0.17849836]\n"
+      "[ 2.69749114 -0.06437266  0.55440601 -0.59770229  1.83671148  0.02661264\n",
+      " -1.45840653  1.96917669  0.32786329 -1.51690815]\n"
      ]
     }
    ],
@@ -1313,26 +1313,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.15586219 0.68891428 0.38675231 0.63546905 0.97286326 0.423102\n",
-      "  0.42841423 0.3478095  0.66130478 0.63443371]\n",
-      " [0.88685415 0.82561703 0.56766321 0.43258818 0.99057275 0.40058341\n",
-      "  0.84581375 0.64630711 0.03266152 0.57599494]\n",
-      " [0.10145028 0.9074157  0.39386718 0.12831838 0.46242424 0.09280524\n",
-      "  0.88415819 0.26443391 0.55095316 0.85897711]\n",
-      " [0.69243548 0.33745439 0.14452065 0.29030256 0.8063779  0.60720029\n",
-      "  0.42914251 0.44895662 0.09310479 0.48442601]\n",
-      " [0.95152814 0.83294718 0.11005335 0.8758851  0.19375828 0.73888203\n",
-      "  0.83203197 0.69203997 0.65147818 0.35195241]\n",
-      " [0.21506004 0.24874378 0.31370028 0.9525328  0.71672791 0.05879106\n",
-      "  0.45007578 0.36388542 0.50937003 0.57854115]\n",
-      " [0.80033616 0.45273617 0.18038547 0.49557088 0.36209091 0.44512218\n",
-      "  0.84078641 0.28924386 0.99166852 0.22896144]\n",
-      " [0.77579275 0.83519517 0.40640797 0.66272614 0.18234499 0.97628064\n",
-      "  0.19808709 0.1280526  0.33700495 0.32114535]\n",
-      " [0.95462534 0.72047148 0.24512233 0.18474924 0.69169665 0.68763036\n",
-      "  0.8861811  0.54193001 0.87830277 0.79251831]\n",
-      " [0.13092444 0.41452482 0.40447213 0.89714357 0.25360039 0.80373997\n",
-      "  0.51279028 0.58161787 0.08742496 0.45104086]]\n"
+      "[[0.72863433 0.17780089 0.4565147  0.92353194 0.4565685  0.45408759\n",
+      "  0.55900177 0.02561386 0.63461575 0.70015185]\n",
+      " [0.52488859 0.52729975 0.49035757 0.2383703  0.9206617  0.2766892\n",
+      "  0.89708415 0.52998985 0.45993741 0.74506271]\n",
+      " [0.67036507 0.42813351 0.79628257 0.00872006 0.06083072 0.80873785\n",
+      "  0.74407544 0.32616922 0.81337164 0.97183244]\n",
+      " [0.23449217 0.64638876 0.5291335  0.03625417 0.47382705 0.03068149\n",
+      "  0.72365764 0.53228519 0.63483713 0.01924105]\n",
+      " [0.99849105 0.05293216 0.52012715 0.77037707 0.28836035 0.7080469\n",
+      "  0.91692081 0.50152186 0.08734023 0.69892546]\n",
+      " [0.96154578 0.52229337 0.78888045 0.59879372 0.38240257 0.55451651\n",
+      "  0.01380833 0.4659454  0.51857188 0.98893579]\n",
+      " [0.38422061 0.39516594 0.22569872 0.40434293 0.80846898 0.33300231\n",
+      "  0.45647803 0.60116948 0.55862544 0.20607165]\n",
+      " [0.50029371 0.11487977 0.17093974 0.40903389 0.40362686 0.63204599\n",
+      "  0.99864751 0.7033198  0.61402242 0.72219757]\n",
+      " [0.78025829 0.65744898 0.93708805 0.68532803 0.22974904 0.00166889\n",
+      "  0.33180288 0.26196737 0.62846663 0.4390092 ]\n",
+      " [0.23570126 0.11877604 0.81642349 0.76688358 0.82757017 0.92808577\n",
+      "  0.89790867 0.97165491 0.98806419 0.48333419]]\n"
      ]
     }
    ],
@@ -1446,13 +1446,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.0458524213754298\n",
-      "3.614161296466206\n",
-      "-0.22985907723809532\n",
-      "[[0.72589774 2.09219464 1.64672839]\n",
-      " [2.09219464 6.9187554  4.62198131]\n",
-      " [1.64672839 4.62198131 6.70530438]]\n",
-      "[12.05431945  0.07101262  2.22462544]\n"
+      "0.15294186382924843\n",
+      "4.473730000968826\n",
+      "0.7333854378748542\n",
+      "[[ 0.89730533  2.66990322  2.88518   ]\n",
+      " [ 2.66990322  8.78338459  8.44007188]\n",
+      " [ 2.88518     8.44007188 16.31799354]]\n",
+      "[22.48904404  0.06826275  3.44137667]\n"
      ]
     }
    ],
@@ -1808,7 +1808,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57294/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m      7\u001b[0m               \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m               \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59734/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m      7\u001b[0m               \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m               \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index 5376525c8..539d37640 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -1533,7 +1533,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9953466931203151\n"
+      "0.9950865473984227\n"
      ]
     }
    ],
@@ -1564,7 +1564,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.010175219431920396\n"
+      "0.009998596494598343\n"
      ]
     }
    ],
@@ -1599,23 +1599,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.03699304 0.0218856  0.02021288 0.0334505  0.02960445 0.01369614\n",
-      " 0.00606112 0.01429853 0.01174937 0.02585059 0.02044223 0.00174884\n",
-      " 0.06133572 0.03159246 0.04435458 0.00125802 0.00100423 0.00230308\n",
-      " 0.01055384 0.02352843 0.06971862 0.01300036 0.00547332 0.05554795\n",
-      " 0.01142354 0.01181458 0.00148216 0.01419341 0.0305221  0.01272102\n",
-      " 0.01700746 0.01552039 0.01616657 0.10695771 0.00405576 0.02979087\n",
-      " 0.0529838  0.01420773 0.06220192 0.04104182 0.00653725 0.07170448\n",
-      " 0.00997215 0.02490769 0.02580654 0.01682317 0.03221473 0.01838531\n",
-      " 0.02028936 0.0064349  0.04323964 0.02705202 0.03692828 0.01975755\n",
-      " 0.05636265 0.02880987 0.05692207 0.04085864 0.01085261 0.01457889\n",
-      " 0.03418643 0.01919791 0.00101555 0.00636951 0.04217103 0.0476266\n",
-      " 0.01169528 0.04544327 0.00978267 0.04046984 0.0032882  0.02072876\n",
-      " 0.05526935 0.05461692 0.00877816 0.00724303 0.00045923 0.00059105\n",
-      " 0.00917881 0.04254787 0.08728977 0.04513394 0.01606644 0.08956994\n",
-      " 0.02687671 0.07449122 0.04497158 0.01713187 0.02553907 0.0397137\n",
-      " 0.03313193 0.00738299 0.01743124 0.02953975 0.01131825 0.0864086\n",
-      " 0.01925934 0.02439287 0.09331672 0.01721277]\n"
+      "[0.0442242  0.01222848 0.01176967 0.00562142 0.00392853 0.02034593\n",
+      " 0.01294799 0.07286621 0.00893112 0.00155695 0.03181997 0.00965205\n",
+      " 0.00523789 0.03510464 0.04098879 0.04626298 0.03733016 0.05632246\n",
+      " 0.02241952 0.03770824 0.05984726 0.00875286 0.04355106 0.01981665\n",
+      " 0.06929519 0.03426934 0.00410974 0.0142117  0.00099936 0.04030508\n",
+      " 0.05247827 0.05227563 0.02499437 0.01459912 0.00154737 0.03099794\n",
+      " 0.06159762 0.00052051 0.04268493 0.01479672 0.01149099 0.02280634\n",
+      " 0.04740084 0.00617261 0.00103024 0.00740838 0.00577229 0.00142146\n",
+      " 0.00140253 0.01112157 0.01180692 0.00039821 0.02257687 0.03196582\n",
+      " 0.01289266 0.03194307 0.00192165 0.08543112 0.01377529 0.06267966\n",
+      " 0.10637914 0.00872869 0.00331168 0.03291795 0.08933713 0.00786896\n",
+      " 0.01299711 0.01870931 0.03870024 0.0089204  0.0180908  0.05893076\n",
+      " 0.00110867 0.0374535  0.03500569 0.00381349 0.01974315 0.01109955\n",
+      " 0.0193386  0.01273648 0.00520618 0.00420536 0.02597861 0.01698477\n",
+      " 0.02535973 0.03901061 0.06862038 0.01811373 0.02478142 0.00293201\n",
+      " 0.06915429 0.01105668 0.01370129 0.03923714 0.02045223 0.00193326\n",
+      " 0.01717238 0.01441661 0.04590873 0.00871267]\n"
      ]
     }
    ],
@@ -1669,15 +1669,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.94735263  0.70175778  2.99348646  1.86611894 -0.45576546]\n",
+      "[  2.08019747  -1.5544062   12.12040799 -11.41377873   5.90871271]\n",
       "Training R2\n",
-      "0.9959836634296064\n",
+      "0.9958244721767004\n",
       "Training MSE\n",
-      "0.0085274606925055\n",
+      "0.010405448150048726\n",
       "Test R2\n",
-      "0.992232777849821\n",
+      "0.9929666457835595\n",
       "Test MSE\n",
-      "0.010638334964957053\n"
+      "0.018365206985554446\n"
      ]
     }
    ],
@@ -2556,7 +2556,13 @@
       " 4.14277718e-08 1.44624525e-08 2.37239927e-09 8.28205578e-10\n",
       " 2.89126914e-10 1.00934327e-10 3.52362157e-11 1.65571174e-11\n",
       " 5.78009660e-12 2.01783414e-12 7.04426744e-13 2.45915671e-13\n",
-      " 8.58492636e-14]\n",
+      " 8.58492636e-14]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Feature max values before scaling:\n",
       " [1.         0.99970894 0.99978365 0.99941797 0.99949266 0.99956735\n",
       " 0.99912709 0.99920175 0.99927642 0.9993511  0.99883628 0.99891093\n",
diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf
index 61385cfd2..bb022d15c 100644
Binary files a/doc/LectureNotes/gaussian.pdf and b/doc/LectureNotes/gaussian.pdf differ