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correcting typos

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Morten Hjorth-Jensen
2025-10-05 20:14:05 +02:00
parent 250a59fbbd
commit 2202f0d2a6
7 changed files with 373 additions and 363 deletions
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doc/pub/week41/html/week41-bs.html
+13 -12
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@@ -44,6 +44,7 @@ doconce format html week41.do.txt --html_style=bootstrap --pygments_html_style=d
2,
None,
'material-for-the-lecture-on-monday-october-6-2025'),
('Readings and Videos:', 2, None, 'readings-and-videos'),
('Mathematics of deep learning',
2,
None,
@@ -330,6 +331,7 @@ MathJax.Hub.Config({
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="#plan-for-week-41-october-6-10" style="font-size: 80%;">Plan for week 41, October 6-10</a></li>
<!-- navigation toc: --> <li><a href="#material-for-the-lecture-on-monday-october-6-2025" style="font-size: 80%;">Material for the lecture on Monday October 6, 2025</a></li>
<!-- navigation toc: --> <li><a href="#readings-and-videos" style="font-size: 80%;">Readings and Videos:</a></li>
<!-- navigation toc: --> <li><a href="#mathematics-of-deep-learning" style="font-size: 80%;">Mathematics of deep learning</a></li>
<!-- navigation toc: --> <li><a href="#reminder-on-books-with-hands-on-material-and-codes" style="font-size: 80%;">Reminder on books with hands-on material and codes</a></li>
<!-- navigation toc: --> <li><a href="#lab-sessions-on-tuesday-and-wednesday" style="font-size: 80%;">Lab sessions on Tuesday and Wednesday</a></li>
@@ -452,24 +454,24 @@ MathJax.Hub.Config({
<ol>
<li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
<li> Building our own Feed-forward Neural Network, getting started
<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_self"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
<!-- * Video of lecture notes at URL:"" -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
</ol>
<!-- !split -->
<h2 id="readings-and-videos" class="anchor">Readings and Videos: </h2>
<div class="panel panel-default">
<div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ol>
<li> These lecture notes</li>
<li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_self">GitHub</a>
<ol type="a"></li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_self">GitHub</a></li>
<li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_self"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
</ol>
<li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_self"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
<li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_self"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
<li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_self"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
<li> We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_self"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
</ol>
<p>We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_self"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
</div>
</div>
@@ -493,9 +495,7 @@ MathJax.Hub.Config({
<div class="panel panel-default">
<div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ul>
<li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_self">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
</ul>
<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_self">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
</div>
</div>
@@ -503,9 +503,10 @@ MathJax.Hub.Config({
<!-- !split -->
<h2 id="lab-sessions-on-tuesday-and-wednesday" class="anchor">Lab sessions on Tuesday and Wednesday </h2>
<ol>
<li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
</ol>
<p>Aim: Getting started with coding neural network. The exercises this
week aim at setting up the feed-forward part of a neural network.
</p>
<!-- !split -->
<h2 id="lecture-monday-october-6" class="anchor">Lecture Monday October 6 </h2>
doc/pub/week41/html/week41-reveal.html
+13 -16
View File
@@ -200,27 +200,26 @@ MathJax.Hub.Config({
<ol>
<p><li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
<p><li> Building our own Feed-forward Neural Network, getting started
<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_blank"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
<!-- * Video of lecture notes at URL:"" -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
</ol>
<p>
</section>
<section>
<h2 id="readings-and-videos">Readings and Videos: </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Readings and Videos:</b>
<b></b>
<p>
<ol>
<p><li> These lecture notes</li>
<p><li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
<p><li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>
<ol type="a"></li>
<p><li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a></li>
<p><li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
</ol>
<p>
<p><li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
<p><li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
<p><li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
<p><li> We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
</ol>
<p>
<p>We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
</div>
</section>
@@ -242,18 +241,16 @@ MathJax.Hub.Config({
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<p><li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
</ul>
<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
</div>
</section>
<section>
<h2 id="lab-sessions-on-tuesday-and-wednesday">Lab sessions on Tuesday and Wednesday </h2>
<ol>
<p><li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
</ol>
<p>Aim: Getting started with coding neural network. The exercises this
week aim at setting up the feed-forward part of a neural network.
</p>
</section>
<section>
doc/pub/week41/html/week41-solarized.html
+13 -13
View File
@@ -71,6 +71,7 @@ div.toc p,a {
2,
None,
'material-for-the-lecture-on-monday-october-6-2025'),
('Readings and Videos:', 2, None, 'readings-and-videos'),
('Mathematics of deep learning',
2,
None,
@@ -367,24 +368,24 @@ MathJax.Hub.Config({
<ol>
<li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
<li> Building our own Feed-forward Neural Network, getting started
<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_blank"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
<!-- * Video of lecture notes at URL:"" -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="readings-and-videos">Readings and Videos: </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Readings and Videos:</b>
<b></b>
<p>
<ol>
<li> These lecture notes</li>
<li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>
<ol type="a"></li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a></li>
<li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
</ol>
<li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
<li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
<li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
<li> We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
</ol>
<p>We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
</div>
@@ -406,18 +407,17 @@ MathJax.Hub.Config({
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
</ul>
<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
</div>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lab-sessions-on-tuesday-and-wednesday">Lab sessions on Tuesday and Wednesday </h2>
<ol>
<li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
</ol>
<p>Aim: Getting started with coding neural network. The exercises this
week aim at setting up the feed-forward part of a neural network.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lecture-monday-october-6">Lecture Monday October 6 </h2>
doc/pub/week41/html/week41.html
+13 -13
View File
@@ -148,6 +148,7 @@ div.toc p,a {
2,
None,
'material-for-the-lecture-on-monday-october-6-2025'),
('Readings and Videos:', 2, None, 'readings-and-videos'),
('Mathematics of deep learning',
2,
None,
@@ -444,24 +445,24 @@ MathJax.Hub.Config({
<ol>
<li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
<li> Building our own Feed-forward Neural Network, getting started
<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_blank"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
<!-- * Video of lecture notes at URL:"" -->
<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="readings-and-videos">Readings and Videos: </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Readings and Videos:</b>
<b></b>
<p>
<ol>
<li> These lecture notes</li>
<li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>
<ol type="a"></li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a></li>
<li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
</ol>
<li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
<li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
<li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
<li> We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
</ol>
<p>We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
</div>
@@ -483,18 +484,17 @@ MathJax.Hub.Config({
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
</ul>
<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
</div>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lab-sessions-on-tuesday-and-wednesday">Lab sessions on Tuesday and Wednesday </h2>
<ol>
<li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
</ol>
<p>Aim: Getting started with coding neural network. The exercises this
week aim at setting up the feed-forward part of a neural network.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lecture-monday-october-6">Lecture Monday October 6 </h2>
doc/pub/week41/ipynb/ipynb-week41-src.tar.gz
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doc/src/week41/week41.do.txt
+12 -8
View File
@@ -10,17 +10,21 @@ DATE: Week 41
===== Material for the lecture on Monday October 6, 2025 =====
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, getting started
# * Video of lecture notes at URL:"https://youtu.be/pMRUbf9E-gM"
# * Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf"
!bblock Readings and Videos:
# * Video of lecture notes at URL:""
# * Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf"
!split
===== Readings and Videos: =====
!bblock
o These lecture notes
o For neural networks we recommend Goodfellow et al chapters 6 and 7.
o Rashkca et al., chapter 11, jupyter-notebook sent separately, from "GitHub":"https://github.com/rasbt/machine-learning-book"
o Neural Networks demystified at URL:"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
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".
o 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
@@ -28,7 +32,6 @@ We also recommend Michael Nielsen's intuitive approach to the neural networks a
!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 "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger":"https://doi.org/10.48550/arXiv.2310.20360"
!eblock
@@ -36,14 +39,15 @@ o "Mathematical Introduction to Deep Learning: Methods, Implementations, and The
!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 Sickit-Learn and PyTorch":"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
!eblock
!split
===== Lab sessions on Tuesday and Wednesday =====
o Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.
Aim: Getting started with coding neural network. The exercises this
week aim at setting up the feed-forward part of a neural network.