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21. Using the MNIST Dataset ‎(11 revisions)
22. 稀疏自编码器符号一览表 ‎(11 revisions)
23. 线性解码器 ‎(11 revisions)
24. Exercise:PCA in 2D ‎(13 revisions)
25. Stacked Autoencoders ‎(13 revisions)
26. Backpropagation Algorithm ‎(14 revisions)
27. 实现主成分分析和白化 ‎(14 revisions)
28. Exercise:Sparse Autoencoder ‎(15 revisions)
29. Neural Network Vectorization ‎(16 revisions)
30. Self-Taught Learning to Deep Networks ‎(17 revisions)
31. 卷积特征提取 ‎(17 revisions)
32. 矢量化编程 ‎(17 revisions)
33. Whitening ‎(18 revisions)
34. Exercise:Learning color features with Sparse Autoencoders ‎(19 revisions)
35. Sparse Coding: Autoencoder Interpretation ‎(19 revisions)
36. 梯度检验与高级优化 ‎(19 revisions)
37. 稀疏自编码重述 ‎(20 revisions)
38. Deep Networks: Overview ‎(21 revisions)
39. Self-Taught Learning ‎(21 revisions)
40. 栈式自编码算法 ‎(21 revisions)
41. Implementing PCA/Whitening ‎(22 revisions)
42. 可视化自编码器训练结果 ‎(22 revisions)
43. Neural Networks ‎(23 revisions)
44. 白化 ‎(23 revisions)
45. 自我学习 ‎(23 revisions)
46. Exercise: Implement deep networks for digit classification ‎(25 revisions)
47. 从自我学习到深层网络 ‎(25 revisions)
48. 微调多层自编码算法 ‎(25 revisions)
49. Data Preprocessing ‎(26 revisions)
50. 独立成分分析 ‎(27 revisions)
51. Fine-tuning Stacked AEs ‎(28 revisions)
52. 用反向传导思想求导 ‎(29 revisions)
53. 逻辑回归的向量化实现样例 ‎(30 revisions)
54. UFLDL Recommended Readings ‎(31 revisions)
55. Linear Decoders ‎(32 revisions)
56. 深度网络概览 ‎(32 revisions)
57. Softmax回归 ‎(34 revisions)
58. 池化 ‎(34 revisions)
59. 自编码算法与稀疏性 ‎(35 revisions)
60. Main Page ‎(36 revisions)
61. Exercise:PCA and Whitening ‎(37 revisions)
62. Exercise:Self-Taught Learning ‎(38 revisions)
63. 数据预处理 ‎(38 revisions)
64. Exercise:Vectorization ‎(39 revisions)
65. PCA ‎(43 revisions)
66. 神经网络向量化 ‎(44 revisions)
67. Feature extraction using convolution ‎(45 revisions)
68. Exercise:Softmax Regression ‎(46 revisions)
69. 稀疏编码 ‎(48 revisions)
70. 反向传导算法 ‎(52 revisions)

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