forked from udlbook/udlbook
-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
71 lines (66 loc) · 5.37 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
<h1>Understanding Deep Learning</h1>
by Simon J.D. Prince
<br>
To be published by MIT Press.
<h2> Download draft PDF </h2>
<a href="https://github.com/udlbook/udlbook/releases/download/v0.2.1/UnderstandingDeepLearning_07_10_22_C.pdf">Draft PDF Chapters 2-13</a><br> 2022-10-07. CC-BY-NC-ND license
<br>
<ul>
<li> Chapters 1,14-19 (coming Jan 2nd, 2023)
<li> Report errata via <a href="https://github.com/udlbook/udlbook/issues">github</a> or contact me directly at [email protected]
<li> Follow me on <a href="https://twitter.com/SimonPrinceAI">Twitter</a> or <a href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</a> for updates.
</ul>
<h2>Table of contents</h2>
<ul>
<li> Chapter 1 - Introduction
<li> Chapter 2 - Supervised learning
<li> Chapter 3 - Shallow neural networks
<li> Chapter 4 - Deep neural networks
<li> Chapter 5 - Loss functions
<li> Chapter 6 - Training models
<li> Chapter 7 - Gradients and initialization
<li> Chapter 8 - Measuring performance
<li> Chapter 9 - Regularization
<li> Chapter 11 - Residual networks
<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>
<h2>Resources for instructors </h2>
<ul>
<li> Chapter 1 - Introduction
<li> Chapter 2 - Supervised learning: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <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: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap10.pptx">PowerPoint Figures</a>
<li> Chapter 11 - Residual networks: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap11.pptx">PowerPoint Figures</a>
<li> Chapter 12 - Transformers: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap12.pptx">PowerPoint Figures</a>
<li> Chapter 13 - Graph neural networks: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap13.pptx">PowerPoint Figures</a>
<li> Chapter 14 - Variational auto-encoders: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 15 - Normalizing flows: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 16 - Generative adversarial networks: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 17 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 18 - Deep reinforcement learning: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 19 - Why does deep learning work?: Slides / Notebooks / PDF Figures / PowerPoint Figures
</ul>