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deep learning book-概览

标签:deep learning book


几个git链接:

内容简介:

  • 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

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