diff --git a/doc/pub/week43/html/week43-bs.html b/doc/pub/week43/html/week43-bs.html
index f35ec26e7..95e708891 100644
--- a/doc/pub/week43/html/week43-bs.html
+++ b/doc/pub/week43/html/week43-bs.html
@@ -473,7 +473,7 @@ MathJax.Hub.Config({
-Oct 19, 2024
+October 21, 2024
@@ -517,8 +517,8 @@ MathJax.Hub.Config({
-- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
-- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
+- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at https://arxiv.org/abs/2105.04026, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
+- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at https://doi.org/10.48550/arXiv.2310.20360
@@ -530,7 +530,7 @@ MathJax.Hub.Config({
@@ -540,15 +540,14 @@ MathJax.Hub.Config({
Reading recommendations
-- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
+- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at https://github.com/rasbt/machine-learning-book. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
- Goodfellow et al, chapter 6 and 7 contain most of the neural network background.
Using Automatic differentiation
-a
-In our discussions of ordinary differential equations
-we will also study the usage of Autograd in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from week 39 and the Autograd documentation.
-t
+
+
In our discussions of ordinary differential equations and neural network codes
+we will also study the usage of Autograd, see for example https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from week 39 and the Autograd documentation at https://github.com/HIPS/autograd.
diff --git a/doc/pub/week43/html/week43-reveal.html b/doc/pub/week43/html/week43-reveal.html
index e5533676d..e5ee3b4c6 100644
--- a/doc/pub/week43/html/week43-reveal.html
+++ b/doc/pub/week43/html/week43-reveal.html
@@ -184,7 +184,7 @@ MathJax.Hub.Config({
-Oct 19, 2024
+October 21, 2024
@@ -234,8 +234,8 @@ MathJax.Hub.Config({
Two recent books online
-- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
-- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
+- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at https://arxiv.org/abs/2105.04026, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
+- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at https://doi.org/10.48550/arXiv.2310.20360
@@ -246,7 +246,7 @@ MathJax.Hub.Config({
@@ -255,17 +255,16 @@ MathJax.Hub.Config({
Reading recommendations
-- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
+- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at https://github.com/rasbt/machine-learning-book. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
- Goodfellow et al, chapter 6 and 7 contain most of the neural network background.
diff --git a/doc/pub/week43/html/week43-solarized.html b/doc/pub/week43/html/week43-solarized.html
index b12aa532f..535d6176e 100644
--- a/doc/pub/week43/html/week43-solarized.html
+++ b/doc/pub/week43/html/week43-solarized.html
@@ -372,7 +372,7 @@ MathJax.Hub.Config({
-Oct 19, 2024
+October 21, 2024
@@ -411,8 +411,8 @@ MathJax.Hub.Config({
Two recent books online
-- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
-- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
+- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at https://arxiv.org/abs/2105.04026, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
+- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at https://doi.org/10.48550/arXiv.2310.20360
@@ -423,7 +423,7 @@ MathJax.Hub.Config({
@@ -432,15 +432,14 @@ MathJax.Hub.Config({
Reading recommendations
-- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
+- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at https://github.com/rasbt/machine-learning-book. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
- Goodfellow et al, chapter 6 and 7 contain most of the neural network background.
Using Automatic differentiation
-a
-In our discussions of ordinary differential equations
-we will also study the usage of Autograd in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from week 39 and the Autograd documentation.
-t
+
+
In our discussions of ordinary differential equations and neural network codes
+we will also study the usage of Autograd, see for example https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from week 39 and the Autograd documentation at https://github.com/HIPS/autograd.
diff --git a/doc/pub/week43/html/week43.html b/doc/pub/week43/html/week43.html
index 6a8e7d251..a41448b95 100644
--- a/doc/pub/week43/html/week43.html
+++ b/doc/pub/week43/html/week43.html
@@ -449,7 +449,7 @@ MathJax.Hub.Config({
-Oct 19, 2024
+October 21, 2024
@@ -488,8 +488,8 @@ MathJax.Hub.Config({
Two recent books online
-- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
-- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
+- The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at https://arxiv.org/abs/2105.04026, published as Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022
+- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at https://doi.org/10.48550/arXiv.2310.20360
@@ -500,7 +500,7 @@ MathJax.Hub.Config({
@@ -509,15 +509,14 @@ MathJax.Hub.Config({
Reading recommendations
-- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
+- Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at https://github.com/rasbt/machine-learning-book. See also chapters 12 and 13 on using Pytorch to make a Neural network code.
- Goodfellow et al, chapter 6 and 7 contain most of the neural network background.
Using Automatic differentiation
-a
-In our discussions of ordinary differential equations
-we will also study the usage of Autograd in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from week 39 and the Autograd documentation.
-t
+
+
In our discussions of ordinary differential equations and neural network codes
+we will also study the usage of Autograd, see for example https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from week 39 and the Autograd documentation at https://github.com/HIPS/autograd.
diff --git a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
index 617aea4c6..379101dcd 100644
Binary files a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz and b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz differ
diff --git a/doc/pub/week43/ipynb/week43.ipynb b/doc/pub/week43/ipynb/week43.ipynb
index 8d1144bc3..97de5a35c 100644
--- a/doc/pub/week43/ipynb/week43.ipynb
+++ b/doc/pub/week43/ipynb/week43.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "5ffa13fd",
+ "id": "a0fb90c6",
"metadata": {
"editable": true
},
@@ -14,7 +14,7 @@
},
{
"cell_type": "markdown",
- "id": "6d4ec5e1",
+ "id": "badbac6f",
"metadata": {
"editable": true
},
@@ -22,14 +22,12 @@
"# Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations\n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n",
"\n",
- "Date: **Oct 19, 2024**\n",
- "\n",
- "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
+ "Date: **October 21, 2024**"
]
},
{
"cell_type": "markdown",
- "id": "78db9d36",
+ "id": "01b07244",
"metadata": {
"editable": true
},
@@ -47,7 +45,7 @@
},
{
"cell_type": "markdown",
- "id": "71e1eeb5",
+ "id": "be20c67e",
"metadata": {
"editable": true
},
@@ -64,7 +62,7 @@
},
{
"cell_type": "markdown",
- "id": "7f7a2fd2",
+ "id": "d0b4a131",
"metadata": {
"editable": true
},
@@ -73,53 +71,52 @@
"\n",
"**Two recent books online.**\n",
"\n",
- "1. [The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen](https://arxiv.org/abs/2105.04026), published as [Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022](https://doi.org/10.1017/9781009025096.002)\n",
+ "1. The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at , published as [Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022](https://doi.org/10.1017/9781009025096.002)\n",
"\n",
- "2. [Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger](https://doi.org/10.48550/arXiv.2310.20360)"
+ "2. Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at "
]
},
{
"cell_type": "markdown",
- "id": "cb7cf4c8",
+ "id": "2b92e08e",
"metadata": {
"editable": true
},
"source": [
"## Reminder on books with hands-on material and codes\n",
- "* [Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch](https://sebastianraschka.com/blog/2022/ml-pytorch-book.html)"
+ "* Sebastian Rashcka et al, Machine learning with Scikit-Learn and PyTorch at "
]
},
{
"cell_type": "markdown",
- "id": "f1d51dcd",
+ "id": "b950e04b",
"metadata": {
"editable": true
},
"source": [
"## Reading recommendations\n",
"\n",
- "1. Rashkca et al., chapter 11, jupyter-notebook sent separately, from [GitHub](https://github.com/rasbt/machine-learning-book). See also chapters 12 and 13 on using Pytorch to make a Neural network code. \n",
+ "1. Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at . See also chapters 12 and 13 on using Pytorch to make a Neural network code. \n",
"\n",
"2. Goodfellow et al, chapter 6 and 7 contain most of the neural network background."
]
},
{
"cell_type": "markdown",
- "id": "564c4073",
+ "id": "cf9b8772",
"metadata": {
"editable": true
},
"source": [
"## Using Automatic differentiation\n",
- "a\n",
- "In our discussions of ordinary differential equations \n",
- "we will also study the usage of [Autograd](https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola) in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from [week 39](https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html) and the [Autograd documentation](https://github.com/HIPS/autograd).\n",
- "t"
+ "\n",
+ "In our discussions of ordinary differential equations and neural network codes\n",
+ "we will also study the usage of Autograd, see for example in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from [week 39](https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html) and the Autograd documentation at ."
]
},
{
"cell_type": "markdown",
- "id": "2033a900",
+ "id": "4c575e2e",
"metadata": {
"editable": true
},
@@ -136,7 +133,7 @@
},
{
"cell_type": "markdown",
- "id": "ba4829f3",
+ "id": "6b085058",
"metadata": {
"editable": true
},
@@ -146,7 +143,7 @@
},
{
"cell_type": "markdown",
- "id": "98da0749",
+ "id": "0a0075b0",
"metadata": {
"editable": true
},
@@ -173,7 +170,7 @@
},
{
"cell_type": "markdown",
- "id": "483e2f2c",
+ "id": "96f13d41",
"metadata": {
"editable": true
},
@@ -196,7 +193,7 @@
},
{
"cell_type": "markdown",
- "id": "b20954e0",
+ "id": "b5207608",
"metadata": {
"editable": true
},
@@ -208,7 +205,7 @@
},
{
"cell_type": "markdown",
- "id": "f5a04ade",
+ "id": "d259bcd5",
"metadata": {
"editable": true
},
@@ -220,7 +217,7 @@
},
{
"cell_type": "markdown",
- "id": "710634c3",
+ "id": "c9394941",
"metadata": {
"editable": true
},
@@ -230,7 +227,7 @@
},
{
"cell_type": "markdown",
- "id": "98263180",
+ "id": "2a843dc8",
"metadata": {
"editable": true
},
@@ -242,7 +239,7 @@
},
{
"cell_type": "markdown",
- "id": "c66e293b",
+ "id": "bfb97adb",
"metadata": {
"editable": true
},
@@ -256,7 +253,7 @@
},
{
"cell_type": "markdown",
- "id": "383b1fae",
+ "id": "c11a76b5",
"metadata": {
"editable": true
},
@@ -268,7 +265,7 @@
},
{
"cell_type": "markdown",
- "id": "628824b3",
+ "id": "c4318d49",
"metadata": {
"editable": true
},
@@ -280,7 +277,7 @@
},
{
"cell_type": "markdown",
- "id": "1c86d973",
+ "id": "13569d4d",
"metadata": {
"editable": true
},
@@ -290,7 +287,7 @@
},
{
"cell_type": "markdown",
- "id": "a296124e",
+ "id": "6012aa12",
"metadata": {
"editable": true
},
@@ -302,7 +299,7 @@
},
{
"cell_type": "markdown",
- "id": "b44a4559",
+ "id": "81f9e9d8",
"metadata": {
"editable": true
},
@@ -314,7 +311,7 @@
},
{
"cell_type": "markdown",
- "id": "0a11151c",
+ "id": "a992f613",
"metadata": {
"editable": true
},
@@ -324,7 +321,7 @@
},
{
"cell_type": "markdown",
- "id": "bf70569b",
+ "id": "3c0d4019",
"metadata": {
"editable": true
},
@@ -336,7 +333,7 @@
},
{
"cell_type": "markdown",
- "id": "731a98b8",
+ "id": "79456495",
"metadata": {
"editable": true
},
@@ -348,7 +345,7 @@
},
{
"cell_type": "markdown",
- "id": "6bf20243",
+ "id": "5a6af59e",
"metadata": {
"editable": true
},
@@ -371,7 +368,7 @@
},
{
"cell_type": "markdown",
- "id": "256d376d",
+ "id": "49cf566c",
"metadata": {
"editable": true
},
@@ -383,7 +380,7 @@
},
{
"cell_type": "markdown",
- "id": "75d41dc3",
+ "id": "ad852edc",
"metadata": {
"editable": true
},
@@ -395,7 +392,7 @@
},
{
"cell_type": "markdown",
- "id": "6d75fa71",
+ "id": "96286962",
"metadata": {
"editable": true
},
@@ -405,7 +402,7 @@
},
{
"cell_type": "markdown",
- "id": "9215eaeb",
+ "id": "729bc2de",
"metadata": {
"editable": true
},
@@ -417,7 +414,7 @@
},
{
"cell_type": "markdown",
- "id": "77867e1c",
+ "id": "fe83e9ae",
"metadata": {
"editable": true
},
@@ -438,7 +435,7 @@
},
{
"cell_type": "markdown",
- "id": "6c553b4c",
+ "id": "30ed8bd3",
"metadata": {
"editable": true
},
@@ -452,7 +449,7 @@
},
{
"cell_type": "markdown",
- "id": "5a3ef5f3",
+ "id": "da73344e",
"metadata": {
"editable": true
},
@@ -464,7 +461,7 @@
},
{
"cell_type": "markdown",
- "id": "470c6fbf",
+ "id": "cb1dbb9d",
"metadata": {
"editable": true
},
@@ -486,7 +483,7 @@
},
{
"cell_type": "markdown",
- "id": "08928465",
+ "id": "936dd159",
"metadata": {
"editable": true
},
@@ -508,7 +505,7 @@
},
{
"cell_type": "markdown",
- "id": "780b8691",
+ "id": "7f709f4f",
"metadata": {
"editable": true
},
@@ -536,7 +533,7 @@
},
{
"cell_type": "markdown",
- "id": "14286855",
+ "id": "60a471f2",
"metadata": {
"editable": true
},
@@ -548,7 +545,7 @@
},
{
"cell_type": "markdown",
- "id": "1817a50c",
+ "id": "6fd10754",
"metadata": {
"editable": true
},
@@ -558,7 +555,7 @@
},
{
"cell_type": "markdown",
- "id": "4e4df109",
+ "id": "94ae419e",
"metadata": {
"editable": true
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@@ -570,7 +567,7 @@
},
{
"cell_type": "markdown",
- "id": "4c115b5c",
+ "id": "374ce974",
"metadata": {
"editable": true
},
@@ -581,7 +578,7 @@
},
{
"cell_type": "markdown",
- "id": "601ad3b6",
+ "id": "3b064a49",
"metadata": {
"editable": true
},
@@ -593,7 +590,7 @@
},
{
"cell_type": "markdown",
- "id": "2f49d2d3",
+ "id": "978a14b7",
"metadata": {
"editable": true
},
@@ -606,7 +603,7 @@
},
{
"cell_type": "markdown",
- "id": "cf6a1725",
+ "id": "20a5cdf1",
"metadata": {
"editable": true
},
@@ -631,7 +628,7 @@
},
{
"cell_type": "markdown",
- "id": "20e5a9a6",
+ "id": "6c12e324",
"metadata": {
"editable": true
},
@@ -644,7 +641,7 @@
},
{
"cell_type": "markdown",
- "id": "ded2cd23",
+ "id": "c9ba5035",
"metadata": {
"editable": true
},
@@ -656,7 +653,7 @@
},
{
"cell_type": "markdown",
- "id": "999d76ec",
+ "id": "02a74952",
"metadata": {
"editable": true
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@@ -668,7 +665,7 @@
},
{
"cell_type": "markdown",
- "id": "97807e16",
+ "id": "b346406f",
"metadata": {
"editable": true
},
@@ -678,7 +675,7 @@
},
{
"cell_type": "markdown",
- "id": "b2f0403b",
+ "id": "6065245f",
"metadata": {
"editable": true
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@@ -690,7 +687,7 @@
},
{
"cell_type": "markdown",
- "id": "a9869137",
+ "id": "186bca89",
"metadata": {
"editable": true
},
@@ -702,7 +699,7 @@
},
{
"cell_type": "markdown",
- "id": "87f77d1c",
+ "id": "b0d95aae",
"metadata": {
"editable": true
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@@ -714,7 +711,7 @@
},
{
"cell_type": "markdown",
- "id": "36039872",
+ "id": "be260340",
"metadata": {
"editable": true
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@@ -726,7 +723,7 @@
},
{
"cell_type": "markdown",
- "id": "7547e347",
+ "id": "ad4e82f5",
"metadata": {
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@@ -736,7 +733,7 @@
},
{
"cell_type": "markdown",
- "id": "83619bba",
+ "id": "884acdf9",
"metadata": {
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@@ -748,7 +745,7 @@
},
{
"cell_type": "markdown",
- "id": "b2808e9d",
+ "id": "9a8c0d3f",
"metadata": {
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@@ -758,7 +755,7 @@
},
{
"cell_type": "markdown",
- "id": "59c51a30",
+ "id": "2e3bb7ce",
"metadata": {
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@@ -770,7 +767,7 @@
},
{
"cell_type": "markdown",
- "id": "719a43ce",
+ "id": "422c9cc8",
"metadata": {
"editable": true
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@@ -783,7 +780,7 @@
},
{
"cell_type": "markdown",
- "id": "972a8958",
+ "id": "1fb9e1e3",
"metadata": {
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@@ -795,7 +792,7 @@
},
{
"cell_type": "markdown",
- "id": "e7232927",
+ "id": "6e7f75ca",
"metadata": {
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@@ -805,7 +802,7 @@
},
{
"cell_type": "markdown",
- "id": "e5152f7e",
+ "id": "aa854a68",
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},
@@ -817,7 +814,7 @@
},
{
"cell_type": "markdown",
- "id": "99dc0aff",
+ "id": "d436ff02",
"metadata": {
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@@ -828,7 +825,7 @@
},
{
"cell_type": "markdown",
- "id": "27d41be2",
+ "id": "a65598d0",
"metadata": {
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@@ -840,7 +837,7 @@
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@@ -850,7 +847,7 @@
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@@ -862,7 +859,7 @@
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@@ -883,7 +880,7 @@
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@@ -896,7 +893,7 @@
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@@ -940,7 +937,7 @@
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@@ -1603,7 +1600,7 @@
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@@ -1767,7 +1764,7 @@
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@@ -1917,7 +1914,7 @@
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@@ -1944,7 +1941,7 @@
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@@ -1955,7 +1952,7 @@
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@@ -2000,7 +1997,7 @@
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@@ -2018,7 +2015,7 @@
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@@ -2307,7 +2304,7 @@
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@@ -2360,7 +2357,7 @@
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@@ -2537,7 +2534,7 @@
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@@ -2578,7 +2575,7 @@
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@@ -2719,7 +2716,7 @@
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@@ -2735,7 +2732,7 @@
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@@ -2748,7 +2745,7 @@
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@@ -2760,7 +2757,7 @@
{
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@@ -2782,7 +2779,7 @@
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@@ -2836,7 +2833,7 @@
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@@ -2944,7 +2941,7 @@
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@@ -3476,7 +3473,7 @@
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@@ -3532,7 +3529,7 @@
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@@ -3548,7 +3545,7 @@
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@@ -3563,7 +3560,7 @@
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@@ -3574,7 +3571,7 @@
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@@ -3589,7 +3586,7 @@
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@@ -3605,7 +3602,7 @@
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@@ -3799,7 +3796,7 @@
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@@ -3886,7 +3883,7 @@
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@@ -3903,7 +3900,7 @@
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@@ -3931,7 +3928,7 @@
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@@ -3970,7 +3967,7 @@
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@@ -3987,7 +3984,7 @@
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@@ -3999,7 +3996,7 @@
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@@ -4027,7 +4024,7 @@
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@@ -6511,7 +6508,7 @@
},
{
"cell_type": "markdown",
- "id": "f39cecbb",
+ "id": "ff28d51f",
"metadata": {
"editable": true
},
@@ -6527,7 +6524,7 @@
},
{
"cell_type": "markdown",
- "id": "057ea99d",
+ "id": "da5421f4",
"metadata": {
"editable": true
},
@@ -6537,7 +6534,7 @@
},
{
"cell_type": "markdown",
- "id": "30c9bec6",
+ "id": "72526baf",
"metadata": {
"editable": true
},
@@ -6549,7 +6546,7 @@
},
{
"cell_type": "markdown",
- "id": "80ac7a5f",
+ "id": "b9ae6a09",
"metadata": {
"editable": true
},
@@ -6566,7 +6563,7 @@
},
{
"cell_type": "markdown",
- "id": "b3bd36b5",
+ "id": "729ce8ab",
"metadata": {
"editable": true
},
@@ -6576,7 +6573,7 @@
},
{
"cell_type": "markdown",
- "id": "b9a70d98",
+ "id": "c7792bbb",
"metadata": {
"editable": true
},
@@ -6592,7 +6589,7 @@
},
{
"cell_type": "markdown",
- "id": "c1b83cb1",
+ "id": "5de3defc",
"metadata": {
"editable": true
},
@@ -6606,7 +6603,7 @@
},
{
"cell_type": "markdown",
- "id": "5319976d",
+ "id": "c0dfe6ce",
"metadata": {
"editable": true
},
@@ -6625,7 +6622,7 @@
{
"cell_type": "code",
"execution_count": 47,
- "id": "e0ce7303",
+ "id": "24b4cb4e",
"metadata": {
"collapsed": false,
"editable": true
@@ -6680,7 +6677,7 @@
},
{
"cell_type": "markdown",
- "id": "b2d1e5f3",
+ "id": "b00796b3",
"metadata": {
"editable": true
},
@@ -6710,7 +6707,7 @@
},
{
"cell_type": "markdown",
- "id": "46371be2",
+ "id": "07d55ec4",
"metadata": {
"editable": true
},
@@ -6739,7 +6736,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "id": "376da27e",
+ "id": "56e1ff08",
"metadata": {
"collapsed": false,
"editable": true
@@ -6786,7 +6783,7 @@
},
{
"cell_type": "markdown",
- "id": "2d56e19d",
+ "id": "23cb46d1",
"metadata": {
"editable": true
},
@@ -6812,7 +6809,7 @@
{
"cell_type": "code",
"execution_count": 49,
- "id": "869a7ce7",
+ "id": "082821ea",
"metadata": {
"collapsed": false,
"editable": true
@@ -7046,7 +7043,7 @@
},
{
"cell_type": "markdown",
- "id": "89970852",
+ "id": "48b0ab9e",
"metadata": {
"editable": true
},
@@ -7058,7 +7055,7 @@
},
{
"cell_type": "markdown",
- "id": "facb0872",
+ "id": "d80c32cf",
"metadata": {
"editable": true
},
@@ -7070,7 +7067,7 @@
},
{
"cell_type": "markdown",
- "id": "581a8a76",
+ "id": "247e3bee",
"metadata": {
"editable": true
},
@@ -7082,7 +7079,7 @@
},
{
"cell_type": "markdown",
- "id": "e6b675e7",
+ "id": "68834b21",
"metadata": {
"editable": true
},
@@ -7099,7 +7096,7 @@
},
{
"cell_type": "markdown",
- "id": "32e6ecf5",
+ "id": "2b44826e",
"metadata": {
"editable": true
},
@@ -7109,7 +7106,7 @@
},
{
"cell_type": "markdown",
- "id": "5e5d27bf",
+ "id": "7fae1b11",
"metadata": {
"editable": true
},
@@ -7121,7 +7118,7 @@
},
{
"cell_type": "markdown",
- "id": "25d94470",
+ "id": "f730d187",
"metadata": {
"editable": true
},
@@ -7138,7 +7135,7 @@
},
{
"cell_type": "markdown",
- "id": "e96a3863",
+ "id": "83554dad",
"metadata": {
"editable": true
},
@@ -7149,7 +7146,7 @@
},
{
"cell_type": "markdown",
- "id": "4157c6b3",
+ "id": "af58b0a5",
"metadata": {
"editable": true
},
@@ -7169,7 +7166,7 @@
},
{
"cell_type": "markdown",
- "id": "75306ee4",
+ "id": "faee51d3",
"metadata": {
"editable": true
},
@@ -7179,7 +7176,7 @@
},
{
"cell_type": "markdown",
- "id": "b04f122c",
+ "id": "4923def0",
"metadata": {
"editable": true
},
@@ -7205,7 +7202,7 @@
},
{
"cell_type": "markdown",
- "id": "84137e6f",
+ "id": "20dbc29e",
"metadata": {
"editable": true
},
@@ -7221,7 +7218,7 @@
},
{
"cell_type": "markdown",
- "id": "b48fe5b1",
+ "id": "8e4820e8",
"metadata": {
"editable": true
},
@@ -7232,7 +7229,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "id": "3a3e0ae8",
+ "id": "7410b8b6",
"metadata": {
"collapsed": false,
"editable": true
@@ -7463,7 +7460,7 @@
},
{
"cell_type": "markdown",
- "id": "f010df37",
+ "id": "8ae30185",
"metadata": {
"editable": true
},
diff --git a/doc/src/week43/week43.do.txt b/doc/src/week43/week43.do.txt
index a0b78c634..35bff4523 100644
--- a/doc/src/week43/week43.do.txt
+++ b/doc/src/week43/week43.do.txt
@@ -1,6 +1,6 @@
TITLE: Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University
-DATE: today
+DATE: October 21, 2024
!split
===== Plans for week 43 =====
@@ -28,35 +28,32 @@ DATE: today
===== Mathematics of deep learning =====
!bblock Two recent books online
-o "The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen":"https://arxiv.org/abs/2105.04026", published as "Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022":"https://doi.org/10.1017/9781009025096.002"
+o The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at URL:"https://arxiv.org/abs/2105.04026", published as "Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022":"https://doi.org/10.1017/9781009025096.002"
-o "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger":"https://doi.org/10.48550/arXiv.2310.20360"
+o Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at URL:"https://doi.org/10.48550/arXiv.2310.20360"
!eblock
!split
===== Reminder on books with hands-on material and codes =====
!bblock
-* "Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch":"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
+* Sebastian Rashcka et al, Machine learning with Scikit-Learn and PyTorch at URL:"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
!eblock
!split
===== Reading recommendations =====
-o Rashkca et al., chapter 11, jupyter-notebook sent separately, from "GitHub":"https://github.com/rasbt/machine-learning-book". See also chapters 12 and 13 on using Pytorch to make a Neural network code.
+o Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at URL:"https://github.com/rasbt/machine-learning-book". See also chapters 12 and 13 on using Pytorch to make a Neural network code.
o Goodfellow et al, chapter 6 and 7 contain most of the neural network background.
-
-
-
!split
===== Using Automatic differentiation =====
-a
-In our discussions of ordinary differential equations
-we will also study the usage of "Autograd":"https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from "week 39":"https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" and the "Autograd documentation":"https://github.com/HIPS/autograd".
-t
+
+In our discussions of ordinary differential equations and neural network codes
+we will also study the usage of Autograd, see for example URL:"https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from "week 39":"https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" and the Autograd documentation at URL:"https://github.com/HIPS/autograd".
+
!split
===== Back propagation and automatic differentiation =====