deepLearningBook

deep learning 学习笔记

• 学习资料来自：
  • 英文：http://www.deeplearningbook.org/
    • 完整版pdf下载
  • 中文：
    • https://hit-scir.gitbooks.io/neural-networks-and-deep-learning-zh_cn/content/index.html
    • 深度 | 深度学习与神经网络全局概览：核心技术的发展历程
  • 同时也顺便学习用LaTex写公式：

目录：

• Table of Contents:更详细的目录列表
• Acknowledgements
• Notation: 使用到的符号说明
• 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