Deep Learning textbook (2016)

web

deeplearningbook.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.

Metadata

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](https://www.deeplearningbook.org/front_matter.pdf)

## An MIT Press book

### Ian Goodfellow and Yoshua Bengio and Aaron Courville

[Exercises](https://www.deeplearningbook.org/exercises.html) [Lectures](https://www.deeplearningbook.org/lecture_slides.html) [External Links](https://www.deeplearningbook.org/external.html)

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
[Amazon](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book).

For up to date announcements, join our
[mailing list](https://groups.google.com/forum/#!forum/deeplearningbook).

## 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](https://github.com/goodfeli/dlbook_notation) files.

[Errata in published editions](https://docs.google.com/document/d/1ABlp7FluwZ0B82_fjNOFVQ2uOZkfuF8elbofhZmNXag/edit?usp=sharing)

## Deep Learning

- [Table of Contents](https://www.deeplearningbook.org/contents/TOC.html)
- [Acknowledgements](https://www.deeplearningbook.org/contents/acknowledgements.html)
- [Notation](https://www.deeplearningbook.org/contents/notation.html)
- [1 Introduction](https://www.deeplearningbook.org/contents/intro.html)
- [Part I: Applied Math and Machine Learning Basics](https://www.deeplearningbook.org/contents/part_basics.html)

  - [2 Linear Algebra](https://www.deeplearningbook.org/contents/linear_algebra.html)
  - [3 Probability and Information Theory](https://www.deeplearningbook.org/contents/prob.html)
  - [4 Numerical Computation](https://www.deeplearningbook.org/contents/numerical.html)
  - [5 Machine Learning Basics](https://www.deeplearningbook.org/contents/ml.html)

- [Part II: Modern Practical Deep Networks](https://www.deeplearningbook.org/contents/part_practical.html)

  - [6 Deep Feedforward Networks](https://www.deeplearningbook.org/contents/mlp.html)
  - [7 Regularization for Deep Learning](https://www.deeplearningbook.org/contents/regularization.html)
  - [8 Optimization for Training Deep Models](https://www.deeplearningbook.org/contents/optimization.html)
  - [9 Convolutional Networks](https://www.deeplearningbook.org/contents/convnets.html)
  - [10 Sequence Modeling: Recurrent and Recursive Nets](https://www.deeplearningbook.org/contents/rnn.html)
  - [11 Practical Methodology](https://www.deeplearningbook.org/contents/guidelines.html)
  

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Resource ID: ff7e829ddc87cdc0 | Stable ID: YzI0ZmM0ZT