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Deep Learning

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MIT Deep Learning Book in PDF format (by Ian Goodfellow, Yoshua Bengio and Aaron Courville).

Author(s): Ian Goodfellow, Yoshua Bengio, Aaron Courville
Publisher: MIT
Year: 2017

Language: English
Pages: 800
Tags: deep learning, machine learning, neural networks

Table of Contents......Page 2
Website......Page 8
Acknowledgements......Page 9
Notation......Page 12
1 Introduction......Page 16
PART I: Applied Math and Machine Learning Basics......Page 44
2 Linear Algebra......Page 46
3 Probability and Information Theory......Page 68
4 Numerical Computation......Page 95
5 Machine Learning Basics......Page 113
PART II: Deep Networks: Modern Practices......Page 181
6 Deep Feedforward Networks......Page 183
7 Regularization for Deep Learning......Page 243
8 Optimization for Training Deep Models......Page 289
9 Convolutional Networks......Page 345
10 Sequence Modeling: Recurrent and Recursive Nets......Page 388
11 Practical Methodology......Page 436
12 Applications......Page 458
PART III: Deep Learning Research......Page 501
13 Linear Factor Models......Page 504
14 Autoencoders......Page 517
15 Representation Learning......Page 541
16 Structured Probabilistic Models for Deep Learning......Page 573
17 Monte Carlo Methods......Page 605
18 Confronting the Partition Function......Page 620
19 Approximate Inference......Page 646
20 Deep Generative Models......Page 669
Bibliography......Page 736
Index......Page 793