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Introduction to CNN
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Subin An
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CNN소개 Keynote입니다. 관련 영상을 촬영하다 무산되어 자료만 올립니다.
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Introduction to CNN
1.
Introduction to CNN
3.
(Arti cial Neural
Networks) Input Output
4.
? 2 , 154
. 77 : 77 : , , . .
5.
DNN : Fully
Connected
6.
https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning ? 200 by 200
?? 40000 ~ 20 - ??
7.
https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning ? 200 by 200 40000 10
* 10 4,000,000 , - (sparse) Connection
8.
https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning , ? 200 by
200 100 10 * 10 10,000
9.
NN CNN ?
10.
NN
11.
CNN .
12.
Feature Extraction Machine Learning End-to-End
Feature Extraction Deep Learning .
13.
WhyCNN? 그래서 왜 CNN을
사용하고, CNN의 특징은 무엇일까
14.
1. Sparse Connection 2.
Parameter Sharing 3. Translational Invariant CNN Main Ideas
15.
I. Sparse Connection Sparse
Connection ( )
16.
I. Sparse Connection layer
receptive eld 3x3 = 5x5
17.
II. Parameter Sharing (=
) parameter sharing
18.
III. Translational Invariant
19.
( CONV layer
Pooling Layer) andCNN
20.
Activation map ,
Depth CNN
22.
32x32 5x5 28x28
activation map
23.
6 6 activation
map
24.
, activation map
25.
1. CONV(Convolution) 2. POOL(Pooling) 3.
FC(Full Connection) CNN Layer
26.
Conv Layer
27.
( )
28.
.
29.
Red Green Blue Channel
? RGB, 3
30.
Conv Layer Feature map
? ?
31.
Stride kernel size . 1, 2
1/4
32.
Padding stride output side e
ect Zero Padding(0 ) +
33.
Pooling Layer
34.
Pooling Stride
35.
Pretrained Model
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