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1. Autoencoders and Sparsity
2. Backpropagation Algorithm
3. Backpropagation vectorization hints
4. Code
5. Data Preprocessing
6. Deep Networks: Overview
7. Deriving gradients using the backpropagation idea
8. Exercise:Convolution and Pooling
9. Exercise:Independent Component Analysis
10. Exercise:Learning color features with Sparse Autoencoders
11. Exercise:PCA and Whitening
12. Exercise:PCA in 2D
13. Exercise:Self-Taught Learning
14. Exercise:Softmax Regression
15. Exercise:Sparse Autoencoder
16. Exercise:Sparse Coding
17. Exercise:Vectorization
18. Exercise: Implement deep networks for digit classification
19. Exercise: PCA in 2D
20. Feature extraction using convolution
21. Fine-tuning Stacked AEs
22. Fminlbfgs Details
23. Gradient checking and advanced optimization
24. Implementing PCA/Whitening
25. Independent Component Analysis
26. Linear Decoders
27. Logistic Regression Vectorization Example
28. MATLAB Modules
29. Main Page
30. Neural Network Vectorization
31. Neural Networks
32. Neural Networks CN
33. PCA
34. Pooling
35. Reflist
36. SOFTMAX回归
37. Sandbox
38. Self-Taught Learning
39. Self-Taught Learning to Deep Networks
40. Softmax Regression
41. Softmax回归
42. Sparse Autoencoder Notation Summary
43. Sparse Coding
44. Sparse Coding: Autoencoder Interpretation
45. Stacked Autoencoders
46. Style Guide
47. UFLDL Recommended Readings
48. UFLDL Tutorial
49. UFLDL Tutorial CN
50. UFLDL教程
51. Useful Links
52. Using the MNIST Dataset
53. Vectorization
54. Visualization with PCA/Whitening
55. Visualizing a Trained Autoencoder
56. Whitening
57. Wiki documentation
58. 主成分分析
59. 从自我学习到深层网络
60. 卷积特征提取
61. 反向传导算法
62. 可视化自编码器训练结果
63. 实现主成分分析和白化
64. 微调多层自编码算法
65. 数据预处理
66. 栈式自编码算法
67. 梯度检验与高级优化
68. 池化
69. 深度网络概览
70. 独立成分分析
71. 用反向传导思想求导
72. 白化
73. 矢量化编程
74. 神经网络
75. 神经网络向量化
76. 稀疏编码
77. 稀疏编码自编码表达
78. 稀疏自编码器符号一览表
79. 稀疏自编码重述
80. 线性解码器
81. 自我学习
82. 自编码算法与稀疏性
83. 逻辑回归的向量化实现样例

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