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Deep Learning textbook (2016)
webdeeplearningbook.org·deeplearningbook.org/
A foundational capabilities textbook; useful for AI safety researchers who need to understand the technical underpinnings of modern neural networks before engaging with alignment or interpretability work.
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Importance: 55/100bookeducational
Summary
A comprehensive textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville covering the mathematical and conceptual foundations of deep learning. It spans topics from basic linear algebra and probability through neural network architectures, optimization, and regularization. It remains one of the most widely used references for understanding modern AI systems.
Key Points
- •Covers foundational mathematics (linear algebra, probability, information theory) required to understand deep learning systems.
- •Explains core neural network concepts: feedforward networks, CNNs, RNNs, optimization algorithms, and regularization techniques.
- •Includes chapters on representation learning and the historical context of deep learning's development.
- •Freely available online, making it an accessible reference for researchers and practitioners entering the field.
- •Understanding these capabilities is relevant for AI safety researchers assessing model behavior, failure modes, and alignment challenges.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Yoshua Bengio | Person | 39.0 |
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Deep Learning
Deep Learning
An MIT Press book
Ian Goodfellow and Yoshua Bengio and Aaron Courville
Exercises  
Lectures  
External Links  
The Deep Learning textbook is a resource intended to help students
and practitioners enter the field of machine learning in general
and deep learning in particular.
The online version of the book is now complete and will remain
available online for free.
The deep learning textbook can now be ordered on
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Citing the book
To cite this book, please use this bibtex entry:
@book{Goodfellow-et-al-2016,
title={Deep Learning},
author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
publisher={MIT Press},
note={\url{http://www.deeplearningbook.org}},
year={2016}
}
To write your own document using our LaTeX style, math notation, or
to copy our notation page, download our
template files.
Errata in published editions
Deep Learning
Table of Contents
Acknowledgements
Notation
1 Introduction
Part I: Applied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
Part II: Modern Practical Deep Networks
6 Deep Feedforward Networks
7 Regularization for Deep Learning
8 Optimization for Training Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications
Part III: Deep Learning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Learning
17 Monte Carlo Methods
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models
Bibliography
Index
FAQ
Can I get a PDF of this book?
No, our contract with MIT Press forbids distribution of too easily copied
electronic formats of the book.
Why are you using HTML format for the web version of the book?
This format is a sort of weak DRM required by our contract with MIT Press.
It's intended to discourage unauthorized copying/editing
of the book.
What is the best way to print the HTML format?
Printing seems to work best printing directly from the browser, using Chrome.
Other browsers do not work as well.
Can I translate the book into Chinese?
Posts and Telecom Press has purchased the rights.
If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the
website, do not hesitate to contact the authors directly by e-mail
at: feedback@deeplearningbook.org
Since the book is complete and in print, we do not make large changes,
only small corrections.
Known issues: In outdated versions of the Edge
browser, the "does not equal" sign sometimes appears as the "equals" sign.
This may be resolved by updating to the latest version.Resource ID:
ff7e829ddc87cdc0 | Stable ID: sid_5zIr9i4lWr