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  1. Reflist ‎(02:42, 27 February 2011)
  2. Backpropagation vectorization hints ‎(01:42, 2 March 2011)
  3. Sandbox ‎(11:50, 23 March 2011)
  4. Visualization with PCA/Whitening ‎(20:20, 6 April 2011)
  5. Exercise: PCA in 2D ‎(23:57, 10 April 2011)
  6. Fminlbfgs Details ‎(01:15, 22 April 2011)
  7. Code ‎(01:25, 22 April 2011)
  8. Wiki documentation ‎(16:57, 22 April 2011)
  9. Style Guide ‎(02:16, 29 April 2011)
  10. MATLAB Modules ‎(19:55, 29 April 2011)
  11. Using the MNIST Dataset ‎(14:46, 3 May 2011)
  12. Exercise:Vectorization ‎(11:00, 26 May 2011)
  13. Exercise:PCA in 2D ‎(11:01, 26 May 2011)
  14. Exercise:PCA and Whitening ‎(11:01, 26 May 2011)
  15. Exercise:Softmax Regression ‎(11:02, 26 May 2011)
  16. Exercise:Self-Taught Learning ‎(11:02, 26 May 2011)
  17. Exercise: Implement deep networks for digit classification ‎(11:04, 26 May 2011)
  18. Exercise:Sparse Coding ‎(06:59, 29 May 2011)
  19. Exercise:Convolution and Pooling ‎(19:16, 3 June 2011)
  20. Useful Links ‎(17:18, 9 June 2011)
  21. Exercise:Learning color features with Sparse Autoencoders ‎(21:00, 21 June 2011)
  22. Exercise:Independent Component Analysis ‎(04:31, 4 October 2011)
  23. UFLDL Recommended Readings ‎(07:00, 18 February 2012)
  24. Exercise:Sparse Autoencoder ‎(14:34, 10 July 2012)
  25. UFLDL Tutorial CN ‎(13:57, 5 March 2013)
  26. SOFTMAX回归 ‎(15:15, 11 March 2013)
  27. 稀疏自编码重述 ‎(12:45, 19 March 2013)
  28. Neural Networks CN ‎(00:27, 29 March 2013)
  29. Main Page ‎(01:25, 31 March 2013)
  30. Neural Networks ‎(19:38, 6 April 2013)
  31. 神经网络 ‎(12:34, 7 April 2013)
  32. Gradient checking and advanced optimization ‎(12:40, 7 April 2013)
  33. Autoencoders and Sparsity ‎(12:43, 7 April 2013)
  34. Sparse Autoencoder Notation Summary ‎(12:45, 7 April 2013)
  35. Visualizing a Trained Autoencoder ‎(12:49, 7 April 2013)
  36. Backpropagation Algorithm ‎(12:50, 7 April 2013)
  37. Vectorization ‎(13:07, 7 April 2013)
  38. Logistic Regression Vectorization Example ‎(13:09, 7 April 2013)
  39. Neural Network Vectorization ‎(13:13, 7 April 2013)
  40. PCA ‎(13:18, 7 April 2013)
  41. Whitening ‎(13:20, 7 April 2013)
  42. Implementing PCA/Whitening ‎(13:22, 7 April 2013)
  43. Softmax Regression ‎(13:24, 7 April 2013)
  44. Self-Taught Learning ‎(13:26, 7 April 2013)
  45. Self-Taught Learning to Deep Networks ‎(13:29, 7 April 2013)
  46. Deep Networks: Overview ‎(13:31, 7 April 2013)
  47. Stacked Autoencoders ‎(13:33, 7 April 2013)
  48. UFLDL Tutorial ‎(18:22, 7 April 2013)
  49. Fine-tuning Stacked AEs ‎(04:04, 8 April 2013)
  50. Linear Decoders ‎(04:06, 8 April 2013)

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