diff --git a/doc/pub/week40/html/._week40-bs000.html b/doc/pub/week40/html/._week40-bs000.html
index 37a6339d7..4be7119e1 100644
--- a/doc/pub/week40/html/._week40-bs000.html
+++ b/doc/pub/week40/html/._week40-bs000.html
@@ -254,7 +254,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 2, 2020
+Oct 4, 2020
diff --git a/doc/pub/week40/html/._week40-bs001.html b/doc/pub/week40/html/._week40-bs001.html
index 62302be3b..f86cf0e1a 100644
--- a/doc/pub/week40/html/._week40-bs001.html
+++ b/doc/pub/week40/html/._week40-bs001.html
@@ -239,7 +239,7 @@ MathJax.Hub.Config({
- Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. Video of Lecture
-- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
+- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. Video of Lecture
Reading suggestions for both days: Aurelien Geron's chapter 10 and Hastie et al chapter 11.
diff --git a/doc/pub/week40/html/week40-bs.html b/doc/pub/week40/html/week40-bs.html
index 37a6339d7..4be7119e1 100644
--- a/doc/pub/week40/html/week40-bs.html
+++ b/doc/pub/week40/html/week40-bs.html
@@ -254,7 +254,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 2, 2020
+Oct 4, 2020
diff --git a/doc/pub/week40/html/week40-reveal.html b/doc/pub/week40/html/week40-reveal.html
index 7c4b8e136..f87e18c1d 100644
--- a/doc/pub/week40/html/week40-reveal.html
+++ b/doc/pub/week40/html/week40-reveal.html
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 2, 2020
+Oct 4, 2020
@@ -163,7 +163,7 @@ MathJax.Hub.Config({
- Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. Video of Lecture
-- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
+- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. Video of Lecture
diff --git a/doc/pub/week40/html/week40-solarized.html b/doc/pub/week40/html/week40-solarized.html
index ac34e0b07..384aa97ae 100644
--- a/doc/pub/week40/html/week40-solarized.html
+++ b/doc/pub/week40/html/week40-solarized.html
@@ -186,7 +186,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 2, 2020
+Oct 4, 2020
@@ -195,7 +195,7 @@ MathJax.Hub.Config({
- Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. Video of Lecture
-- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
+- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. Video of Lecture
Reading suggestions for both days: Aurelien Geron's chapter 10 and Hastie et al chapter 11.
diff --git a/doc/pub/week40/html/week40.html b/doc/pub/week40/html/week40.html
index 0bd2193f0..3f0dd9b0c 100644
--- a/doc/pub/week40/html/week40.html
+++ b/doc/pub/week40/html/week40.html
@@ -191,7 +191,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 2, 2020
+Oct 4, 2020
@@ -200,7 +200,7 @@ MathJax.Hub.Config({
- Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. Video of Lecture
-- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
+- Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. Video of Lecture
Reading suggestions for both days: Aurelien Geron's chapter 10 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 69a08dfbf..83b089782 100644
Binary files a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz and b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz differ
diff --git a/doc/pub/week40/ipynb/week40.ipynb b/doc/pub/week40/ipynb/week40.ipynb
index 4c625df87..a37a26969 100644
--- a/doc/pub/week40/ipynb/week40.ipynb
+++ b/doc/pub/week40/ipynb/week40.ipynb
@@ -10,7 +10,7 @@
" \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",
"\n",
- "Date: **Oct 2, 2020**\n",
+ "Date: **Oct 4, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -21,7 +21,7 @@
"\n",
"* 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-STK4155/h20/forelesningsvideoer/LectureOctober1.mp4?vrtx=view-as-webpage) \n",
"\n",
- "* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n",
+ "* 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-STK4155/h20/forelesningsvideoer/LectureOctober2.mp4?vrtx=view-as-webpage) \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",
@@ -142,7 +142,9 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import numpy as np \n",
@@ -204,7 +206,9 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import numpy as np \n",
@@ -243,7 +247,9 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -798,7 +804,9 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -854,7 +862,9 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -890,7 +900,9 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -941,7 +953,9 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -981,7 +995,9 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1013,7 +1029,9 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1072,7 +1090,9 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1096,7 +1116,9 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1143,7 +1165,9 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1171,7 +1195,9 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1199,7 +1225,9 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"import autograd.numpy as np\n",
@@ -1229,7 +1257,9 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"a += b\n",
@@ -1822,7 +1852,9 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"\"\"\"The sigmoid function (or the logistic curve) is a \n",
@@ -2530,25 +2562,7 @@
]
}
],
- "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": "3.6.8"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}