Welcome to dl’s documentation!

Contents:

• Part I: Applied Math and Machine Learning Basics
  • 5 Machine Learning Basics
• Part II: Modern Practical Deep Networks
  • 8 Optimization for Training Deep Models
  • 9 The convolutional Networks
  • 11 Practical Methodoloogy
  • 12 Applications
• Part III: Deep Learning Research
  • 14 Autoencoders
  • 15 Representation Learning
  • 16 Structured Probablistic Models for Deep Learning
  • 17 Monte Carlo Methods
  • 18 Confronting the Partition Function
  • 19 Approximate Inference
  • 20 Deep Generative Models
• Extra
  • Reinforcement Learning

Indices and tables

• Index
• Module Index
• Search Page