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<h1>Understanding Deep Learning</h1> <h1>Understanding Deep Learning</h1>
by Simon J.D. Prince
<br>
To be published by MIT Press.
<h2>Key links</h2>
<ul>
<li> <a href="https://github.com/udlbook/udlbook/releases/download/v0.1.0/UnderstandingDeepLearning_30_07_22_C.pdf">Draft PDF Chapters 2-9</a>
<li> Draft PDF Chapters 10-13 (coming Sept 1st, 2022)
<li> Draft PDF Chapters 1,14-19 (coming Jan 2nd, 2023)
<li> Jupyter notebooks (coming Spring 2023)
<li> Report errata via <a href="https://github.com/udlbook/udlbook/issues">github</a> or contact me directly at udlbookmail@gmail.com
</ul>
<h2>Table of contents</h2>
<ul>
<li> Chapter 1 - Introduction
<li> Chapter 2 - Supervised learning <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap2.pptx">PowerPoint Figures</a>
<li> Chapter 3 - Shallow neural networks <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap3.pptx">PowerPoint Figures</a>
<li> Chapter 4 - Deep neural networks <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap4.pptx">PowerPoint Figures</a>
<li> Chapter 5 - Loss functions <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap5.pptx">PowerPoint Figures</a>
<li> Chapter 6 - Training models <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap6.pptx">PowerPoint Figures</a>
<li> Chapter 7 - Gradients and initialization <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap7.pptx">PowerPoint Figures</a>
<li> Chapter 8 - Measuring performance <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap8.pptx">PowerPoint Figures</a>
<li> Chapter 9 - Regularization <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap9.pptx">PowerPoint Figures</a>
<li> Chapter 10 - Convolutional nets
<li> Chapter 11 - Residual networks and BatchNorm
<li> Chapter 12 - Transformers
<li> Chapter 13 - Graph neural networks
<li> Chapter 14 - Variational auto-encoders
<li> Chapter 15 - Normalizing flows
<li> Chapter 16 - Generative adversarial networks
<li> Chapter 17 - Diffusion models
<li> Chapter 18 - Deep reinforcement learning
<li> Chapter 19 - Why does deep learning work?
</ul>
<br>
Citation:
<pre><code>
@book{prince2022understanding,
author = "Simon J.D. Prince",
title = "Understanding Deep Learning",
publisher = "MIT Press",
year = 2022,
url = "https://udlbook.github.io/udlbook/"
}
</code></pre>