Convolutional neural networks (CNNs) are a type of neural network that use convolution operations and pooling layers instead of fully connected layers. CNNs take advantage of the spatial structure of input data like images and learn hierarchical pattern representations through multiple convolutional and pooling layers. The key operations in CNNs are convolutions for extracting features, subsampling or pooling for reducing dimensionality, and fully connected layers for classification.

1. WHAT IS CNN?
CONVOLUTIONAL NEURAL
NETWORK(CNN)

2. CNN
GOAL
▸ FFNN(Feed-Foward Neural NetworK) .
▸ FFNN CNN .
▸ .
▸ CNN .
▸ CNN , .
▸ CNN .

3. TEXT
FFNN(FEED-FOWARD NEURAL NETWORK)
1. Input x
2. y = Wx + b -
3. Activation Function (sigmoid, ReLu) (a =b(y))
4. output layer Multilayer Perceptron(MLP) softmax
Activation function .

4. TEXT
CNN ...
1. Input x
2. y = Wx + b
3. Activation Function (sigmoid, ReLu) (a =b(y))
4. output layer Multilayer Perceptron(MLP) softmax
Activation function .
2 .

5. CNN
CNN .
Convolutions + Subsampling + Full Connection

6. ?
CONVOLUTION
▸ ,
.
▸ ( )
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: ?
.

7. CONVOLUTION
a f(a)
a g(b)
a+b = c c f(a)g(b)
3 1 2 || 0 3

8. CONVOLUTION

9. CONVOLUTION

10. CONVOLUTION
▸
▸
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.

11. CNN
CNN .
Convolutions + Subsampling + Full Connection

12. SUBSAMPLING / POOLING CNN
ZERO-PADDING
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13. SUBSAMPLING / POOLING
STRIDE
.

14. CNN

15. CNN
SUBSAMPLING / POOLING

16. CNN

17. CNN

18. CNN

19. CNN

20. NEXT TIME
4 CNN
▸ AlexNET
▸ VGG
▸ GooLeNet
▸ ResNet