본 chapter에서는 Gradient를 구하기 위한 Backpropagation을 이해하고 Neural Network의 기본에 대해 설명한다.

• Video : https://www.youtube.com/watch?v=d14TUNcbn1k
• Slide : http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf

(TODO: 귀차니즘의 압박으로 정리를 안해놓았지만 언젠간 해야지)

Backpropagation

Chain rule

Sigmoid gate example

Patterns in backward flow

Gradients add at branches

Vectorized operations

Neural Network

Artificial Neural Network

Activation Function

Neural networks Architectures