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Introduction to CNN

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CNN소개 Keynote입니다.

관련 영상을 촬영하다 무산되어 자료만 올립니다.

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Introduction to CNN

  1. 1. Introduction to CNN
  2. 2. (Arti cial Neural Networks) Input Output
  3. 3. ? 2 , 154 . 77 : 77 : , , . .
  4. 4. DNN : Fully Connected
  5. 5. https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning ? 200 by 200 ?? 40000 ~ 20 - ??
  6. 6. https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning ? 200 by 200 40000 10 * 10 4,000,000 , - (sparse) Connection
  7. 7. https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning , ? 200 by 200 100 10 * 10 10,000
  8. 8. NN CNN ?
  9. 9. NN
  10. 10. CNN .
  11. 11. Feature Extraction Machine Learning End-to-End Feature Extraction Deep Learning .
  12. 12. WhyCNN? 그래서 왜 CNN을 사용하고, CNN의 특징은 무엇일까
  13. 13. 1. Sparse Connection 2. Parameter Sharing 3. Translational Invariant CNN Main Ideas
  14. 14. I. Sparse Connection Sparse Connection ( )
  15. 15. I. Sparse Connection layer receptive eld 3x3 = 5x5
  16. 16. II. Parameter Sharing (= ) parameter sharing
  17. 17. III. Translational Invariant
  18. 18. ( CONV layer Pooling Layer) andCNN
  19. 19. Activation map , Depth CNN
  20. 20. 32x32 5x5 28x28 activation map
  21. 21. 6 6 activation map
  22. 22. , activation map
  23. 23. 1. CONV(Convolution) 2. POOL(Pooling) 3. FC(Full Connection) CNN Layer
  24. 24. Conv Layer
  25. 25. ( )
  26. 26. .
  27. 27. Red Green Blue Channel ? RGB, 3
  28. 28. Conv Layer Feature map ? ?
  29. 29. Stride kernel size . 1, 2 1/4
  30. 30. Padding stride output side e ect Zero Padding(0 ) +
  31. 31. Pooling Layer
  32. 32. Pooling Stride
  33. 33. Pretrained Model

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