Ian Goodfellow, Yoshua Bengio and Aaron Courville

• 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
    

BibliographyIndex