From 988c2b759722ae08037b6a03bb61135424975c84 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Fri, 8 Oct 2021 14:26:53 +0200
Subject: [PATCH] added video

---
 doc/pub/week40/html/._week40-bs001.html      |   5 +
 doc/pub/week40/html/week40-reveal.html       |   6 +
 doc/pub/week40/html/week40-solarized.html    |   5 +
 doc/pub/week40/html/week40.html              |   5 +
 doc/pub/week40/ipynb/ipynb-week40-src.tar.gz | Bin 33808 -> 33808 bytes
 doc/pub/week40/ipynb/week40.ipynb            |   3 +
 doc/pub/week41/ipynb/week41.ipynb            | 124 +++++++------------
 doc/src/week40/week40.do.txt                 |   2 +-
 8 files changed, 70 insertions(+), 80 deletions(-)

diff --git a/doc/pub/week40/html/._week40-bs001.html b/doc/pub/week40/html/._week40-bs001.html
index 740be79eb..682a4d5fa 100644
--- a/doc/pub/week40/html/._week40-bs001.html
+++ b/doc/pub/week40/html/._week40-bs001.html
@@ -329,6 +329,11 @@ MathJax.Hub.Config({
 </ul>
 
 <li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li> 
+
+<ul>
+  <li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage" target="_self">Video of Lecture</a></li>
+</ul>
+
 </ul>
 
 Reading suggestions for both days: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_self">Aurelien Geron's chapter 10</a> and Hastie et al chapter 11.
diff --git a/doc/pub/week40/html/week40-reveal.html b/doc/pub/week40/html/week40-reveal.html
index 32a4d35b8..bb9639c9a 100644
--- a/doc/pub/week40/html/week40-reveal.html
+++ b/doc/pub/week40/html/week40-reveal.html
@@ -169,6 +169,12 @@ MathJax.Hub.Config({
 <p><li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
 </ul>
 <p><li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li> 
+
+<ul>
+
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
+</ul>
+<p>
 </ul>
 <p>
 
diff --git a/doc/pub/week40/html/week40-solarized.html b/doc/pub/week40/html/week40-solarized.html
index bfcf09ac9..5216c674b 100644
--- a/doc/pub/week40/html/week40-solarized.html
+++ b/doc/pub/week40/html/week40-solarized.html
@@ -278,6 +278,11 @@ MathJax.Hub.Config({
 </ul>
 
 <li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li> 
+
+<ul>
+  <li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
+</ul>
+
 </ul>
 
 Reading suggestions for both days: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapter 10</a> and Hastie et al chapter 11.
diff --git a/doc/pub/week40/html/week40.html b/doc/pub/week40/html/week40.html
index 8cdb7fdf3..335199555 100644
--- a/doc/pub/week40/html/week40.html
+++ b/doc/pub/week40/html/week40.html
@@ -283,6 +283,11 @@ MathJax.Hub.Config({
 </ul>
 
 <li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li> 
+
+<ul>
+  <li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
+</ul>
+
 </ul>
 
 Reading suggestions for both days: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapter 10</a> and Hastie et al chapter 11.
diff --git a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
index 506cea50c82ea63b8d7fa50d72c75f9c393e20ac..51eed6eb871f1b7bdbd8f7fcb2e30f6c1b12da45 100644
GIT binary patch
delta 18
ZcmbQx!8D<RiCw;%gMrC1VIw<d3ji@@1kL~e

delta 18
ZcmbQx!8D<RiCw;%gMslz{6==p763IA1#18R

diff --git a/doc/pub/week40/ipynb/week40.ipynb b/doc/pub/week40/ipynb/week40.ipynb
index e1f728064..9b75a5667 100644
--- a/doc/pub/week40/ipynb/week40.ipynb
+++ b/doc/pub/week40/ipynb/week40.ipynb
@@ -26,6 +26,9 @@
     "\n",
     "* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. \n",
     "\n",
+    "  * [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage)\n",
+    "\n",
+    "\n",
     "Reading suggestions for both days: [Aurelien Geron's chapter 10](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf) and Hastie et al chapter 11.\n",
     "For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.\n",
     "For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4\n",
diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb
index e536eff6d..e0af85976 100644
--- a/doc/pub/week41/ipynb/week41.ipynb
+++ b/doc/pub/week41/ipynb/week41.ipynb
@@ -491,9 +491,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -562,9 +560,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -686,9 +682,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# building our neural network\n",
@@ -765,9 +759,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# setup the feed-forward pass, subscript h = hidden layer\n",
@@ -932,9 +924,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# to categorical turns our integer vector into a onehot representation\n",
@@ -1033,9 +1023,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class NeuralNetwork:\n",
@@ -1157,9 +1145,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "epochs = 100\n",
@@ -1193,9 +1179,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "eta_vals = np.logspace(-5, 1, 7)\n",
@@ -1230,9 +1214,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# visual representation of grid search\n",
@@ -1292,9 +1274,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
@@ -1325,9 +1305,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -1410,9 +1388,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pip3 install tensorflow"
@@ -1429,9 +1405,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda create -n tf tensorflow\n",
@@ -1448,9 +1422,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda create -n tf-gpu tensorflow-gpu\n",
@@ -1471,9 +1443,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda install keras"
@@ -1495,9 +1465,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# import necessary packages\n",
@@ -1548,9 +1516,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.keras.layers import Input\n",
@@ -1575,9 +1541,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -1603,9 +1567,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -1628,9 +1590,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -1676,9 +1636,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -2398,9 +2356,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# import necessary packages\n",
@@ -2455,9 +2411,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.keras import datasets, layers, models\n",
@@ -2495,9 +2449,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
@@ -2538,9 +2490,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2571,9 +2521,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# visual representation of grid search\n",
@@ -2620,7 +2568,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/doc/src/week40/week40.do.txt b/doc/src/week40/week40.do.txt
index d6de94ac3..a6740ce89 100644
--- a/doc/src/week40/week40.do.txt
+++ b/doc/src/week40/week40.do.txt
@@ -9,7 +9,7 @@ DATE: today
 * Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. 
   * "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage"
 * Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. 
-
+  * "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage"
 Reading suggestions for both days: "Aurelien Geron's chapter 10":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" and Hastie et al chapter 11.
 For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.
 For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4