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
http://www.deeplearningbook.org
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
http://www.deeplearningbook.org