Deep Learning Book

http://www.deeplearningbook.org/

Deep Learning

An MIT Press book

Ian Goodfellow, Yoshua Bengio and Aaron Courville

Exercises   Lecture Slides  

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 print version will be available for sale soon.

Citing the book

To cite this book, please use this bibtex entry:

@unpublished{Goodfellow-et-al-2016-Book,
    title={Deep Learning},
    author={Ian Goodfellow, Yoshua Bengio, and Aaron Courville},
    note={Book in preparation for MIT Press},
    url={http://www.deeplearningbook.org},
    year={2016}
}

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 drafts? 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. Unfortunately, the conversion from PDF to HTML is not perfect, and some things like subscript expressions do not render correctly. If you have a suggestion for a better way of making the book available to a wide audience while preventing unauthorized copies, please let us know.
• 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. In particular, the Edge browser displays the "does not equal" sign as the "equals" sign in some cases.

• When will the book come out? It's difficult to predict. MIT Press is currently preparing the book for printing. Please contact us if you are interested in using the textbook for course materials in the short term; we will put you in contact with MIT Press.
  

If you notice any typos, etc., do not hesitate to contact any of the authors directly by e-mail: Ian (<lastname.firstname>@gmail.com), Yoshua (<firstname>.<lastname>@umontreal.ca), Aaron (<firstname>.<lastname>@gmail.com). The book is now complete and we are not currently making revisions beyond correcting any minor errors that remain.

• 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
• 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