Deep Learning An MIT Press book in preparation Ian Goodfellow, Aaron Courville, and Yoshua Bengio

http://goodfeli.github.io/dlbook/

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. Google employees who would like a paper copy of the book can send Ian the name of the printer nearest their desk and he will send a print job to that printer containing as much of the book as you would like to read.
  
• Why are you using HTML format for the drafts? This format is a sort of weak DRM. 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.

• When will the book come out? It's difficult to predict. We're doing our best to finish it as soon as possible. We expect to deliver the manuscript to reviews by January, but there will be some delay with review, typesetting, etc. Please contact us if you are interested in using the textbook for course materials in the short term, we may be able to find a way to accommodate you.
  

Draft Version 2015-12-17

Please help us make this a great book! This is still only a draft and can be improved in many ways. We've included some of our TODO marks to help you see which sections are still under rapid development. If you see a section that we appear to have finished writing and you find it unclear or inaccurate, please let us know. Please specify (by date) which version you are commenting on. Do not hesitate to contact any of the authors directly by e-mail: Ian (<lastname>@google.com), Aaron (<firstname>.<lastname>@gmail.com), Yoshua (<firstname>.<lastname>@umontreal.ca).

• 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 Feedforward Deep 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 The Manifold Perspective on Representation Learning
• 17 Structured Probabilistic Models for Deep Learning
• 18 Monte Carlo Methods
• 19 Confronting the Partition Function
• 20 Approximate Inference
• 21 Deep Generative Models
• Bibliography
• Index