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

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