Deep Learning

An MIT Press book

Ian Goodfellow and Yoshua Bengio and Aaron Courville

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.

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Citing the book

@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

FAQ

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  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.

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• Can I translate the book into Chinese?

Posts and Telecom Press has purchased the rights.

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