Rufous

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.
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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}
}




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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




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