update: 2017-08-15

最近在用tensorflow做深度学习方面的工作，总有「书到用时方恨少」之感。虽然研究生时上过两门相关课程，但均是浅尝辙止。这回和冬博士讨教，他推荐了Deep Learning这本书，于是打算认真研读下。

思路安排如下：因为已经有数学和机器学习基础，所以跳过第一部份，如果遇到困难，再返查细节。重点阅读第二部份，笔记，如有习题，尽量做习题，并且用tensorflow示意。通览第三部份，笔记，有个大致印象即可。预计耗时3个月。

目录

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

后记