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  1. Exercise:Sparse Autoencoder ‎(24 links)
  2. Backpropagation Algorithm ‎(14 links)
  3. Exercise:PCA and Whitening ‎(12 links)
  4. Exercise:Vectorization ‎(12 links)
  5. Exercise: Implement deep networks for digit classification ‎(12 links)
  6. Neural Networks ‎(12 links)
  7. Autoencoders and Sparsity ‎(11 links)
  8. Exercise:PCA in 2D ‎(11 links)
  9. Exercise:Softmax Regression ‎(10 links)
  10. Gradient checking and advanced optimization ‎(10 links)
  11. Sparse Autoencoder Notation Summary ‎(10 links)
  12. Visualizing a Trained Autoencoder ‎(10 links)
  13. Whitening ‎(10 links)
  14. 神经网络 ‎(10 links)
  15. PCA ‎(9 links)
  16. 反向传导算法 ‎(9 links)
  17. 自编码算法与稀疏性 ‎(9 links)
  18. /Print ‎(8 links)
  19. Exercise:Self-Taught Learning ‎(8 links)
  20. Implementing PCA/Whitening ‎(8 links)
  21. Sparse Coding: Autoencoder Interpretation ‎(8 links)
  22. Wikipedia:Template documentation ‎(8 links)
  23. Wikipedia:Transclusion ‎(8 links)
  24. 可视化自编码器训练结果 ‎(8 links)
  25. 梯度检验与高级优化 ‎(8 links)
  26. 稀疏自编码器符号一览表 ‎(8 links)
  27. Template:Main other ‎(8 links)
  28. Help:Books/for experts ‎(8 links)
  29. Deep Networks: Overview ‎(7 links)
  30. Fine-tuning Stacked AEs ‎(7 links)
  31. Independent Component Analysis ‎(7 links)
  32. Logistic Regression Vectorization Example ‎(7 links)
  33. Neural Network Vectorization ‎(7 links)
  34. Self-Taught Learning to Deep Networks ‎(7 links)
  35. Stacked Autoencoders ‎(7 links)
  36. Exercise:Convolution and Pooling ‎(6 links)
  37. Exercise:Learning color features with Sparse Autoencoders ‎(6 links)
  38. Vectorization ‎(6 links)
  39. 从自我学习到深层网络 ‎(6 links)
  40. 微调多层自编码算法 ‎(6 links)
  41. 栈式自编码算法 ‎(6 links)
  42. 深度网络概览 ‎(6 links)
  43. Deriving gradients using the backpropagation idea ‎(5 links)
  44. Softmax Regression ‎(5 links)
  45. Using the MNIST Dataset ‎(5 links)
  46. 主成分分析 ‎(5 links)
  47. 实现主成分分析和白化 ‎(5 links)
  48. 白化 ‎(5 links)
  49. 矢量化编程 ‎(5 links)
  50. 神经网络向量化 ‎(5 links)

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