From beb104cebf2f2008f98919edc8d2ed57078e1a02 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 9 Nov 2023 17:54:42 +0100
Subject: [PATCH] update

---
 .../_build/.doctrees/environment.pickle       | Bin 589631 -> 590854 bytes
 .../_build/.doctrees/week45.doctree           | Bin 937660 -> 938006 bytes
 .../_build/html/_sources/week45.ipynb         | 144 ++++-----
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/_build/html/week45.html      | 124 ++++----
 .../_build/jupyter_execute/week45.ipynb       | 290 ++++++++----------
 .../_build/jupyter_execute/week45.py          |   4 +
 7 files changed, 259 insertions(+), 305 deletions(-)

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 94dd3b3abeb69c7b754b4056021669be26f2c535..ff13fee30d80573c5ffbc055be3d1dd87843cfc8 100644
GIT binary patch
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diff --git a/doc/LectureNotes/_build/html/_sources/week45.ipynb b/doc/LectureNotes/_build/html/_sources/week45.ipynb
index f3cbde629..d80fa50f3 100644
--- a/doc/LectureNotes/_build/html/_sources/week45.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e9869f2",
+   "id": "967cdaca",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fca263d7",
+   "id": "7f44e4d0",
    "metadata": {
     "editable": true
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@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c3d9fea",
+   "id": "3094316d",
    "metadata": {
     "editable": true
    },
@@ -58,6 +58,10 @@
     "\n",
     "    * These lecture notes\n",
     "\n",
+    "    * [Video of lecture](https://youtu.be/z0x-vgyAZUk)\n",
+    "\n",
+    "    * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf)\n",
+    "\n",
     "    * For a more in depth discussion on  neural networks we recommend Goodfellow et al chapter 10. See also chapter 11 and 12 on practicalities and applications    \n",
     "\n",
     "     * Reading suggestions for implementation of RNNs: [Aurelien Geron's chapter 14](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf).\n",
@@ -69,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0107e8f",
+   "id": "46b1e9db",
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@@ -79,7 +83,7 @@
   },
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-   "id": "44ed2bb7",
+   "id": "19315bd5",
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@@ -98,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7d202a1c",
+   "id": "d03a6f44",
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@@ -155,7 +159,7 @@
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   {
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-   "id": "0fcc974f",
+   "id": "e6cbd4ca",
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@@ -167,7 +171,7 @@
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   {
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-   "id": "693475c8",
+   "id": "1ea723fc",
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@@ -182,7 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cdd8cf73",
+   "id": "0ebb62df",
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@@ -235,7 +239,7 @@
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   {
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-   "id": "51ef3976",
+   "id": "0fb161e1",
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@@ -250,7 +254,7 @@
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   {
    "cell_type": "markdown",
-   "id": "033bda9d",
+   "id": "8bdb137e",
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@@ -270,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0a320c52",
+   "id": "af61779f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -324,7 +328,7 @@
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   {
    "cell_type": "markdown",
-   "id": "bcd8ea70",
+   "id": "89f07674",
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     "editable": true
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@@ -339,7 +343,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "6fe70002",
+   "id": "37c8ca05",
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@@ -366,7 +370,7 @@
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+   "id": "509bf7a9",
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@@ -380,7 +384,7 @@
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-   "id": "96a74e80",
+   "id": "e9d06a0c",
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@@ -425,7 +429,7 @@
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-   "id": "98525677",
+   "id": "ab1f9810",
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@@ -450,7 +454,7 @@
   {
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    "execution_count": 6,
-   "id": "d00d3339",
+   "id": "d8a5dabe",
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@@ -462,7 +466,7 @@
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+   "id": "69fd6511",
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@@ -473,7 +477,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "382459a0",
+   "id": "b74de9e5",
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@@ -485,7 +489,7 @@
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-   "id": "06da1caf",
+   "id": "24e267e4",
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@@ -498,7 +502,7 @@
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-   "id": "2d846128",
+   "id": "77a9c527",
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@@ -531,7 +535,7 @@
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+   "id": "5f6ea3f0",
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@@ -545,7 +549,7 @@
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+   "id": "b0d324b8",
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@@ -557,7 +561,7 @@
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+   "id": "d5593af9",
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@@ -569,7 +573,7 @@
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+   "id": "e857f89e",
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@@ -581,7 +585,7 @@
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-   "id": "64e4f58f",
+   "id": "13dc6da3",
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@@ -591,7 +595,7 @@
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@@ -603,7 +607,7 @@
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@@ -613,7 +617,7 @@
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@@ -625,7 +629,7 @@
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@@ -636,7 +640,7 @@
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural Networks"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33,42],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"3d":42,"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41,42],"45":43,"5":0,"7":34,"9":[26,43],"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41,42],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41,42],"import":[5,21,25,32,33,34,42],"long":43,"new":[4,34,35,43],"short":43,"try":43,A:[0,1,4,8,9,21,32,34,35,36,40,41,42,43],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,No:42,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41,42,43],To:[32,33],With:[4,34,43],about:[32,33],abov:[23,34,41,42],activ:[1,12,23,27,34,38,39,40,41,42,43],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],add:42,addit:[42,43],adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36,42,43],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:[8,43],an:[0,4,10,32,43],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arithmet:42,arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],backpropag:[42,43],backward:43,bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36,43],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37,42],bring:[12,39],build:[1,3,9,23,40,41,42],c:[26,27,32],calcul:33,can:[21,32,35,37,38,43],cancer:[1,7,9,11,36,40,41,43],cart:9,cell:43,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:[3,42],classic:11,classif:[1,9,10,23,27,36,39,40,41,43],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:[3,42],code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41,42],coeffici:42,collect:[1,3,39,40,41,42],combin:43,come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],compil:42,complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],connect:42,construct:[39,40,41],content:42,continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39,42],convolution2dlay:42,correctli:[34,35],correl:[11,33,36,42,43],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41,42],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],ct:42,cumul:43,curv:43,cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41,42,43],dataset:[1,3,39,40,41,42],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,demonstr:42,dens:[0,32,42],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41,42],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38,42],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,23,26,33,41,42],directli:[37,38],disadvantag:9,discret:29,discuss:[36,43],distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:[29,42],don:42,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,effect:43,effici:42,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:[32,42],euler:[2,41],evalu:[1,27,39,40,41,42],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41,42,43],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explod:43,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:[4,43],extrem:[10,32],ey:10,f:[26,27],f_1:43,fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],file:42,find:[35,37,38,42],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flatten:42,flow:40,fold:[35,36],forc:[3,42],forest:10,format:[26,27,32,43],forward:[1,2,12,38,39,40,41],four:43,fourier:[3,42],frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39,42],full:[2,39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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural 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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week45.html b/doc/LectureNotes/_build/html/week45.html
index eed96be58..0653de141 100644
--- a/doc/LectureNotes/_build/html/week45.html
+++ b/doc/LectureNotes/_build/html/week45.html
@@ -867,6 +867,8 @@ doconce format html week45.do.txt --no_mako -->
 <li><p>Readings and Videos:</p>
 <ul>
 <li><p>These lecture notes</p></li>
+<li><p><a class="reference external" href="https://youtu.be/z0x-vgyAZUk">Video of lecture</a></p></li>
+<li><p><a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf">Whiteboard notes</a></p></li>
 <li><p>For a more in depth discussion on  neural networks we recommend Goodfellow et al chapter 10. See also chapter 11 and 12 on practicalities and applications</p></li>
 <li><p>Reading suggestions for implementation of RNNs: <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf">Aurelien Geron’s chapter 14</a>.</p></li>
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=SEnXr6v2ifU&amp;ab_channel=AlexanderAmini">Video  on Recurrent Neural Networks from MIT</a></p></li>
@@ -1076,7 +1078,7 @@ case under study.</p>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x107a08b50&gt;})
+                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x168435640&gt;})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
@@ -1620,327 +1622,309 @@ systems such as automatic translation and speech-to-text.</p>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 1/100
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-11-08 15:31:22.964588: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-11-09 17:53:10.939507: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 3s - loss: 1.3549 - 3s/epoch - 66ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 3s - loss: 0.5078 - 3s/epoch - 65ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 2/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4772 - 473ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4096 - 450ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 3/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4055 - 471ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3998 - 450ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 4/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3987 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3960 - 449ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 5/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3954 - 475ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3937 - 462ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 6/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3923 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3922 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 7/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3914 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3894 - 462ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 8/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3887 - 457ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3860 - 458ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 9/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3877 - 462ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 10/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3882 - 460ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3861 - 463ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 11/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3868 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3855 - 469ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 12/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3862 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3860 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 13/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3862 - 484ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3844 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 14/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3854 - 464ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3842 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 15/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3847 - 476ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3857 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 16/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3839 - 473ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3821 - 472ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 17/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3843 - 474ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3848 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 18/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3837 - 456ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3823 - 468ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 19/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3829 - 461ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3842 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 20/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3819 - 494ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3822 - 468ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 21/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3812 - 480ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3818 - 471ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 22/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3816 - 491ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3794 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 23/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3818 - 459ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3820 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 24/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3812 - 470ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3803 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 25/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3811 - 452ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3802 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 26/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3809 - 455ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3797 - 468ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 27/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3798 - 453ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3789 - 472ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 28/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3809 - 454ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3764 - 472ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 29/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3802 - 457ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3767 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 30/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3798 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3745 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 31/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3789 - 456ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3765 - 469ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 32/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3781 - 457ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3791 - 469ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 33/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3790 - 456ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3770 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 34/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3775 - 455ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3780 - 468ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 35/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3785 - 459ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3770 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 36/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3777 - 454ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3748 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 37/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3782 - 453ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3773 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 38/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3780 - 460ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3760 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 39/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3764 - 479ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3759 - 473ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 40/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3773 - 498ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3735 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 41/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3749 - 505ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3736 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 42/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3769 - 489ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3733 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 43/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3766 - 462ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3727 - 471ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 44/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3752 - 459ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3749 - 469ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 45/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3758 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3740 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 46/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3734 - 477ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3705 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 47/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3753 - 475ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3732 - 469ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 48/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3756 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3717 - 467ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 49/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3748 - 487ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3714 - 470ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 50/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3741 - 478ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3705 - 471ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 51/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3744 - 478ms/epoch - 10ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 52/100
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3756 - 458ms/epoch - 9ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 53/100
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3725 - 464ms/epoch - 9ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 54/100
-</pre></div>
-</div>
 <div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
 <span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace">                         </span>Traceback (most recent call last)
 <span class="nn">Input In [9],</span> in <span class="ni">&lt;cell line: 53&gt;</span><span class="nt">()</span>
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
index 96715bc38..45a13e2eb 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e9869f2",
+   "id": "967cdaca",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fca263d7",
+   "id": "7f44e4d0",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c3d9fea",
+   "id": "3094316d",
    "metadata": {
     "editable": true
    },
@@ -58,6 +58,10 @@
     "\n",
     "    * These lecture notes\n",
     "\n",
+    "    * [Video of lecture](https://youtu.be/z0x-vgyAZUk)\n",
+    "\n",
+    "    * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf)\n",
+    "\n",
     "    * For a more in depth discussion on  neural networks we recommend Goodfellow et al chapter 10. See also chapter 11 and 12 on practicalities and applications    \n",
     "\n",
     "     * Reading suggestions for implementation of RNNs: [Aurelien Geron's chapter 14](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf).\n",
@@ -69,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0107e8f",
+   "id": "46b1e9db",
    "metadata": {
     "editable": true
    },
@@ -79,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44ed2bb7",
+   "id": "19315bd5",
    "metadata": {
     "editable": true
    },
@@ -98,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7d202a1c",
+   "id": "d03a6f44",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -170,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fcc974f",
+   "id": "e6cbd4ca",
    "metadata": {
     "editable": true
    },
@@ -182,7 +186,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "693475c8",
+   "id": "1ea723fc",
    "metadata": {
     "editable": true
    },
@@ -197,7 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cdd8cf73",
+   "id": "0ebb62df",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -264,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51ef3976",
+   "id": "0fb161e1",
    "metadata": {
     "editable": true
    },
@@ -279,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "033bda9d",
+   "id": "8bdb137e",
    "metadata": {
     "editable": true
    },
@@ -299,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0a320c52",
+   "id": "af61779f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -310,7 +314,7 @@
      "output_type": "stream",
      "text": [
       "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
-      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x107a08b50>})\n",
+      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x168435640>})\n",
       "Best estimated lambda-value: 0.9849967686928113\n",
       "MSE score: 1.0853136633465326\n",
       "R2 score: -0.0002382102844775691\n"
@@ -365,7 +369,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcd8ea70",
+   "id": "89f07674",
    "metadata": {
     "editable": true
    },
@@ -380,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "6fe70002",
+   "id": "37c8ca05",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -431,7 +435,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4440f93",
+   "id": "509bf7a9",
    "metadata": {
     "editable": true
    },
@@ -445,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "96a74e80",
+   "id": "e9d06a0c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -519,7 +523,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98525677",
+   "id": "ab1f9810",
    "metadata": {
     "editable": true
    },
@@ -544,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d00d3339",
+   "id": "d8a5dabe",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -556,7 +560,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39d74647",
+   "id": "69fd6511",
    "metadata": {
     "editable": true
    },
@@ -567,7 +571,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "382459a0",
+   "id": "b74de9e5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -579,7 +583,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06da1caf",
+   "id": "24e267e4",
    "metadata": {
     "editable": true
    },
@@ -592,7 +596,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d846128",
+   "id": "77a9c527",
    "metadata": {
     "editable": true
    },
@@ -625,7 +629,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23991e3b",
+   "id": "5f6ea3f0",
    "metadata": {
     "editable": true
    },
@@ -639,7 +643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4581b004",
+   "id": "b0d324b8",
    "metadata": {
     "editable": true
    },
@@ -651,7 +655,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "855fc1d5",
+   "id": "d5593af9",
    "metadata": {
     "editable": true
    },
@@ -663,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b676376",
+   "id": "e857f89e",
    "metadata": {
     "editable": true
    },
@@ -675,7 +679,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64e4f58f",
+   "id": "13dc6da3",
    "metadata": {
     "editable": true
    },
@@ -685,7 +689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bef46ee",
+   "id": "0f77aa9a",
    "metadata": {
     "editable": true
    },
@@ -697,7 +701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed7cfd1e",
+   "id": "3c560ddf",
    "metadata": {
     "editable": true
    },
@@ -707,7 +711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "307589a6",
+   "id": "b46686df",
    "metadata": {
     "editable": true
    },
@@ -719,7 +723,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24f17f38",
+   "id": "24aec375",
    "metadata": {
     "editable": true
    },
@@ -730,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e825c026",
+   "id": "8d288b9d",
    "metadata": {
     "editable": true
    },
@@ -749,7 +753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6dcb34a4",
+   "id": "e1663205",
    "metadata": {
     "editable": true
    },
@@ -761,7 +765,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a975cae8",
+   "id": "b34d890c",
    "metadata": {
     "editable": true
    },
@@ -771,7 +775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4971baf",
+   "id": "14e7f314",
    "metadata": {
     "editable": true
    },
@@ -783,7 +787,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4bd001d",
+   "id": "879bac6b",
    "metadata": {
     "editable": true
    },
@@ -795,7 +799,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8b1f114",
+   "id": "31d2b3b7",
    "metadata": {
     "editable": true
    },
@@ -807,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "959cdbff",
+   "id": "34fd7537",
    "metadata": {
     "editable": true
    },
@@ -817,7 +821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59ee7f47",
+   "id": "4bebbe84",
    "metadata": {
     "editable": true
    },
@@ -829,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cdba722",
+   "id": "690c8d76",
    "metadata": {
     "editable": true
    },
@@ -857,7 +861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35390ed4",
+   "id": "12d8a75e",
    "metadata": {
     "editable": true
    },
@@ -869,7 +873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1651ea3",
+   "id": "1df29154",
    "metadata": {
     "editable": true
    },
@@ -894,7 +898,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bfac852",
+   "id": "9083178e",
    "metadata": {
     "editable": true
    },
@@ -912,7 +916,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f655bb24",
+   "id": "e7719466",
    "metadata": {
     "editable": true
    },
@@ -923,7 +927,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "31f4ee21",
+   "id": "bcddf060",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1114,7 +1118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "098bfaec",
+   "id": "6ec55e94",
    "metadata": {
     "editable": true
    },
@@ -1124,7 +1128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55cc0e20",
+   "id": "690b1a08",
    "metadata": {
     "editable": true
    },
@@ -1151,7 +1155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8860da9c",
+   "id": "825bc136",
    "metadata": {
     "editable": true
    },
@@ -1162,7 +1166,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7252c0b8",
+   "id": "ce956b33",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1291,14 +1295,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-08 15:31:22.964588: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-11-09 17:53:10.939507: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 3s - loss: 1.3549 - 3s/epoch - 66ms/step\n"
+      "50/50 - 3s - loss: 0.5078 - 3s/epoch - 65ms/step\n"
      ]
     },
     {
@@ -1312,7 +1316,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4772 - 473ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4096 - 450ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1326,7 +1330,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4055 - 471ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3998 - 450ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1340,7 +1344,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3987 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3960 - 449ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1354,7 +1358,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3954 - 475ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3937 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1368,7 +1372,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3923 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3922 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1382,7 +1386,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3914 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3894 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1396,7 +1400,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3887 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3860 - 458ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1410,7 +1414,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3877 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1424,7 +1428,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3882 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3861 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1438,7 +1442,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3868 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3855 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1452,7 +1456,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3862 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3860 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1466,7 +1470,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3862 - 484ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3844 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1480,7 +1484,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3854 - 464ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3842 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1494,7 +1498,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3847 - 476ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3857 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1508,7 +1512,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3839 - 473ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3821 - 472ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1522,7 +1526,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3843 - 474ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3848 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1536,7 +1540,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3837 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3823 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1550,7 +1554,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3829 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3842 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1564,7 +1568,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3819 - 494ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3822 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1578,7 +1582,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3812 - 480ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3818 - 471ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1592,7 +1596,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3816 - 491ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3794 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1606,7 +1610,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3818 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3820 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1620,7 +1624,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3812 - 470ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3803 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1634,7 +1638,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3811 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3802 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1648,7 +1652,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3809 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3797 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1662,7 +1666,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3798 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3789 - 472ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1676,7 +1680,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3809 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3764 - 472ms/epoch - 9ms/step\n"
      ]
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     {
@@ -1690,7 +1694,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3802 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3767 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1704,7 +1708,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3798 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3745 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1718,7 +1722,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3789 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3765 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1732,7 +1736,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3781 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3791 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1746,7 +1750,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3790 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3770 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1760,7 +1764,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3775 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3780 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1774,7 +1778,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3785 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3770 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1788,7 +1792,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3777 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3748 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1802,7 +1806,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3782 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3773 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1816,7 +1820,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3780 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3760 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1830,7 +1834,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3764 - 479ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3759 - 473ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1844,7 +1848,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3773 - 498ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3735 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1858,7 +1862,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3749 - 505ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3736 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1872,7 +1876,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3769 - 489ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3733 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1886,7 +1890,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3766 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3727 - 471ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1900,7 +1904,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3752 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3749 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1914,7 +1918,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3758 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3740 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1928,7 +1932,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3734 - 477ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3705 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1942,7 +1946,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3753 - 475ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3732 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1956,7 +1960,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3756 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3717 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1970,7 +1974,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3748 - 487ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3714 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1984,7 +1988,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3741 - 478ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3705 - 471ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1994,48 +1998,6 @@
       "Epoch 51/100\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3744 - 478ms/epoch - 10ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 52/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3756 - 458ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 53/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3725 - 464ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 54/100\n"
-     ]
-    },
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -2124,7 +2086,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcbca2f7",
+   "id": "002c4d99",
    "metadata": {
     "editable": true
    },
@@ -2144,7 +2106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c5b70a2",
+   "id": "6572c07f",
    "metadata": {
     "editable": true
    },
@@ -2160,7 +2122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29c129fe",
+   "id": "fbd8e269",
    "metadata": {
     "editable": true
    },
@@ -2182,7 +2144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2bc1aa3",
+   "id": "919bb09e",
    "metadata": {
     "editable": true
    },
@@ -2200,7 +2162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01ef7e17",
+   "id": "6e3360db",
    "metadata": {
     "editable": true
    },
@@ -2246,7 +2208,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8dd26bf6",
+   "id": "0f284cbd",
    "metadata": {
     "editable": true
    },
@@ -2267,7 +2229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b42d5ef",
+   "id": "b5e9785e",
    "metadata": {
     "editable": true
    },
@@ -2328,7 +2290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f8de715",
+   "id": "50a43c30",
    "metadata": {
     "editable": true
    },
@@ -2353,7 +2315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9998cb2c",
+   "id": "f3e0d31b",
    "metadata": {
     "editable": true
    },
@@ -2372,7 +2334,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45dfbeff",
+   "id": "b1571231",
    "metadata": {
     "editable": true
    },
@@ -2396,7 +2358,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "141b8f50",
+   "id": "e7886dd3",
    "metadata": {
     "editable": true
    },
@@ -2478,7 +2440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbadc1e0",
+   "id": "f878b195",
    "metadata": {
     "editable": true
    },
@@ -2494,7 +2456,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "9365f92f",
+   "id": "1889da48",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2533,7 +2495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55c2aa23",
+   "id": "5bf3bea4",
    "metadata": {
     "editable": true
    },
@@ -2577,7 +2539,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "b57a3099",
+   "id": "6fc9b3dd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2660,7 +2622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b519f6c",
+   "id": "6b02bff4",
    "metadata": {
     "editable": true
    },
@@ -2671,7 +2633,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "82db8594",
+   "id": "7030f585",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2773,7 +2735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4711b0d4",
+   "id": "53dc1510",
    "metadata": {
     "editable": true
    },
@@ -2795,7 +2757,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "22123a0f",
+   "id": "62aa2c1c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2892,7 +2854,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50cd4d20",
+   "id": "56fb2d92",
    "metadata": {
     "editable": true
    },
@@ -2918,7 +2880,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "5c3d5a53",
+   "id": "145ed525",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.py b/doc/LectureNotes/_build/jupyter_execute/week45.py
index 208275bf0..9213a9295 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.py
@@ -36,6 +36,10 @@
 # 
 #     * These lecture notes
 # 
+#     * [Video of lecture](https://youtu.be/z0x-vgyAZUk)
+# 
+#     * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf)
+# 
 #     * For a more in depth discussion on  neural networks we recommend Goodfellow et al chapter 10. See also chapter 11 and 12 on practicalities and applications    
 # 
 #      * Reading suggestions for implementation of RNNs: [Aurelien Geron's chapter 14](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf).