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  1. (hist) ‎SOFTMAX回归 ‎[0 bytes]
  2. (hist) ‎Code ‎[25 bytes]
  3. (hist) ‎UFLDL Tutorial CN ‎[41 bytes]
  4. (hist) ‎Exercise: PCA in 2D ‎[46 bytes]
  5. (hist) ‎Visualization with PCA/Whitening ‎[67 bytes]
  6. (hist) ‎Neural Networks CN ‎[143 bytes]
  7. (hist) ‎Style Guide ‎[300 bytes]
  8. (hist) ‎Main Page ‎[355 bytes]
  9. (hist) ‎Useful Links ‎[447 bytes]
  10. (hist) ‎Sandbox ‎[498 bytes]
  11. (hist) ‎Wiki documentation ‎[610 bytes]
  12. (hist) ‎Using the MNIST Dataset ‎[1,050 bytes]
  13. (hist) ‎Fminlbfgs Details ‎[1,433 bytes]
  14. (hist) ‎MATLAB Modules ‎[1,947 bytes]
  15. (hist) ‎Backpropagation vectorization hints ‎[2,601 bytes]
  16. (hist) ‎Fine-tuning Stacked AEs ‎[2,643 bytes]
  17. (hist) ‎UFLDL教程 ‎[2,742 bytes]
  18. (hist) ‎Pooling ‎[2,823 bytes]
  19. (hist) ‎微调多层自编码算法 ‎[2,890 bytes]
  20. (hist) ‎矢量化编程 ‎[2,932 bytes]
  21. (hist) ‎稀疏自编码器符号一览表 ‎[3,017 bytes]
  22. (hist) ‎Vectorization ‎[3,063 bytes]
  23. (hist) ‎UFLDL Tutorial ‎[3,125 bytes]
  24. (hist) ‎Visualizing a Trained Autoencoder ‎[3,147 bytes]
  25. (hist) ‎Sparse Autoencoder Notation Summary ‎[3,204 bytes]
  26. (hist) ‎池化 ‎[3,297 bytes]
  27. (hist) ‎可视化自编码器训练结果 ‎[3,356 bytes]
  28. (hist) ‎稀疏自编码重述 ‎[3,750 bytes]
  29. (hist) ‎Implementing PCA/Whitening ‎[3,827 bytes]
  30. (hist) ‎Exercise:Learning color features with Sparse Autoencoders ‎[3,851 bytes]
  31. (hist) ‎Exercise:PCA in 2D ‎[4,081 bytes]
  32. (hist) ‎Linear Decoders ‎[4,182 bytes]
  33. (hist) ‎实现主成分分析和白化 ‎[4,302 bytes]
  34. (hist) ‎Exercise:Sparse Coding ‎[4,304 bytes]
  35. (hist) ‎Exercise:Vectorization ‎[4,320 bytes]
  36. (hist) ‎Exercise:Self-Taught Learning ‎[4,327 bytes]
  37. (hist) ‎线性解码器 ‎[4,337 bytes]
  38. (hist) ‎逻辑回归的向量化实现样例 ‎[4,444 bytes]
  39. (hist) ‎Exercise:Independent Component Analysis ‎[4,535 bytes]
  40. (hist) ‎Independent Component Analysis ‎[4,587 bytes]
  41. (hist) ‎Logistic Regression Vectorization Example ‎[4,621 bytes]
  42. (hist) ‎独立成分分析 ‎[4,791 bytes]
  43. (hist) ‎卷积特征提取 ‎[4,929 bytes]
  44. (hist) ‎从自我学习到深层网络 ‎[5,011 bytes]
  45. (hist) ‎Feature extraction using convolution ‎[5,057 bytes]
  46. (hist) ‎Exercise: Implement deep networks for digit classification ‎[5,274 bytes]
  47. (hist) ‎Self-Taught Learning to Deep Networks ‎[5,486 bytes]
  48. (hist) ‎Stacked Autoencoders ‎[6,007 bytes]
  49. (hist) ‎白化 ‎[6,350 bytes]
  50. (hist) ‎栈式自编码算法 ‎[6,457 bytes]

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