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Morten Hjorth-Jensen
2024-10-19 09:16:14 +02:00
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doc/pub/week43/html/week43-bs.html
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</center>
<br>
<center>
<h4>Oct 19, 2024</h4>
<h4>October 21, 2024</h4>
</center> <!-- date -->
<br>
@@ -517,8 +517,8 @@ MathJax.Hub.Config({
<div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ol>
<li> <a href="https://arxiv.org/abs/2105.04026" target="_self">The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen</a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_self">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<li> <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_self">Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger</a></li>
<li> The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at <a href="https://arxiv.org/abs/2105.04026" target="_self"><tt>https://arxiv.org/abs/2105.04026</tt></a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_self">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<li> Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_self"><tt>https://doi.org/10.48550/arXiv.2310.20360</tt></a></li>
</ol>
</div>
</div>
@@ -530,7 +530,7 @@ MathJax.Hub.Config({
<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>
<li> Sebastian Rashcka et al, Machine learning with Scikit-Learn and PyTorch at <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_self"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
</div>
@@ -540,15 +540,14 @@ MathJax.Hub.Config({
<h2 id="reading-recommendations" class="anchor">Reading recommendations </h2>
<ol>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_self">GitHub</a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at <a href="https://github.com/rasbt/machine-learning-book" target="_self"><tt>https://github.com/rasbt/machine-learning-book</tt></a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<li> Goodfellow et al, chapter 6 and 7 contain most of the neural network background.</li>
</ol>
<!-- !split -->
<h2 id="using-automatic-differentiation" class="anchor">Using Automatic differentiation </h2>
<p>a
In our discussions of ordinary differential equations
we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_self">Autograd</a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_self">week 39</a> and the <a href="https://github.com/HIPS/autograd" target="_self">Autograd documentation</a>.
t
<p>In our discussions of ordinary differential equations and neural network codes
we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_self"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_self">week 39</a> and the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_self"><tt>https://github.com/HIPS/autograd</tt></a>.
</p>
<!-- !split -->
doc/pub/week43/html/week43-reveal.html
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</center>
<br>
<center>
<h4>Oct 19, 2024</h4>
<h4>October 21, 2024</h4>
</center> <!-- date -->
<br>
@@ -234,8 +234,8 @@ MathJax.Hub.Config({
<b>Two recent books online</b>
<p>
<ol>
<p><li> <a href="https://arxiv.org/abs/2105.04026" target="_blank">The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen</a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_blank">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<p><li> <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_blank">Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger</a></li>
<p><li> The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at <a href="https://arxiv.org/abs/2105.04026" target="_blank"><tt>https://arxiv.org/abs/2105.04026</tt></a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_blank">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<p><li> Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_blank"><tt>https://doi.org/10.48550/arXiv.2310.20360</tt></a></li>
</ol>
</div>
</section>
@@ -246,7 +246,7 @@ MathJax.Hub.Config({
<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>
<p><li> Sebastian Rashcka et al, Machine learning with Scikit-Learn and PyTorch at <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
</section>
@@ -255,17 +255,16 @@ MathJax.Hub.Config({
<h2 id="reading-recommendations">Reading recommendations </h2>
<ol>
<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>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<p><li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at <a href="https://github.com/rasbt/machine-learning-book" target="_blank"><tt>https://github.com/rasbt/machine-learning-book</tt></a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<p><li> Goodfellow et al, chapter 6 and 7 contain most of the neural network background.</li>
</ol>
</section>
<section>
<h2 id="using-automatic-differentiation">Using Automatic differentiation </h2>
<p>a
In our discussions of ordinary differential equations
we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank">Autograd</a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_blank">week 39</a> and the <a href="https://github.com/HIPS/autograd" target="_blank">Autograd documentation</a>.
t
<p>In our discussions of ordinary differential equations and neural network codes
we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_blank">week 39</a> and the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a>.
</p>
</section>
doc/pub/week43/html/week43-solarized.html
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@@ -372,7 +372,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Oct 19, 2024</h4>
<h4>October 21, 2024</h4>
</center> <!-- date -->
<br>
@@ -411,8 +411,8 @@ MathJax.Hub.Config({
<b>Two recent books online</b>
<p>
<ol>
<li> <a href="https://arxiv.org/abs/2105.04026" target="_blank">The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen</a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_blank">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<li> <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_blank">Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger</a></li>
<li> The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at <a href="https://arxiv.org/abs/2105.04026" target="_blank"><tt>https://arxiv.org/abs/2105.04026</tt></a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_blank">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<li> Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_blank"><tt>https://doi.org/10.48550/arXiv.2310.20360</tt></a></li>
</ol>
</div>
@@ -423,7 +423,7 @@ MathJax.Hub.Config({
<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>
<li> Sebastian Rashcka et al, Machine learning with Scikit-Learn and PyTorch at <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
@@ -432,15 +432,14 @@ MathJax.Hub.Config({
<h2 id="reading-recommendations">Reading recommendations </h2>
<ol>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at <a href="https://github.com/rasbt/machine-learning-book" target="_blank"><tt>https://github.com/rasbt/machine-learning-book</tt></a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<li> Goodfellow et al, chapter 6 and 7 contain most of the neural network background.</li>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="using-automatic-differentiation">Using Automatic differentiation </h2>
<p>a
In our discussions of ordinary differential equations
we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank">Autograd</a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_blank">week 39</a> and the <a href="https://github.com/HIPS/autograd" target="_blank">Autograd documentation</a>.
t
<p>In our discussions of ordinary differential equations and neural network codes
we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_blank">week 39</a> and the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a>.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/week43/html/week43.html
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@@ -449,7 +449,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Oct 19, 2024</h4>
<h4>October 21, 2024</h4>
</center> <!-- date -->
<br>
@@ -488,8 +488,8 @@ MathJax.Hub.Config({
<b>Two recent books online</b>
<p>
<ol>
<li> <a href="https://arxiv.org/abs/2105.04026" target="_blank">The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen</a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_blank">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<li> <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_blank">Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger</a></li>
<li> The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen at <a href="https://arxiv.org/abs/2105.04026" target="_blank"><tt>https://arxiv.org/abs/2105.04026</tt></a>, published as <a href="https://doi.org/10.1017/9781009025096.002" target="_blank">Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022</a></li>
<li> Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger at <a href="https://doi.org/10.48550/arXiv.2310.20360" target="_blank"><tt>https://doi.org/10.48550/arXiv.2310.20360</tt></a></li>
</ol>
</div>
@@ -500,7 +500,7 @@ MathJax.Hub.Config({
<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>
<li> Sebastian Rashcka et al, Machine learning with Scikit-Learn and PyTorch at <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
@@ -509,15 +509,14 @@ MathJax.Hub.Config({
<h2 id="reading-recommendations">Reading recommendations </h2>
<ol>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from GitHub site at <a href="https://github.com/rasbt/machine-learning-book" target="_blank"><tt>https://github.com/rasbt/machine-learning-book</tt></a>. See also chapters 12 and 13 on using Pytorch to make a Neural network code.</li>
<li> Goodfellow et al, chapter 6 and 7 contain most of the neural network background.</li>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="using-automatic-differentiation">Using Automatic differentiation </h2>
<p>a
In our discussions of ordinary differential equations
we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank">Autograd</a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_blank">week 39</a> and the <a href="https://github.com/HIPS/autograd" target="_blank">Autograd documentation</a>.
t
<p>In our discussions of ordinary differential equations and neural network codes
we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the lectures slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_blank">week 39</a> and the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a>.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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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 =====