deepLearningBook
deep learning 学习笔记
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