목차
1 Introduction
ⅠApplied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics

ⅡDeep Networks: Modern Practices
6 Deep Feedfoward Networks
7 Regularization for Deep Leaning
8 Optimization for Raining Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications

Ⅲ Deep Leaning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Leaning
17 Monte Carlo Methods
18 Confonting the Partition Function
19 Approximate Inference
20 Deep Generative Models