diff --git a/doc/pub/week41/html/._week41-bs002.html b/doc/pub/week41/html/._week41-bs002.html
index 56a81ba6d..e40d78038 100644
--- a/doc/pub/week41/html/._week41-bs002.html
+++ b/doc/pub/week41/html/._week41-bs002.html
@@ -342,22 +342,30 @@ MathJax.Hub.Config({
Material for the lecture on Monday October 7, 2024
-
+
- Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
- Building our own Feed-forward Neural Network
-- Readings and Videos:
-
-
-I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at http://neuralnetworksanddeeplearning.com/chap4.html.
+
+
+
diff --git a/doc/pub/week41/html/week41-reveal.html b/doc/pub/week41/html/week41-reveal.html
index 5ab4c69ff..e390a2e4b 100644
--- a/doc/pub/week41/html/week41-reveal.html
+++ b/doc/pub/week41/html/week41-reveal.html
@@ -197,30 +197,31 @@ MathJax.Hub.Config({
Material for the lecture on Monday October 7, 2024
-
diff --git a/doc/pub/week41/html/week41-solarized.html b/doc/pub/week41/html/week41-solarized.html
index 46fb7e9da..1d336f81a 100644
--- a/doc/pub/week41/html/week41-solarized.html
+++ b/doc/pub/week41/html/week41-solarized.html
@@ -298,22 +298,29 @@ MathJax.Hub.Config({
Material for the lecture on Monday October 7, 2024
-
+
- Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
- Building our own Feed-forward Neural Network
-- Readings and Videos:
-
-
-I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at http://neuralnetworksanddeeplearning.com/chap4.html.
+
+
+
Readings and Videos:
+
+
+- These lecture notes
+- Rashcka et al chapter 11
+- For neural networks we recommend Goodfellow et al chapter 6.
+
+ - Neural Networks demystified at https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs
+
+
Building Neural Networks from scratch at https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex
+
Video on Neural Networks at https://www.youtube.com/watch?v=CqOfi41LfDw
+
Video on the back propagation algorithm at https://www.youtube.com/watch?v=Ilg3gGewQ5U
+
+
We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at http://neuralnetworksanddeeplearning.com/chap4.html.
+
Material for the active learning sessions on Tuesday and Wednesday
diff --git a/doc/pub/week41/html/week41.html b/doc/pub/week41/html/week41.html
index c2478214d..3acd69a81 100644
--- a/doc/pub/week41/html/week41.html
+++ b/doc/pub/week41/html/week41.html
@@ -375,22 +375,29 @@ MathJax.Hub.Config({
Material for the lecture on Monday October 7, 2024
-
+
- Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
- Building our own Feed-forward Neural Network
-- Readings and Videos:
-
-
-I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at http://neuralnetworksanddeeplearning.com/chap4.html.
+
+
+
Readings and Videos:
+
+
+- These lecture notes
+- Rashcka et al chapter 11
+- For neural networks we recommend Goodfellow et al chapter 6.
+
+ - Neural Networks demystified at https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs
+
+
Building Neural Networks from scratch at https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex
+
Video on Neural Networks at https://www.youtube.com/watch?v=CqOfi41LfDw
+
Video on the back propagation algorithm at https://www.youtube.com/watch?v=Ilg3gGewQ5U
+
+
We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at http://neuralnetworksanddeeplearning.com/chap4.html.
+
Material for the active learning sessions on Tuesday and Wednesday
diff --git a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz
index b41fbe87e..b7b358977 100644
Binary files a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz and b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz differ
diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb
index 7fa78c784..3def9b7f3 100644
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"## Material for the lecture on Monday October 7, 2024\n",
- "* Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.\n",
+ "1. Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.\n",
"\n",
- "* Building our own Feed-forward Neural Network\n",
+ "2. Building our own Feed-forward Neural Network\n",
"\n",
"\n",
"\n",
- "* Readings and Videos:\n",
+ "**Readings and Videos:**\n",
"\n",
- " * These lecture notes\n",
+ "1. These lecture notes\n",
"\n",
- " * For neural networks we recommend Goodfellow et al chapter 6.\n",
+ "2. Rashcka et al chapter 11 \n",
"\n",
- " * [Neural Networks demystified](https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs)\n",
+ "3. For neural networks we recommend Goodfellow et al chapter 6.\n",
"\n",
- " * [Building Neural Networks from scratch](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex)\n",
+ "a. Neural Networks demystified at \n",
"\n",
- " * [Video on Neural Networks](https://www.youtube.com/watch?v=CqOfi41LfDw)\n",
+ "2. Building Neural Networks from scratch at \n",
"\n",
- " * [Video on the back propagation algorithm](https://www.youtube.com/watch?v=Ilg3gGewQ5U)\n",
+ "3. Video on Neural Networks at \n",
"\n",
- "I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at ."
+ "4. Video on the back propagation algorithm at \n",
+ "\n",
+ "We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at ."
]
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"cell_type": "markdown",
- "id": "88011cdc",
+ "id": "d0ef37e8",
"metadata": {
"editable": true
},
@@ -3452,7 +3454,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "e8222862",
+ "id": "d2891643",
"metadata": {
"collapsed": false,
"editable": true
@@ -3479,7 +3481,7 @@
},
{
"cell_type": "markdown",
- "id": "b1e2e538",
+ "id": "5c91c8fc",
"metadata": {
"editable": true
},
@@ -3493,7 +3495,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "80b01b3a",
+ "id": "c245d23d",
"metadata": {
"collapsed": false,
"editable": true
@@ -3524,7 +3526,7 @@
},
{
"cell_type": "markdown",
- "id": "b376cce8",
+ "id": "ea3be61a",
"metadata": {
"editable": true
},
@@ -3535,7 +3537,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "a8195066",
+ "id": "d26e1748",
"metadata": {
"collapsed": false,
"editable": true
@@ -3579,7 +3581,7 @@
},
{
"cell_type": "markdown",
- "id": "836934f9",
+ "id": "572f3f4b",
"metadata": {
"editable": true
},
@@ -3602,7 +3604,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "6d537674",
+ "id": "fdb38053",
"metadata": {
"collapsed": false,
"editable": true
@@ -3629,7 +3631,7 @@
},
{
"cell_type": "markdown",
- "id": "4e6fd391",
+ "id": "5fc3dd13",
"metadata": {
"editable": true
},
@@ -3640,7 +3642,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "8760d8af",
+ "id": "cd9e4505",
"metadata": {
"collapsed": false,
"editable": true
@@ -3685,7 +3687,7 @@
},
{
"cell_type": "markdown",
- "id": "36c08f07",
+ "id": "30d61094",
"metadata": {
"editable": true
},
@@ -3712,7 +3714,7 @@
},
{
"cell_type": "markdown",
- "id": "8ed8a9d1",
+ "id": "9b4cd63f",
"metadata": {
"editable": true
},
@@ -3750,7 +3752,7 @@
},
{
"cell_type": "markdown",
- "id": "58af02d9",
+ "id": "1a2bd6f1",
"metadata": {
"editable": true
},
@@ -3762,7 +3764,7 @@
},
{
"cell_type": "markdown",
- "id": "669b626a",
+ "id": "a569e669",
"metadata": {
"editable": true
},
@@ -3777,7 +3779,7 @@
},
{
"cell_type": "markdown",
- "id": "58f23d44",
+ "id": "51363b82",
"metadata": {
"editable": true
},
@@ -3787,7 +3789,7 @@
},
{
"cell_type": "markdown",
- "id": "0e16989f",
+ "id": "e4172d21",
"metadata": {
"editable": true
},
@@ -3800,7 +3802,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "62970cc9",
+ "id": "b8640651",
"metadata": {
"collapsed": false,
"editable": true
@@ -3876,7 +3878,7 @@
},
{
"cell_type": "markdown",
- "id": "a187ce27",
+ "id": "f6b2c036",
"metadata": {
"editable": true
},
@@ -3886,7 +3888,7 @@
},
{
"cell_type": "markdown",
- "id": "7e5cc157",
+ "id": "49fdf701",
"metadata": {
"editable": true
},
@@ -3897,7 +3899,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "2f28ce5f",
+ "id": "45dae979",
"metadata": {
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"editable": true
diff --git a/doc/src/week41/week41.do.txt b/doc/src/week41/week41.do.txt
index 40591b038..1be02d626 100644
--- a/doc/src/week41/week41.do.txt
+++ b/doc/src/week41/week41.do.txt
@@ -8,19 +8,20 @@ DATE: Week 41
!split
===== Material for the lecture on Monday October 7, 2024 =====
-* Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
-* Building our own Feed-forward Neural Network
+o Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
+o Building our own Feed-forward Neural Network
# * "Video of lecture notes":"https://youtu.be/5-RRTO9uDvI"
# * "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf"
-* Readings and Videos:
- * These lecture notes
- * For neural networks we recommend Goodfellow et al chapter 6.
- * "Neural Networks demystified":"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
- * "Building Neural Networks from scratch":"https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
- * "Video on Neural Networks":"https://www.youtube.com/watch?v=CqOfi41LfDw"
- * "Video on the back propagation algorithm":"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
-I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at URL:"http://neuralnetworksanddeeplearning.com/chap4.html".
-
+!bblock Readings and Videos:
+ o These lecture notes
+ o Rashcka et al chapter 11
+ o For neural networks we recommend Goodfellow et al chapter 6.
+ o Neural Networks demystified at URL:"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
+ o Building Neural Networks from scratch at URL:"https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
+ o Video on Neural Networks at URL:"https://www.youtube.com/watch?v=CqOfi41LfDw"
+ o Video on the back propagation algorithm at URL:"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
+We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at URL:"http://neuralnetworksanddeeplearning.com/chap4.html".
+!eblock
!split