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A yet another brief introduction to neural networks
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A yet another brief introduction to neural networks
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1. A yet another brief introduction to Neural Networks
2. Neural Network x z y
3. x z y Neural Network
4. x z y 784 10 MNIST (28*28) [0.05, 0.05, 0.05, 0.40, 0.05, 0.05, 0.15, 0.05, 0.15, 0.05] 10 0 1 ... 9 28*28= 784
5. x z y Neuron j W1 j
6. j W1 j k k W2 x z y Neuron
7. x z y W1 W2
8. Neural Network - Forward propagation - Back propagation - Parameter update
9. Neural Network - Forward propagation - Back propagation - Parameter update
10. Forward Propagation x z y output x input y
11. x z y z = f(W1x + b1) f( ) tanh sigmoid f(x0) f(x1) f(x2) f(x3) f(x) =
12. x z y z = f(W1x + b1) f( ) tanh,sigmoid reLU, maxout... f(x0) f(x1) f(x2) f(x3) f(x) = f( )
13. x z y z = f(W1x + b1) f( )
14. x z y z = f(W1x + b1) f( ) f( ) tanh,sigmoid reLU, maxout... f(x0) f(x1) f(x2) f(x3) f(x) =
15. x z y ( ) y = (W2z + b2)
16. x z y ( ) y = (W2z + b2)
17. x z y - - - - y = (W2z + b2)
18. x z y (a) = a y = (W2z + b2)
19. [0:1] 0.0~1.0 (a) = 1 1+exp( a) Sigmoidx z y y = (W2z + b2)
20. x z y 01 Softmax sum( 0.2 0.7 0.1 )=1.0 (a) = exp(a) exp(a) y = (W2z + b2)
21. x z y (a) = 1 1+exp( a) element-wise Sigmoid [0:1] [0:1] [0:1] y = (W2z + b2)
22. ... 1 N ...
23. Forward Propagation x z y z = f(W1x + b1) y = (W2z + b2)
24. Neural Network - Forward propagation - Back propagation - Parameter update
25. Back Propagation x z y t NN
26. Back Propagation x z y t NN : z = f(W1x + b1) y = (W2z + b2)
27. Back Propagation x z y t E( ) = 1 2 y( ) t 2 E = K k=1 tk log yk E = t log y (1 t) log(1 y) E = K k=1 t log y + (1 t) log(1 y)
28. Back Propagation x z y t y = y t
29. Back Propagation x z y t z = WT 2 yf (az) y = y t
30. Back Propagation x z y t y = y t z = WT 2 yf (az)
31. Back Propagation x z y t En W2 = yzT En W1 = zxT
32. Back Propagation x z y t En W2 = yzT En W1 = zxT
33. Neural Network - Forward propagation - Back propagation - Parameter update
34. Update parameters x z y t W1 = W1 En W1 W2 = W2 En W2
35. Update parameters x z y t W1 = W1 En W1 W2 = W2 En W2 -Gradient Descent -Stochastic Gradient Descent -SGD with mini-batch
36. Neural Network
37. Forward Propagation x z y output x input y z = f(W1x + b1) y = (W2z + b2)
38. Back Propagation x z y t z = WT 2 yf (az) y = y t En W2 = yzT En W1 = zxT
39. Update parameters x z y t W1 = W1 En W1 W2 = W2 En W2
40. Autoencoder Neural Network 1. 2. 1 1 ()
41. Neural Network Autoencoder z = f(W1x + b1) y = (W2z + b2)
42. Neural Network Autoencoder y = (W2z + b2) (a) = 1 1+exp( a) element-wise Sigmoid () (0.0:, 1.0:)
43. Autoencoder
44. Denoising Autoencoder add noise denoise +
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