From b37c49c413d2d16ba85cd32c6a36a57d60fb27ee Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 9 Nov 2023 17:51:29 +0100
Subject: [PATCH] update with links

---
 doc/pub/week45/html/._week45-bs001.html      |    2 +
 doc/pub/week45/html/week45-reveal.html       |    4 +
 doc/pub/week45/html/week45-solarized.html    |    2 +
 doc/pub/week45/html/week45.html              |    2 +
 doc/pub/week45/ipynb/ipynb-week45-src.tar.gz |  Bin 6057452 -> 6057452 bytes
 doc/pub/week45/ipynb/week45.ipynb            | 2197 +++---------------
 doc/src/week45/week45.do.txt                 |    2 +
 7 files changed, 321 insertions(+), 1888 deletions(-)

diff --git a/doc/pub/week45/html/._week45-bs001.html b/doc/pub/week45/html/._week45-bs001.html
index 228c03090..c0a0ff484 100644
--- a/doc/pub/week45/html/._week45-bs001.html
+++ b/doc/pub/week45/html/._week45-bs001.html
@@ -241,6 +241,8 @@ MathJax.Hub.Config({
   <li> Readings and Videos:</li>
 <ul>
     <li> These lecture notes</li>
+    <li> <a href="https://youtu.be/z0x-vgyAZUk" target="_self">Video of lecture</a></li>
+    <li> <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf" target="_self">Whiteboard notes</a></li>
     <li> 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</li>    
 <ul>
      <li> Reading suggestions for implementation of RNNs: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_self">Aurelien Geron's chapter 14</a>.</li>
diff --git a/doc/pub/week45/html/week45-reveal.html b/doc/pub/week45/html/week45-reveal.html
index bf95a84ee..e98452bc4 100644
--- a/doc/pub/week45/html/week45-reveal.html
+++ b/doc/pub/week45/html/week45-reveal.html
@@ -229,6 +229,10 @@ MathJax.Hub.Config({
 
 <p><li> These lecture notes</li>
 
+<p><li> <a href="https://youtu.be/z0x-vgyAZUk" target="_blank">Video of lecture</a></li>
+
+<p><li> <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf" target="_blank">Whiteboard notes</a></li>
+
 <p><li> 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</li>    
 <ul>
 
diff --git a/doc/pub/week45/html/week45-solarized.html b/doc/pub/week45/html/week45-solarized.html
index 23424a6ce..61e6b417b 100644
--- a/doc/pub/week45/html/week45-solarized.html
+++ b/doc/pub/week45/html/week45-solarized.html
@@ -229,6 +229,8 @@ MathJax.Hub.Config({
   <li> Readings and Videos:</li>
 <ul>
     <li> These lecture notes</li>
+    <li> <a href="https://youtu.be/z0x-vgyAZUk" target="_blank">Video of lecture</a></li>
+    <li> <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf" target="_blank">Whiteboard notes</a></li>
     <li> 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</li>    
 <ul>
      <li> Reading suggestions for implementation of RNNs: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapter 14</a>.</li>
diff --git a/doc/pub/week45/html/week45.html b/doc/pub/week45/html/week45.html
index d89263aa9..554301e63 100644
--- a/doc/pub/week45/html/week45.html
+++ b/doc/pub/week45/html/week45.html
@@ -306,6 +306,8 @@ MathJax.Hub.Config({
   <li> Readings and Videos:</li>
 <ul>
     <li> These lecture notes</li>
+    <li> <a href="https://youtu.be/z0x-vgyAZUk" target="_blank">Video of lecture</a></li>
+    <li> <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov9.pdf" target="_blank">Whiteboard notes</a></li>
     <li> 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</li>    
 <ul>
      <li> Reading suggestions for implementation of RNNs: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapter 14</a>.</li>
diff --git a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz
index 5a3975e10cd4728dda9f7e3033202b43420e8c70..44aabc0bb6ce20d420c04978a58b88ad4e489a18 100644
GIT binary patch
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diff --git a/doc/pub/week45/ipynb/week45.ipynb b/doc/pub/week45/ipynb/week45.ipynb
index 3c7f78419..d80fa50f3 100644
--- a/doc/pub/week45/ipynb/week45.ipynb
+++ b/doc/pub/week45/ipynb/week45.ipynb
@@ -2,8 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e9869f2",
-   "metadata": {},
+   "id": "967cdaca",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week45.do.txt --no_mako -->\n",
@@ -12,8 +14,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fca263d7",
-   "metadata": {},
+   "id": "7f44e4d0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "# Week 45,  Recurrent Neural Networks\n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
@@ -23,8 +27,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c3d9fea",
-   "metadata": {},
+   "id": "3094316d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Plan for week 45\n",
     "\n",
@@ -52,6 +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",
@@ -63,16 +73,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0107e8f",
-   "metadata": {},
+   "id": "46b1e9db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Material for the lab sessions, additional ways to present classification results and other practicalities"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "44ed2bb7",
-   "metadata": {},
+   "id": "19315bd5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Searching for Optimal Regularization Parameters $\\lambda$\n",
     "\n",
@@ -88,8 +102,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7d202a1c",
-   "metadata": {},
+   "id": "d03a6f44",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -142,8 +159,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fcc974f",
-   "metadata": {},
+   "id": "e6cbd4ca",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here we have performed a rather data greedy calculation as function of the regularization parameter $\\lambda$. There is no resampling here. The latter can easily be added by employing the function **RidgeCV** instead of just calling the **Ridge** function. For **RidgeCV** we need to pass the array of $\\lambda$ values.\n",
     "By inspecting the figure we can in turn determine which is the optimal regularization parameter.\n",
@@ -152,8 +171,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "693475c8",
-   "metadata": {},
+   "id": "1ea723fc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Grid Search\n",
     "\n",
@@ -165,8 +186,11 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cdd8cf73",
-   "metadata": {},
+   "id": "0ebb62df",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -215,8 +239,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51ef3976",
-   "metadata": {},
+   "id": "0fb161e1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "By default the grid search function includes cross validation with\n",
     "five folds. The [Scikit-Learn\n",
@@ -228,8 +254,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "033bda9d",
-   "metadata": {},
+   "id": "8bdb137e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Randomized Grid Search\n",
     "\n",
@@ -246,8 +274,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0a320c52",
-   "metadata": {},
+   "id": "af61779f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -297,8 +328,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcd8ea70",
-   "metadata": {},
+   "id": "89f07674",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Wisconsin Cancer Data\n",
     "\n",
@@ -310,8 +343,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "6fe70002",
-   "metadata": {},
+   "id": "37c8ca05",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -334,8 +370,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4440f93",
-   "metadata": {},
+   "id": "509bf7a9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Using the correlation matrix\n",
     "\n",
@@ -346,8 +384,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "96a74e80",
-   "metadata": {},
+   "id": "e9d06a0c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -388,8 +429,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98525677",
-   "metadata": {},
+   "id": "ab1f9810",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Discussing the correlation data\n",
     "\n",
@@ -411,8 +454,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d00d3339",
-   "metadata": {},
+   "id": "d8a5dabe",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
@@ -420,8 +466,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39d74647",
-   "metadata": {},
+   "id": "69fd6511",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and then"
    ]
@@ -429,8 +477,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "382459a0",
-   "metadata": {},
+   "id": "b74de9e5",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "correlation_matrix = cancerpd.corr().round(1)"
@@ -438,8 +489,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06da1caf",
-   "metadata": {},
+   "id": "24e267e4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Diagonalizing this matrix we can in turn say something about which\n",
     "features are of relevance and which are not. This leads  us to\n",
@@ -449,8 +502,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d846128",
-   "metadata": {},
+   "id": "77a9c527",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other ways of presenting a classification problem\n",
     "\n",
@@ -480,8 +535,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23991e3b",
-   "metadata": {},
+   "id": "5f6ea3f0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Combinations of classification results\n",
     "\n",
@@ -492,8 +549,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4581b004",
-   "metadata": {},
+   "id": "b0d324b8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {TPR} ={\\frac {\\mathrm {TP} }{\\mathrm {P} }}={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FN} }}=1-\\mathrm {FNR} }\n",
@@ -502,8 +561,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "855fc1d5",
-   "metadata": {},
+   "id": "d5593af9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The $TPR$ defines how many correct positive results occur among all positive samples available during the test\n",
     "\n",
@@ -512,8 +573,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b676376",
-   "metadata": {},
+   "id": "e857f89e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FNR} ={\\frac {\\mathrm {FN} }{\\mathrm {P} }}={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TP} }} }\n",
@@ -522,16 +585,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64e4f58f",
-   "metadata": {},
+   "id": "13dc6da3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6bef46ee",
-   "metadata": {},
+   "id": "0f77aa9a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {TNR} ={\\frac {\\mathrm {TN} }{\\mathrm {N} }}={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FP} }}=1-\\mathrm {FPR} }\n",
@@ -540,16 +607,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed7cfd1e",
-   "metadata": {},
+   "id": "3c560ddf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "with the fall-out false positive rate"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "307589a6",
-   "metadata": {},
+   "id": "b46686df",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FPR} ={\\frac {\\mathrm {FP} }{\\mathrm {N} }}={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TN} }}=1-\\mathrm {TNR} }\n",
@@ -558,8 +629,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24f17f38",
-   "metadata": {},
+   "id": "24aec375",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The $FPR$ defines how many incorrect positive results occur among\n",
     "all negative samples available during the test."
@@ -567,8 +640,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e825c026",
-   "metadata": {},
+   "id": "8d288b9d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Positive and negative prediction values\n",
     "\n",
@@ -584,8 +659,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6dcb34a4",
-   "metadata": {},
+   "id": "e1663205",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {PPV} ={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FP} }}=1-\\mathrm {FDR} }\n",
@@ -594,16 +671,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a975cae8",
-   "metadata": {},
+   "id": "b34d890c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**Negative predictive value $NPV$.**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d4971baf",
-   "metadata": {},
+   "id": "14e7f314",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {NPV} ={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FN} }}=1-\\mathrm {FOR} }\n",
@@ -612,8 +693,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4bd001d",
-   "metadata": {},
+   "id": "879bac6b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other quantities\n",
     "\n",
@@ -622,8 +705,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8b1f114",
-   "metadata": {},
+   "id": "31d2b3b7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FDR} ={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TP} }}=1-\\mathrm {PPV} }\n",
@@ -632,16 +717,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "959cdbff",
-   "metadata": {},
+   "id": "34fd7537",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**False omission rate $FOR$.**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "59ee7f47",
-   "metadata": {},
+   "id": "4bebbe84",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {FOR} ={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TN} }}=1-\\mathrm {NPV} }\n",
@@ -650,8 +739,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cdba722",
-   "metadata": {},
+   "id": "690c8d76",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## $F_1$ score\n",
     "\n",
@@ -676,8 +767,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35390ed4",
-   "metadata": {},
+   "id": "12d8a75e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\mathrm {F} _{1}=2\\times {\\frac {\\mathrm {PPV} \\times \\mathrm {TPR} }{\\mathrm {PPV} +\\mathrm {TPR} }}={\\frac {2\\mathrm {TP} }{2\\mathrm {TP} +\\mathrm {FP} +\\mathrm {FN} }}}\n",
@@ -686,8 +779,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1651ea3",
-   "metadata": {},
+   "id": "1df29154",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## ROC curve\n",
     "\n",
@@ -709,8 +804,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bfac852",
-   "metadata": {},
+   "id": "9083178e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Cumulative gain curve\n",
     "\n",
@@ -725,8 +822,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f655bb24",
-   "metadata": {},
+   "id": "e7719466",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other measures in classification studies: Cancer Data  again"
    ]
@@ -734,8 +833,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "31f4ee21",
-   "metadata": {},
+   "id": "bcddf060",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -774,16 +876,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "098bfaec",
-   "metadata": {},
+   "id": "6ec55e94",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Material for Lecture Thursday November 9"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "55cc0e20",
-   "metadata": {},
+   "id": "690b1a08",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Recurrent neural networks (RNNs): Overarching view\n",
     "\n",
@@ -807,251 +913,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8860da9c",
-   "metadata": {},
+   "id": "825bc136",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A simple example"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "7252c0b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_2\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " simple_rnn_2 (SimpleRNN)    (None, 32)                1184      \n",
-      "                                                                 \n",
-      " dense_4 (Dense)             (None, 8)                 264       \n",
-      "                                                                 \n",
-      " dense_5 (Dense)             (None, 1)                 9         \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 1,457\n",
-      "Trainable params: 1,457\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/100\n",
-      "50/50 - 3s - loss: 0.4503 - 3s/epoch - 69ms/step\n",
-      "Epoch 2/100\n",
-      "50/50 - 1s - loss: 0.4265 - 770ms/epoch - 15ms/step\n",
-      "Epoch 3/100\n",
-      "50/50 - 1s - loss: 0.4127 - 571ms/epoch - 11ms/step\n",
-      "Epoch 4/100\n",
-      "50/50 - 1s - loss: 0.4051 - 604ms/epoch - 12ms/step\n",
-      "Epoch 5/100\n",
-      "50/50 - 1s - loss: 0.4030 - 522ms/epoch - 10ms/step\n",
-      "Epoch 6/100\n",
-      "50/50 - 1s - loss: 0.4041 - 531ms/epoch - 11ms/step\n",
-      "Epoch 7/100\n",
-      "50/50 - 1s - loss: 0.4015 - 507ms/epoch - 10ms/step\n",
-      "Epoch 8/100\n",
-      "50/50 - 1s - loss: 0.3995 - 510ms/epoch - 10ms/step\n",
-      "Epoch 9/100\n",
-      "50/50 - 1s - loss: 0.4005 - 514ms/epoch - 10ms/step\n",
-      "Epoch 10/100\n",
-      "50/50 - 1s - loss: 0.3977 - 504ms/epoch - 10ms/step\n",
-      "Epoch 11/100\n",
-      "50/50 - 1s - loss: 0.4007 - 501ms/epoch - 10ms/step\n",
-      "Epoch 12/100\n",
-      "50/50 - 0s - loss: 0.3978 - 493ms/epoch - 10ms/step\n",
-      "Epoch 13/100\n",
-      "50/50 - 1s - loss: 0.4000 - 502ms/epoch - 10ms/step\n",
-      "Epoch 14/100\n",
-      "50/50 - 0s - loss: 0.3963 - 495ms/epoch - 10ms/step\n",
-      "Epoch 15/100\n",
-      "50/50 - 0s - loss: 0.3960 - 493ms/epoch - 10ms/step\n",
-      "Epoch 16/100\n",
-      "50/50 - 0s - loss: 0.3951 - 483ms/epoch - 10ms/step\n",
-      "Epoch 17/100\n",
-      "50/50 - 0s - loss: 0.3947 - 490ms/epoch - 10ms/step\n",
-      "Epoch 18/100\n",
-      "50/50 - 0s - loss: 0.3939 - 483ms/epoch - 10ms/step\n",
-      "Epoch 19/100\n",
-      "50/50 - 0s - loss: 0.3925 - 481ms/epoch - 10ms/step\n",
-      "Epoch 20/100\n",
-      "50/50 - 0s - loss: 0.3926 - 487ms/epoch - 10ms/step\n",
-      "Epoch 21/100\n",
-      "50/50 - 1s - loss: 0.3935 - 502ms/epoch - 10ms/step\n",
-      "Epoch 22/100\n",
-      "50/50 - 0s - loss: 0.3892 - 493ms/epoch - 10ms/step\n",
-      "Epoch 23/100\n",
-      "50/50 - 0s - loss: 0.3913 - 483ms/epoch - 10ms/step\n",
-      "Epoch 24/100\n",
-      "50/50 - 0s - loss: 0.3897 - 483ms/epoch - 10ms/step\n",
-      "Epoch 25/100\n",
-      "50/50 - 1s - loss: 0.3894 - 502ms/epoch - 10ms/step\n",
-      "Epoch 26/100\n",
-      "50/50 - 0s - loss: 0.3886 - 488ms/epoch - 10ms/step\n",
-      "Epoch 27/100\n",
-      "50/50 - 0s - loss: 0.3909 - 480ms/epoch - 10ms/step\n",
-      "Epoch 28/100\n",
-      "50/50 - 0s - loss: 0.3862 - 491ms/epoch - 10ms/step\n",
-      "Epoch 29/100\n",
-      "50/50 - 0s - loss: 0.3883 - 494ms/epoch - 10ms/step\n",
-      "Epoch 30/100\n",
-      "50/50 - 0s - loss: 0.3890 - 488ms/epoch - 10ms/step\n",
-      "Epoch 31/100\n",
-      "50/50 - 0s - loss: 0.3883 - 486ms/epoch - 10ms/step\n",
-      "Epoch 32/100\n",
-      "50/50 - 0s - loss: 0.3868 - 486ms/epoch - 10ms/step\n",
-      "Epoch 33/100\n",
-      "50/50 - 0s - loss: 0.3862 - 495ms/epoch - 10ms/step\n",
-      "Epoch 34/100\n",
-      "50/50 - 0s - loss: 0.3873 - 492ms/epoch - 10ms/step\n",
-      "Epoch 35/100\n",
-      "50/50 - 0s - loss: 0.3869 - 487ms/epoch - 10ms/step\n",
-      "Epoch 36/100\n",
-      "50/50 - 0s - loss: 0.3873 - 487ms/epoch - 10ms/step\n",
-      "Epoch 37/100\n",
-      "50/50 - 0s - loss: 0.3865 - 479ms/epoch - 10ms/step\n",
-      "Epoch 38/100\n",
-      "50/50 - 0s - loss: 0.3849 - 499ms/epoch - 10ms/step\n",
-      "Epoch 39/100\n",
-      "50/50 - 0s - loss: 0.3871 - 491ms/epoch - 10ms/step\n",
-      "Epoch 40/100\n",
-      "50/50 - 0s - loss: 0.3845 - 490ms/epoch - 10ms/step\n",
-      "Epoch 41/100\n",
-      "50/50 - 0s - loss: 0.3840 - 498ms/epoch - 10ms/step\n",
-      "Epoch 42/100\n",
-      "50/50 - 0s - loss: 0.3817 - 490ms/epoch - 10ms/step\n",
-      "Epoch 43/100\n",
-      "50/50 - 0s - loss: 0.3838 - 500ms/epoch - 10ms/step\n",
-      "Epoch 44/100\n",
-      "50/50 - 0s - loss: 0.3824 - 485ms/epoch - 10ms/step\n",
-      "Epoch 45/100\n",
-      "50/50 - 0s - loss: 0.3840 - 497ms/epoch - 10ms/step\n",
-      "Epoch 46/100\n",
-      "50/50 - 1s - loss: 0.3826 - 515ms/epoch - 10ms/step\n",
-      "Epoch 47/100\n",
-      "50/50 - 1s - loss: 0.3791 - 533ms/epoch - 11ms/step\n",
-      "Epoch 48/100\n",
-      "50/50 - 1s - loss: 0.3823 - 540ms/epoch - 11ms/step\n",
-      "Epoch 49/100\n",
-      "50/50 - 1s - loss: 0.3829 - 540ms/epoch - 11ms/step\n",
-      "Epoch 50/100\n",
-      "50/50 - 1s - loss: 0.3813 - 618ms/epoch - 12ms/step\n",
-      "Epoch 51/100\n",
-      "50/50 - 1s - loss: 0.3821 - 563ms/epoch - 11ms/step\n",
-      "Epoch 52/100\n",
-      "50/50 - 1s - loss: 0.3821 - 581ms/epoch - 12ms/step\n",
-      "Epoch 53/100\n",
-      "50/50 - 1s - loss: 0.3806 - 620ms/epoch - 12ms/step\n",
-      "Epoch 54/100\n",
-      "50/50 - 1s - loss: 0.3782 - 601ms/epoch - 12ms/step\n",
-      "Epoch 55/100\n",
-      "50/50 - 1s - loss: 0.3793 - 584ms/epoch - 12ms/step\n",
-      "Epoch 56/100\n",
-      "50/50 - 1s - loss: 0.3778 - 578ms/epoch - 12ms/step\n",
-      "Epoch 57/100\n",
-      "50/50 - 1s - loss: 0.3793 - 567ms/epoch - 11ms/step\n",
-      "Epoch 58/100\n",
-      "50/50 - 1s - loss: 0.3805 - 595ms/epoch - 12ms/step\n",
-      "Epoch 59/100\n",
-      "50/50 - 1s - loss: 0.3798 - 571ms/epoch - 11ms/step\n",
-      "Epoch 60/100\n",
-      "50/50 - 1s - loss: 0.3779 - 534ms/epoch - 11ms/step\n",
-      "Epoch 61/100\n",
-      "50/50 - 1s - loss: 0.3768 - 521ms/epoch - 10ms/step\n",
-      "Epoch 62/100\n",
-      "50/50 - 1s - loss: 0.3778 - 525ms/epoch - 11ms/step\n",
-      "Epoch 63/100\n",
-      "50/50 - 1s - loss: 0.3786 - 530ms/epoch - 11ms/step\n",
-      "Epoch 64/100\n",
-      "50/50 - 1s - loss: 0.3766 - 533ms/epoch - 11ms/step\n",
-      "Epoch 65/100\n",
-      "50/50 - 1s - loss: 0.3784 - 523ms/epoch - 10ms/step\n",
-      "Epoch 66/100\n",
-      "50/50 - 1s - loss: 0.3771 - 520ms/epoch - 10ms/step\n",
-      "Epoch 67/100\n",
-      "50/50 - 1s - loss: 0.3740 - 515ms/epoch - 10ms/step\n",
-      "Epoch 68/100\n",
-      "50/50 - 1s - loss: 0.3755 - 520ms/epoch - 10ms/step\n",
-      "Epoch 69/100\n",
-      "50/50 - 1s - loss: 0.3758 - 507ms/epoch - 10ms/step\n",
-      "Epoch 70/100\n",
-      "50/50 - 1s - loss: 0.3746 - 503ms/epoch - 10ms/step\n",
-      "Epoch 71/100\n",
-      "50/50 - 1s - loss: 0.3755 - 512ms/epoch - 10ms/step\n",
-      "Epoch 72/100\n",
-      "50/50 - 1s - loss: 0.3734 - 501ms/epoch - 10ms/step\n",
-      "Epoch 73/100\n",
-      "50/50 - 1s - loss: 0.3752 - 520ms/epoch - 10ms/step\n",
-      "Epoch 74/100\n",
-      "50/50 - 1s - loss: 0.3746 - 515ms/epoch - 10ms/step\n",
-      "Epoch 75/100\n",
-      "50/50 - 1s - loss: 0.3759 - 506ms/epoch - 10ms/step\n",
-      "Epoch 76/100\n",
-      "50/50 - 1s - loss: 0.3741 - 525ms/epoch - 10ms/step\n",
-      "Epoch 77/100\n",
-      "50/50 - 1s - loss: 0.3723 - 513ms/epoch - 10ms/step\n",
-      "Epoch 78/100\n",
-      "50/50 - 1s - loss: 0.3701 - 526ms/epoch - 11ms/step\n",
-      "Epoch 79/100\n",
-      "50/50 - 1s - loss: 0.3703 - 521ms/epoch - 10ms/step\n",
-      "Epoch 80/100\n",
-      "50/50 - 1s - loss: 0.3730 - 616ms/epoch - 12ms/step\n",
-      "Epoch 81/100\n",
-      "50/50 - 1s - loss: 0.3734 - 630ms/epoch - 13ms/step\n",
-      "Epoch 82/100\n",
-      "50/50 - 1s - loss: 0.3735 - 585ms/epoch - 12ms/step\n",
-      "Epoch 83/100\n",
-      "50/50 - 1s - loss: 0.3721 - 520ms/epoch - 10ms/step\n",
-      "Epoch 84/100\n",
-      "50/50 - 1s - loss: 0.3715 - 545ms/epoch - 11ms/step\n",
-      "Epoch 85/100\n",
-      "50/50 - 1s - loss: 0.3705 - 620ms/epoch - 12ms/step\n",
-      "Epoch 86/100\n",
-      "50/50 - 1s - loss: 0.3719 - 800ms/epoch - 16ms/step\n",
-      "Epoch 87/100\n",
-      "50/50 - 1s - loss: 0.3705 - 817ms/epoch - 16ms/step\n",
-      "Epoch 88/100\n",
-      "50/50 - 1s - loss: 0.3715 - 585ms/epoch - 12ms/step\n",
-      "Epoch 89/100\n",
-      "50/50 - 1s - loss: 0.3705 - 647ms/epoch - 13ms/step\n",
-      "Epoch 90/100\n",
-      "50/50 - 1s - loss: 0.3712 - 600ms/epoch - 12ms/step\n",
-      "Epoch 91/100\n",
-      "50/50 - 1s - loss: 0.3656 - 523ms/epoch - 10ms/step\n",
-      "Epoch 92/100\n",
-      "50/50 - 1s - loss: 0.3687 - 515ms/epoch - 10ms/step\n",
-      "Epoch 93/100\n",
-      "50/50 - 1s - loss: 0.3692 - 530ms/epoch - 11ms/step\n",
-      "Epoch 94/100\n",
-      "50/50 - 1s - loss: 0.3700 - 507ms/epoch - 10ms/step\n",
-      "Epoch 95/100\n",
-      "50/50 - 1s - loss: 0.3683 - 598ms/epoch - 12ms/step\n",
-      "Epoch 96/100\n",
-      "50/50 - 1s - loss: 0.3683 - 625ms/epoch - 12ms/step\n",
-      "Epoch 97/100\n",
-      "50/50 - 1s - loss: 0.3683 - 598ms/epoch - 12ms/step\n",
-      "Epoch 98/100\n",
-      "50/50 - 1s - loss: 0.3676 - 571ms/epoch - 11ms/step\n",
-      "Epoch 99/100\n",
-      "50/50 - 1s - loss: 0.3707 - 577ms/epoch - 12ms/step\n",
-      "Epoch 100/100\n",
-      "50/50 - 1s - loss: 0.3663 - 599ms/epoch - 12ms/step\n",
-      "0.37175318598747253\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 9,
+   "id": "ce956b33",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Start importing packages\n",
     "import pandas as pd\n",
@@ -1119,8 +997,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcbca2f7",
-   "metadata": {},
+   "id": "002c4d99",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### RNNs\n",
     "\n",
@@ -1137,8 +1017,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c5b70a2",
-   "metadata": {},
+   "id": "6572c07f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Basic layout\n",
     "\n",
@@ -1151,8 +1033,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29c129fe",
-   "metadata": {},
+   "id": "fbd8e269",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### We need to specify the initial activity state of all the hidden and output units\n",
     "\n",
@@ -1171,8 +1055,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2bc1aa3",
-   "metadata": {},
+   "id": "919bb09e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### We can specify inputs in several ways\n",
     "\n",
@@ -1187,8 +1073,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01ef7e17",
-   "metadata": {},
+   "id": "6e3360db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### We can specify targets in several ways\n",
     "\n",
@@ -1231,8 +1119,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8dd26bf6",
-   "metadata": {},
+   "id": "0f284cbd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Backpropagation through time\n",
     "\n",
@@ -1250,8 +1140,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b42d5ef",
-   "metadata": {},
+   "id": "b5e9785e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### The backward pass is linear\n",
     "\n",
@@ -1309,8 +1201,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f8de715",
-   "metadata": {},
+   "id": "50a43c30",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The problem of exploding or vanishing gradients\n",
     "* What happens to the magnitude of the gradients as we backpropagate through many layers?\n",
@@ -1332,8 +1226,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9998cb2c",
-   "metadata": {},
+   "id": "f3e0d31b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Four effective ways to learn an RNN\n",
     "1. Long Short Term Memory Make the RNN out of little modules that are designed to remember values for a long time.\n",
@@ -1349,8 +1245,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45dfbeff",
-   "metadata": {},
+   "id": "b1571231",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Long Short Term Memory (LSTM)\n",
     "\n",
@@ -1371,8 +1269,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "141b8f50",
-   "metadata": {},
+   "id": "e7886dd3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Implementing a memory cell in a neural network\n",
     "\n",
@@ -1451,8 +1351,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbadc1e0",
-   "metadata": {},
+   "id": "f878b195",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## An extrapolation example\n",
     "\n",
@@ -1464,9 +1366,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "9365f92f",
-   "metadata": {},
+   "execution_count": 10,
+   "id": "1889da48",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -1501,8 +1406,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55c2aa23",
-   "metadata": {},
+   "id": "5bf3bea4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Formatting the Data\n",
     "\n",
@@ -1542,9 +1449,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "b57a3099",
-   "metadata": {},
+   "execution_count": 11,
+   "id": "6fc9b3dd",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# FORMAT_DATA\n",
@@ -1623,394 +1533,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b519f6c",
-   "metadata": {},
+   "id": "6b02bff4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Predicting New Points With A Trained Recurrent Neural Network"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "82db8594",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_1 (InputLayer)        [(None, 2, 1)]            0         \n",
-      "                                                                 \n",
-      " RNN (SimpleRNN)             (None, 200)               40400     \n",
-      "                                                                 \n",
-      " dense (Dense)               (None, 1)                 201       \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 40,601\n",
-      "Trainable params: 40,601\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/150\n",
-      "1/1 [==============================] - 4s 4s/step - loss: 0.1358 - val_loss: 0.1403\n",
-      "Epoch 2/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 0.0512 - val_loss: 0.0151\n",
-      "Epoch 3/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 0.0079 - val_loss: 0.0109\n",
-      "Epoch 4/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 0.0020 - val_loss: 0.0761\n",
-      "Epoch 5/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 0.0186 - val_loss: 0.1332\n",
-      "Epoch 6/150\n",
-      "1/1 [==============================] - 0s 35ms/step - loss: 0.0344 - val_loss: 0.1432\n",
-      "Epoch 7/150\n",
-      "1/1 [==============================] - 0s 38ms/step - loss: 0.0369 - val_loss: 0.1146\n",
-      "Epoch 8/150\n",
-      "1/1 [==============================] - 0s 33ms/step - loss: 0.0286 - val_loss: 0.0705\n",
-      "Epoch 9/150\n",
-      "1/1 [==============================] - 0s 35ms/step - loss: 0.0165 - val_loss: 0.0309\n",
-      "Epoch 10/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0064 - val_loss: 0.0067\n",
-      "Epoch 11/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0012 - val_loss: 2.4796e-05\n",
-      "Epoch 12/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0012 - val_loss: 0.0060\n",
-      "Epoch 13/150\n",
-      "1/1 [==============================] - 0s 35ms/step - loss: 0.0046 - val_loss: 0.0167\n",
-      "Epoch 14/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0089 - val_loss: 0.0250\n",
-      "Epoch 15/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0119 - val_loss: 0.0273\n",
-      "Epoch 16/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0126 - val_loss: 0.0232\n",
-      "Epoch 17/150\n",
-      "1/1 [==============================] - 0s 35ms/step - loss: 0.0109 - val_loss: 0.0153\n",
-      "Epoch 18/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0079 - val_loss: 0.0069\n",
-      "Epoch 19/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 0.0044 - val_loss: 0.0013\n",
-      "Epoch 20/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 0.0018 - val_loss: 1.7361e-04\n",
-      "Epoch 21/150\n",
-      "1/1 [==============================] - 0s 34ms/step - loss: 4.6921e-04 - val_loss: 0.0035\n",
-      "Epoch 22/150\n",
-      "1/1 [==============================] - 0s 35ms/step - loss: 6.1296e-04 - val_loss: 0.0097\n",
-      "Epoch 23/150\n",
-      "1/1 [==============================] - 0s 33ms/step - loss: 0.0018 - val_loss: 0.0160\n",
-      "Epoch 24/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 0.0032 - val_loss: 0.0201\n",
-      "Epoch 25/150\n",
-      "1/1 [==============================] - 0s 33ms/step - loss: 0.0043 - val_loss: 0.0206\n",
-      "Epoch 26/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 0.0044 - val_loss: 0.0176\n",
-      "Epoch 27/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 0.0038 - val_loss: 0.0125\n",
-      "Epoch 28/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 0.0026 - val_loss: 0.0070\n",
-      "Epoch 29/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 0.0013 - val_loss: 0.0027\n",
-      "Epoch 30/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 4.4365e-04 - val_loss: 3.8111e-04\n",
-      "Epoch 31/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 1.3242e-04 - val_loss: 9.4957e-05\n",
-      "Epoch 32/150\n",
-      "1/1 [==============================] - 0s 33ms/step - loss: 3.4446e-04 - val_loss: 0.0011\n",
-      "Epoch 33/150\n",
-      "1/1 [==============================] - 0s 33ms/step - loss: 8.5255e-04 - val_loss: 0.0025\n",
-      "Epoch 34/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 0.0014 - val_loss: 0.0034\n",
-      "Epoch 35/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 0.0016 - val_loss: 0.0034\n",
-      "Epoch 36/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 0.0016 - val_loss: 0.0026\n",
-      "Epoch 37/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 0.0012 - val_loss: 0.0014\n",
-      "Epoch 38/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 7.4979e-04 - val_loss: 4.0524e-04\n",
-      "Epoch 39/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 3.0988e-04 - val_loss: 1.0932e-06\n",
-      "Epoch 40/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 5.9443e-05 - val_loss: 3.0524e-04\n",
-      "Epoch 41/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 4.6006e-05 - val_loss: 0.0011\n",
-      "Epoch 42/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 2.1511e-04 - val_loss: 0.0020\n",
-      "Epoch 43/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 4.4589e-04 - val_loss: 0.0026\n",
-      "Epoch 44/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 6.1132e-04 - val_loss: 0.0026\n",
-      "Epoch 45/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 6.3438e-04 - val_loss: 0.0021\n",
-      "Epoch 46/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 5.1457e-04 - val_loss: 0.0013\n",
-      "Epoch 47/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 3.1613e-04 - val_loss: 5.5759e-04\n",
-      "Epoch 48/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 1.2872e-04 - val_loss: 9.6631e-05\n",
-      "Epoch 49/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 2.2951e-05 - val_loss: 1.1998e-05\n",
-      "Epoch 50/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 2.1911e-05 - val_loss: 2.1755e-04\n",
-      "Epoch 51/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.8825e-05 - val_loss: 5.2317e-04\n",
-      "Epoch 52/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.9744e-04 - val_loss: 7.3731e-04\n",
-      "Epoch 53/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 2.6219e-04 - val_loss: 7.5233e-04\n",
-      "Epoch 54/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 2.6303e-04 - val_loss: 5.7753e-04\n",
-      "Epoch 55/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 2.0478e-04 - val_loss: 3.1400e-04\n",
-      "Epoch 56/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.1931e-04 - val_loss: 9.1250e-05\n",
-      "Epoch 57/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 4.6464e-05 - val_loss: 5.5289e-07\n",
-      "Epoch 58/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.4166e-05 - val_loss: 5.6228e-05\n",
-      "Epoch 59/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 2.6900e-05 - val_loss: 1.9963e-04\n",
-      "Epoch 60/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 6.7020e-05 - val_loss: 3.3837e-04\n",
-      "Epoch 61/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.0667e-04 - val_loss: 3.9688e-04\n",
-      "Epoch 62/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 1.2309e-04 - val_loss: 3.5120e-04\n",
-      "Epoch 63/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0917e-04 - val_loss: 2.3268e-04\n",
-      "Epoch 64/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 7.4492e-05 - val_loss: 1.0310e-04\n",
-      "Epoch 65/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 3.7710e-05 - val_loss: 1.8492e-05\n",
-      "Epoch 66/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.5549e-05 - val_loss: 1.9955e-06\n",
-      "Epoch 67/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 1.4572e-05 - val_loss: 3.8244e-05\n",
-      "Epoch 68/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 2.9549e-05 - val_loss: 8.8578e-05\n",
-      "Epoch 69/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 4.8135e-05 - val_loss: 1.1572e-04\n",
-      "Epoch 70/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 5.8425e-05 - val_loss: 1.0356e-04\n",
-      "Epoch 71/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 5.5028e-05 - val_loss: 6.2381e-05\n",
-      "Epoch 72/150\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 28ms/step - loss: 4.0764e-05 - val_loss: 1.9152e-05\n",
-      "Epoch 73/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 2.3823e-05 - val_loss: 4.2704e-08\n",
-      "Epoch 74/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.2548e-05 - val_loss: 1.5742e-05\n",
-      "Epoch 75/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0956e-05 - val_loss: 5.7038e-05\n",
-      "Epoch 76/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.7268e-05 - val_loss: 1.0175e-04\n",
-      "Epoch 77/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 2.5808e-05 - val_loss: 1.2780e-04\n",
-      "Epoch 78/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 3.0734e-05 - val_loss: 1.2449e-04\n",
-      "Epoch 79/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 2.9276e-05 - val_loss: 9.6184e-05\n",
-      "Epoch 80/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 2.2761e-05 - val_loss: 5.7550e-05\n",
-      "Epoch 81/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.5174e-05 - val_loss: 2.4306e-05\n",
-      "Epoch 82/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0468e-05 - val_loss: 5.3055e-06\n",
-      "Epoch 83/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0336e-05 - val_loss: 1.5623e-08\n",
-      "Epoch 84/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.3634e-05 - val_loss: 1.6709e-06\n",
-      "Epoch 85/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.7513e-05 - val_loss: 3.1836e-06\n",
-      "Epoch 86/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 1.9329e-05 - val_loss: 1.8082e-06\n",
-      "Epoch 87/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.8117e-05 - val_loss: 1.3788e-08\n",
-      "Epoch 88/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.4860e-05 - val_loss: 2.6885e-06\n",
-      "Epoch 89/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.1582e-05 - val_loss: 1.2991e-05\n",
-      "Epoch 90/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 9.9947e-06 - val_loss: 2.9656e-05\n",
-      "Epoch 91/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0539e-05 - val_loss: 4.7325e-05\n",
-      "Epoch 92/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.2335e-05 - val_loss: 5.9438e-05\n",
-      "Epoch 93/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.3925e-05 - val_loss: 6.1708e-05\n",
-      "Epoch 94/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.4243e-05 - val_loss: 5.4090e-05\n",
-      "Epoch 95/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.3189e-05 - val_loss: 4.0300e-05\n",
-      "Epoch 96/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.1522e-05 - val_loss: 2.5492e-05\n",
-      "Epoch 97/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0252e-05 - val_loss: 1.3687e-05\n",
-      "Epoch 98/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.9786e-06 - val_loss: 6.3993e-06\n",
-      "Epoch 99/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.0592e-05 - val_loss: 2.9519e-06\n",
-      "Epoch 100/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.1463e-05 - val_loss: 1.8671e-06\n",
-      "Epoch 101/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.1921e-05 - val_loss: 2.2165e-06\n",
-      "Epoch 102/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.1679e-05 - val_loss: 4.0655e-06\n",
-      "Epoch 103/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0944e-05 - val_loss: 7.9283e-06\n",
-      "Epoch 104/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0205e-05 - val_loss: 1.3821e-05\n",
-      "Epoch 105/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.8716e-06 - val_loss: 2.0695e-05\n",
-      "Epoch 106/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0032e-05 - val_loss: 2.6674e-05\n",
-      "Epoch 107/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.0446e-05 - val_loss: 2.9913e-05\n",
-      "Epoch 108/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.0759e-05 - val_loss: 2.9500e-05\n",
-      "Epoch 109/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 1.0747e-05 - val_loss: 2.5844e-05\n",
-      "Epoch 110/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0443e-05 - val_loss: 2.0358e-05\n",
-      "Epoch 111/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0066e-05 - val_loss: 1.4707e-05\n",
-      "Epoch 112/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8486e-06 - val_loss: 1.0118e-05\n",
-      "Epoch 113/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8802e-06 - val_loss: 7.0882e-06\n",
-      "Epoch 114/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0072e-05 - val_loss: 5.5600e-06\n",
-      "Epoch 115/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0249e-05 - val_loss: 5.2934e-06\n",
-      "Epoch 116/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 1.0277e-05 - val_loss: 6.1254e-06\n",
-      "Epoch 117/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0149e-05 - val_loss: 7.9791e-06\n",
-      "Epoch 118/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.9651e-06 - val_loss: 1.0667e-05\n",
-      "Epoch 119/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8466e-06 - val_loss: 1.3735e-05\n",
-      "Epoch 120/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8504e-06 - val_loss: 1.6485e-05\n",
-      "Epoch 121/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.9414e-06 - val_loss: 1.8216e-05\n",
-      "Epoch 122/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0032e-05 - val_loss: 1.8516e-05\n",
-      "Epoch 123/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 1.0051e-05 - val_loss: 1.7417e-05\n",
-      "Epoch 124/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.9901e-06 - val_loss: 1.5359e-05\n",
-      "Epoch 125/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.8990e-06 - val_loss: 1.2963e-05\n",
-      "Epoch 126/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.8395e-06 - val_loss: 1.0794e-05\n",
-      "Epoch 127/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8410e-06 - val_loss: 9.2127e-06\n",
-      "Epoch 128/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8856e-06 - val_loss: 8.3720e-06\n",
-      "Epoch 129/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.9287e-06 - val_loss: 8.2850e-06\n",
-      "Epoch 130/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.9349e-06 - val_loss: 8.8960e-06\n",
-      "Epoch 131/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.9022e-06 - val_loss: 1.0086e-05\n",
-      "Epoch 132/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.8567e-06 - val_loss: 1.1645e-05\n",
-      "Epoch 133/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 9.8290e-06 - val_loss: 1.3271e-05\n",
-      "Epoch 134/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.8325e-06 - val_loss: 1.4617e-05\n",
-      "Epoch 135/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.8556e-06 - val_loss: 1.5394e-05\n",
-      "Epoch 136/150\n",
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.8750e-06 - val_loss: 1.5461e-05\n",
-      "Epoch 137/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 9.8746e-06 - val_loss: 1.4877e-05\n",
-      "Epoch 138/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8558e-06 - val_loss: 1.3863e-05\n",
-      "Epoch 139/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 9.8336e-06 - val_loss: 1.2705e-05\n",
-      "Epoch 140/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8227e-06 - val_loss: 1.1674e-05\n",
-      "Epoch 141/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8274e-06 - val_loss: 1.0959e-05\n",
-      "Epoch 142/150\n",
-      "1/1 [==============================] - 0s 27ms/step - loss: 9.8396e-06 - val_loss: 1.0660e-05\n",
-      "Epoch 143/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8474e-06 - val_loss: 1.0788e-05\n",
-      "Epoch 144/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8444e-06 - val_loss: 1.1292e-05\n",
-      "Epoch 145/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8337e-06 - val_loss: 1.2054e-05\n",
-      "Epoch 146/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 9.8234e-06 - val_loss: 1.2912e-05\n",
-      "Epoch 147/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8202e-06 - val_loss: 1.3680e-05\n",
-      "Epoch 148/150\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 28ms/step - loss: 9.8241e-06 - val_loss: 1.4195e-05\n",
-      "Epoch 149/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8303e-06 - val_loss: 1.4361e-05\n",
-      "Epoch 150/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 9.8323e-06 - val_loss: 1.4168e-05\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MSE:  0.0001688185180222488\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11679/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
-      "  next_input = np.array([[last, next[0]]], dtype=np.float64)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  8.960891042000014\n"
-     ]
-    }
-   ],
+   "execution_count": 12,
+   "id": "7030f585",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "def test_rnn (x1, y_test, plot_min, plot_max):\n",
     "    \"\"\"\n",
@@ -2107,8 +1646,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4711b0d4",
-   "metadata": {},
+   "id": "53dc1510",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other Things to Try\n",
     "\n",
@@ -2126,382 +1667,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "22123a0f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model_1\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_2 (InputLayer)        [(None, 2, 1)]            0         \n",
-      "                                                                 \n",
-      " RNN1 (SimpleRNN)            (None, 2, 500)            251000    \n",
-      "                                                                 \n",
-      " RNN2 (SimpleRNN)            (None, 500)               500500    \n",
-      "                                                                 \n",
-      " dense (Dense)               (None, 1)                 501       \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 752,001\n",
-      "Trainable params: 752,001\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/150\n",
-      "1/1 [==============================] - 3s 3s/step - loss: 2.3905 - val_loss: 2.6248\n",
-      "Epoch 2/150\n",
-      "1/1 [==============================] - 0s 43ms/step - loss: 4.2149 - val_loss: 1.0031\n",
-      "Epoch 3/150\n",
-      "1/1 [==============================] - 0s 43ms/step - loss: 2.0713 - val_loss: 0.1563\n",
-      "Epoch 4/150\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 0.0429 - val_loss: 2.2720\n",
-      "Epoch 5/150\n",
-      "1/1 [==============================] - 0s 46ms/step - loss: 1.2154 - val_loss: 3.1357\n",
-      "Epoch 6/150\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 1.8554 - val_loss: 1.8682\n",
-      "Epoch 7/150\n",
-      "1/1 [==============================] - 0s 47ms/step - loss: 0.9310 - val_loss: 0.4494\n",
-      "Epoch 8/150\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 0.1030 - val_loss: 4.2951e-08\n",
-      "Epoch 9/150\n",
-      "1/1 [==============================] - 0s 60ms/step - loss: 0.2212 - val_loss: 0.1764\n",
-      "Epoch 10/150\n",
-      "1/1 [==============================] - 0s 48ms/step - loss: 0.7531 - val_loss: 0.2583\n",
-      "Epoch 11/150\n",
-      "1/1 [==============================] - 0s 52ms/step - loss: 0.9096 - val_loss: 0.0959\n",
-      "Epoch 12/150\n",
-      "1/1 [==============================] - 0s 51ms/step - loss: 0.5790 - val_loss: 0.0046\n",
-      "Epoch 13/150\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 0.1685 - val_loss: 0.2457\n",
-      "Epoch 14/150\n",
-      "1/1 [==============================] - 0s 46ms/step - loss: 0.0473 - val_loss: 0.7292\n",
-      "Epoch 15/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.2274 - val_loss: 1.1137\n",
-      "Epoch 16/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.4414 - val_loss: 1.1417\n",
-      "Epoch 17/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.4583 - val_loss: 0.8415\n",
-      "Epoch 18/150\n",
-      "1/1 [==============================] - 0s 47ms/step - loss: 0.2861 - val_loss: 0.4378\n",
-      "Epoch 19/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.0989 - val_loss: 0.1418\n",
-      "Epoch 20/150\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 0.0441 - val_loss: 0.0178\n",
-      "Epoch 21/150\n",
-      "1/1 [==============================] - 0s 47ms/step - loss: 0.1257 - val_loss: 2.0294e-04\n",
-      "Epoch 22/150\n",
-      "1/1 [==============================] - 0s 45ms/step - loss: 0.2330 - val_loss: 0.0019\n",
-      "Epoch 23/150\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 0.2590 - val_loss: 0.0016\n",
-      "Epoch 24/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.1885 - val_loss: 0.0415\n",
-      "Epoch 25/150\n",
-      "1/1 [==============================] - 0s 45ms/step - loss: 0.0899 - val_loss: 0.1619\n",
-      "Epoch 26/150\n",
-      "1/1 [==============================] - 0s 48ms/step - loss: 0.0423 - val_loss: 0.3437\n",
-      "Epoch 27/150\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 0.0687 - val_loss: 0.5095\n",
-      "Epoch 28/150\n",
-      "1/1 [==============================] - 0s 43ms/step - loss: 0.1266 - val_loss: 0.5778\n",
-      "Epoch 29/150\n",
-      "1/1 [==============================] - 0s 46ms/step - loss: 0.1558 - val_loss: 0.5216\n",
-      "Epoch 30/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.1317 - val_loss: 0.3810\n",
-      "Epoch 31/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.0798 - val_loss: 0.2271\n",
-      "Epoch 32/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.0448 - val_loss: 0.1133\n",
-      "Epoch 33/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0492 - val_loss: 0.0531\n",
-      "Epoch 34/150\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 0.0787 - val_loss: 0.0326\n",
-      "Epoch 35/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.1003 - val_loss: 0.0373\n",
-      "Epoch 36/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0943 - val_loss: 0.0676\n",
-      "Epoch 37/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0681 - val_loss: 0.1296\n",
-      "Epoch 38/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0457 - val_loss: 0.2170\n",
-      "Epoch 39/150\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 0.0437 - val_loss: 0.3036\n",
-      "Epoch 40/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0584 - val_loss: 0.3554\n",
-      "Epoch 41/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0721 - val_loss: 0.3522\n",
-      "Epoch 42/150\n",
-      "1/1 [==============================] - 0s 38ms/step - loss: 0.0712 - val_loss: 0.2999\n",
-      "Epoch 43/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0576 - val_loss: 0.2249\n",
-      "Epoch 44/150\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "Epoch 75/150\n",
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-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MSE:  0.33032583548035827\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11679/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
-      "  next_input = np.array([[last, next[0]]], dtype=np.float64)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  10.101145834000022\n"
-     ]
-    }
-   ],
+   "execution_count": 13,
+   "id": "62aa2c1c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "def rnn_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
     "    \"\"\"\n",
@@ -2593,8 +1765,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50cd4d20",
-   "metadata": {},
+   "id": "56fb2d92",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other Types of Recurrent Neural Networks\n",
     "\n",
@@ -2616,740 +1790,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "5c3d5a53",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model_2\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_3 (InputLayer)        [(None, 2, 1)]            0         \n",
-      "                                                                 \n",
-      " dnn (Dense)                 (None, 2, 125)            250       \n",
-      "                                                                 \n",
-      " dnn1 (Dense)                (None, 2, 125)            15750     \n",
-      "                                                                 \n",
-      " RNN1 (GRU)                  (None, 2, 250)            282750    \n",
-      "                                                                 \n",
-      " RNN (GRU)                   (None, 250)               376500    \n",
-      "                                                                 \n",
-      " dense (Dense)               (None, 1)                 251       \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 675,501\n",
-      "Trainable params: 675,501\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/150\n",
-      "1/1 [==============================] - 10s 10s/step - loss: 0.2483 - val_loss: 0.6297\n",
-      "Epoch 2/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.1954 - val_loss: 0.4846\n",
-      "Epoch 3/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.1452 - val_loss: 0.3423\n",
-      "Epoch 4/150\n",
-      "1/1 [==============================] - 0s 36ms/step - loss: 0.0972 - val_loss: 0.2069\n",
-      "Epoch 5/150\n",
-      "1/1 [==============================] - 0s 46ms/step - loss: 0.0536 - val_loss: 0.0905\n",
-      "Epoch 6/150\n",
-      "1/1 [==============================] - 0s 38ms/step - loss: 0.0197 - val_loss: 0.0152\n",
-      "Epoch 7/150\n",
-      "1/1 [==============================] - 0s 43ms/step - loss: 0.0045 - val_loss: 0.0037\n",
-      "Epoch 8/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0152 - val_loss: 0.0381\n",
-      "Epoch 9/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.0395 - val_loss: 0.0573\n",
-      "Epoch 10/150\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 0.0501 - val_loss: 0.0436\n",
-      "Epoch 11/150\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 0.0416 - val_loss: 0.0177\n",
-      "Epoch 12/150\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 0.0248 - val_loss: 0.0014\n",
-      "Epoch 13/150\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 0.0105 - val_loss: 0.0037\n",
-      "Epoch 14/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0039 - val_loss: 0.0215\n",
-      "Epoch 15/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.0045 - val_loss: 0.0456\n",
-      "Epoch 16/150\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 0.0094 - val_loss: 0.0669\n",
-      "Epoch 17/150\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 0.0148 - val_loss: 0.0796\n",
-      "Epoch 18/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.0184 - val_loss: 0.0813\n",
-      "Epoch 19/150\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 0.0190 - val_loss: 0.0728\n",
-      "Epoch 20/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0168 - val_loss: 0.0569\n",
-      "Epoch 21/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0126 - val_loss: 0.0377\n",
-      "Epoch 22/150\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 0.0078 - val_loss: 0.0195\n",
-      "Epoch 23/150\n",
-      "1/1 [==============================] - 0s 37ms/step - loss: 0.0040 - val_loss: 0.0064\n",
-      "Epoch 24/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.0023 - val_loss: 5.0631e-04\n",
-      "Epoch 25/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.0031 - val_loss: 6.7572e-04\n",
-      "Epoch 26/150\n",
-      "1/1 [==============================] - 0s 51ms/step - loss: 0.0055 - val_loss: 0.0034\n",
-      "Epoch 27/150\n",
-      "1/1 [==============================] - 0s 36ms/step - loss: 0.0079 - val_loss: 0.0048\n",
-      "Epoch 28/150\n",
-      "1/1 [==============================] - 0s 37ms/step - loss: 0.0087 - val_loss: 0.0035\n",
-      "Epoch 29/150\n",
-      "1/1 [==============================] - 0s 37ms/step - loss: 0.0074 - val_loss: 0.0011\n",
-      "Epoch 30/150\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 0.0049 - val_loss: 7.6232e-06\n",
-      "Epoch 31/150\n",
-      "1/1 [==============================] - 0s 38ms/step - loss: 0.0026 - val_loss: 0.0018\n",
-      "Epoch 32/150\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 0.0014 - val_loss: 0.0061\n",
-      "Epoch 33/150\n",
-      "1/1 [==============================] - 0s 36ms/step - loss: 0.0015 - val_loss: 0.0114\n",
-      "Epoch 34/150\n",
-      "1/1 [==============================] - 0s 37ms/step - loss: 0.0024 - val_loss: 0.0157\n",
-      "Epoch 35/150\n",
-      "1/1 [==============================] - 0s 38ms/step - loss: 0.0034 - val_loss: 0.0174\n",
-      "Epoch 36/150\n",
-      "1/1 [==============================] - 0s 37ms/step - loss: 0.0038 - val_loss: 0.0161\n",
-      "Epoch 37/150\n",
-      "1/1 [==============================] - 0s 36ms/step - loss: 0.0036 - val_loss: 0.0124\n",
-      "Epoch 38/150\n",
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-     ]
-    },
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-     "output_type": "stream",
-     "text": [
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-     ]
-    },
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-     "text": [
-      "1/1 [==============================] - 0s 37ms/step - loss: 1.3078e-05 - val_loss: 5.8127e-05\n",
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-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11679/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
-      "  next_input = np.array([[last, next[0]]], dtype=np.float64)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MSE:  0.0025297125391877074\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  18.10655679099989\n",
-      "Model: \"model_3\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_4 (InputLayer)        [(None, 1, 1)]            0         \n",
-      "                                                                 \n",
-      " RNN (SimpleRNN)             (None, 200)               40400     \n",
-      "                                                                 \n",
-      " dense (Dense)               (None, 1)                 201       \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 40,601\n",
-      "Trainable params: 40,601\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/150\n",
-      "1/1 [==============================] - 3s 3s/step - loss: 2.4544 - val_loss: 4.6542\n",
-      "Epoch 2/150\n",
-      "1/1 [==============================] - 0s 37ms/step - loss: 1.8163 - val_loss: 3.5777\n",
-      "Epoch 3/150\n",
-      "1/1 [==============================] - 0s 47ms/step - loss: 1.2813 - val_loss: 2.6541\n",
-      "Epoch 4/150\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 0.8500 - val_loss: 1.8841\n",
-      "Epoch 5/150\n",
-      "1/1 [==============================] - 0s 40ms/step - loss: 0.5206 - val_loss: 1.2650\n",
-      "Epoch 6/150\n",
-      "1/1 [==============================] - 0s 33ms/step - loss: 0.2887 - val_loss: 0.7903\n",
-      "Epoch 7/150\n",
-      "1/1 [==============================] - 0s 38ms/step - loss: 0.1465 - val_loss: 0.4481\n",
-      "Epoch 8/150\n",
-      "1/1 [==============================] - 0s 36ms/step - loss: 0.0819 - val_loss: 0.2213\n",
-      "Epoch 9/150\n",
-      "1/1 [==============================] - 0s 32ms/step - loss: 0.0787 - val_loss: 0.0878\n",
-      "Epoch 10/150\n",
-      "1/1 [==============================] - 0s 30ms/step - loss: 0.1177 - val_loss: 0.0228\n",
-      "Epoch 11/150\n",
-      "1/1 [==============================] - 0s 31ms/step - loss: 0.1785 - val_loss: 0.0014\n",
-      "Epoch 12/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 0.2423 - val_loss: 0.0021\n",
-      "Epoch 13/150\n",
-      "1/1 [==============================] - 0s 29ms/step - loss: 0.2946 - val_loss: 0.0093\n",
-      "Epoch 14/150\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  9.673238541000046\n"
-     ]
-    }
-   ],
+   "execution_count": 14,
+   "id": "145ed525",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "def lstm_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
     "    \"\"\"\n",
@@ -3542,35 +1989,9 @@
     "end = timer()\n",
     "print('Time: ', end-start)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d23615b1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.10"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/doc/src/week45/week45.do.txt b/doc/src/week45/week45.do.txt
index 24372bc18..fdf82c0c3 100644
--- a/doc/src/week45/week45.do.txt
+++ b/doc/src/week45/week45.do.txt
@@ -18,6 +18,8 @@ DATE: November 6-10
   * Recurrent  Neural Networks (RNNs)
   * Readings and Videos:
     * 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".
     * "Video  on Recurrent Neural Networks from MIT":"https://www.youtube.com/watch?v=SEnXr6v2ifU&ab_channel=AlexanderAmini"