The Deep Learning Book

• Table of Contents

• Acknowledgements

• Notation

• Introduction

• Part I: Applied Math and Machine Learning Basics

  • Linear Algebra
  • Probability and Information Theory
  • Numerical Computation
  • Machine Learning Basics

• Part II: Modern Practical Deep Networks

  • Deep Feedforward Networks
  • Regularization for Deep Learning
  • Optimization for Training Deep Models
  • Convolutional Networks
  • Sequence Modeling: Recurrent and Recursive Nets
  • Practical Methodology
  • Applications

• Part III: Deep Learning Research

  • Linear Factor Models
  • Autoencoders
  • Representation Learning
  • Structured Probabilistic Models for Deep Learning
  • Monte Carlo Methods
  • Confronting the Partition Function
  • Approximate Inference
  • Deep Generative Models

• Bibliography

• Index

Notes & Credits:

Authored by: Ian Goodfellow and Yoshua Bengio and Aaron Courville

Publisher: MIT Press

Release Year: 2016

URL: Deep Learning Book