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CS231N

Lecture	Description	Appendix
1	Course Introduction​
2	Image Classification
3	Loss Functions and Optimization
4	Backpropagation and Neural Networks
5	Convolutional Neural Networks	CNN
6	Training Neural Networks, part I
7	Training Neural Networks, part II
8	Deep Learning Software
9	CNN Architectures
10	Recurrent Neural Networks	RNN
11	Detection and Segmentation
12	Visualizing and Understanding
13	Generative Models
14	Deep Reinforcement Learning
15	Efficient Methods and Hardware for Deep Learning
16	Adversarial Examples and Adversarial Training

Object Detection

Subject
R-CNN
Fast RCNN
Faster RCNN
YOLO

Reinforcement Learning

Lecture	Description
1	Introduction to Reinforcement Learning
2	Markov Decision Processes
3	Planning by Dynamic Programming
4	Model-Free Prediction
5	Model-Free Control
6	Value Function Approximation
7	Policy Gradient Methods

RL Algorithm

Subject
DQN
DDQN
A3C
DDPG
TRPO
PPO