1 Introduction
I Applied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
II Deep Networks: Modern Practices
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
1 Introduction
p 17,
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
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