INDEX 1 Convolution Layer 2 Padding 2 1

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INDEX 1. Convolution Layer 2. Padding 2

INDEX 1. Convolution Layer 2. Padding 2

1. Convolution 한 개의 필터를 거치면 depth가 1인 Activation map이 나온다 5

1. Convolution 한 개의 필터를 거치면 depth가 1인 Activation map이 나온다 5

1. Convolution 7

1. Convolution 7

1. Convolution 1 2 3 4 5 6 7 8 9 7 x 7의

1. Convolution 1 2 3 4 5 6 7 8 9 7 x 7의 데이터에서 3 x 3인 필터로 Convolve 1 2 3 4 5 6 7 8 9 a 1 a 2 a 3 a 4 a 5 a 6 a 7 a 8 a 9 b b b b b 이 필터가 input data 위를 움직임 8

2. Padding 결국 Convolution layer는 필터의 수, 크기, stride와 zero padding의 범위 4개의 hyper-parameter를

2. Padding 결국 Convolution layer는 필터의 수, 크기, stride와 zero padding의 범위 4개의 hyper-parameter를 가진다 14

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