https://github.com/ysh329/Chinese-UFLDL-Tutorial

[UNMAINTAINED] 非监督特征学习与深度学习中文教程，该版本翻译自新版 UFLDL Tutorial 。建议新人们去学习斯坦福的CS231n课程，该门课程在网易云课堂上也有一个配有中文字幕的版本。
https://github.com/ysh329/Chinese-UFLDL-Tutorial

convolutional-neural-networks exercise sparse-autoencoders supervised-neural-network taught-learning unsupervised-learning

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[UNMAINTAINED] 非监督特征学习与深度学习中文教程，该版本翻译自新版 UFLDL Tutorial 。建议新人们去学习斯坦福的CS231n课程，该门课程在网易云课堂上也有一个配有中文字幕的版本。

• Host: GitHub
• URL: https://github.com/ysh329/Chinese-UFLDL-Tutorial
• Owner: ysh329
• License: apache-2.0
• Archived: true
• Created: 2015-09-13T15:52:02.000Z (almost 10 years ago)
• Default Branch: online
• Last Pushed: 2018-03-13T04:55:40.000Z (over 7 years ago)
• Last Synced: 2024-08-03T23:28:48.574Z (11 months ago)
• Topics: convolutional-neural-networks, exercise, sparse-autoencoders, supervised-neural-network, taught-learning, unsupervised-learning
• Homepage: https://github.com/ysh329/Chinese-UFLDL-Tutorial
• Size: 1.54 MB
• Stars: 350
• Watchers: 29
• Forks: 118
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
注意：这个项目我不再维护，我觉得我的翻译真的不够好来帮助其他人，尤其是那些刚入门或者刚开始学习了解深度学习、神经网络的人们。为了不误导其他人，我建议新人们去学习斯坦福的[CS231n课程](http://cs231n.github.io)，该门课程在[网易云课堂上也有一个配有中文字幕的版本](http://study.163.com/course/courseMain.htm?courseId=1003223001)。 Have fun!

为了极佳的阅读体验，您可点击 [这里](https://github.com/ysh329/Chinese-UFLDL-Tutorial/archive/master.zip) 将本文档下载到本地，并安装 [Haroopad](http://pad.haroopress.com/user.html#download) 进行阅读。

# 非监督特征学习与深度学习 中文教程

中文版的新版 UFLDL 教程（项目地址： www.github.com/ysh329/Chinese-UFLDL-Tutorial ），该版本翻译自 [UFLDL Tutorial](http://deeplearning.stanford.edu/tutorial/) ，是新版教程的翻译。也可参考 [旧版 UFLDL 中文教程](http://ufldl.stanford.edu/wiki/index.php/UFLDL教程) 。翻译过程中有一些数学公式，使用 [Haroopad](http://pad.haroopress.com/user.html#download) 编辑和排版， Haroopad 是一个优秀的离线 [MarkDown](https://en.wikipedia.org/wiki/Markdown) 编辑器，支持 [TeX](https://en.wikipedia.org/wiki/TeX) 公式编辑，支持多平台（Win/Mac/Linux），目前还在翻译中，翻译完成后会考虑使用 TeX 重新排版。  

自己对新版 UFLDL 教程翻译过程中，发现的英文错误，见 [新版教程英文原文勘误表](./新版教程英文原文勘误表.md) 。  

**注： UFLDL 是非监督特征学习及深度学习（Unsupervised Feature Learning and Deep Learning）的缩写，而不仅指深度学习（Deep Learning）。**  

-  翻译者：Shuai Yuan ，部分小节参考旧版翻译进行修正和补充。

-  若有翻译错误，请直接 [New issue](https://github.com/ysh329/Chinese-UFLDL-Tutorial/issues/new) 或 [发邮件](Mailto:[email protected]) ，感谢！  

>更多详细参考资料，见 [计算机科学](https://github.com/bayandin/awesome-awesomeness) ， [人工智能](https://github.com/owainlewis/awesome-artificial-intelligence) ， [机器学习](https://github.com/josephmisiti/awesome-machine-learning) ， [深度学习](https://github.com/ChristosChristofidis/awesome-deep-learning) ， [强化学习](https://github.com/aikorea/awesome-rl) ， [深度强化学习](https://github.com/junhyukoh/deep-reinforcement-learning-papers) ， [公开数据集](https://github.com/ChristosChristofidis/awesome-public-datasets) 。

# 欢迎来到新版 UFLDL 中文教程！

说明：本教程将会教给您非监督特征学习以及深度学习的主要思想。通过它，您将会实现几个特征学习或深度学习的算法，看到这些算法为您（的工作）带来作用，以及学习如何将这些思想应用到适用的新问题上。

本教程假定您已经有了基本的机器学习知识（具体而言，熟悉监督学习，逻辑斯特回归以及梯度下降法的思想）。如果您不熟悉这些，我们建议您先去 [机器学习课程](http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning) 中去学习，并完成其中的第II，III，IV章节（即到逻辑斯特回归）。

材料由以下人员提供：Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen, Adam Coates, Andrew Maas, Awni Hannun, Brody Huval, Tao Wang, Sameep Tandon

## 获取初学者代码（Starter Code）

### 初学者代码

您可以获得初学者所有练习的代码从 [该Github的代码仓库](https://github.com/amaas/stanford_dl_ex) 。  

有关的数据文件可以从 [这里](http://ai.stanford.edu/~amaas/data/data.zip) 下载。 下载到的数据需要解压到名为`“common”`的文件夹中（以便初学者代码的使用）。

# 目录

**每个小节后面的\[old\]\[new]\[旧\]分别代表：旧版英文、新版英文、旧版中文三个版本。若没有对应的版本则用\[无\]代替。**

* **预备知识（Miscellaneous）**

  * [MATLAB 文件指引（MATLAB Modules）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86%EF%BC%88Miscellaneous%20%EF%BC%89/MATLAB%E3%80%80%E6%96%87%E4%BB%B6%E6%8C%87%E5%BC%95%EF%BC%88MATLAB%20Modules%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/MATLAB_Modules)\]\[无\]\[无\]

  * [代码风格（Style Guide）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86%EF%BC%88Miscellaneous%20%EF%BC%89/%E4%BB%A3%E7%A0%81%E9%A3%8E%E6%A0%BC%EF%BC%88Style%20Guide%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Style_Guide)\]\[无\]\[无\]

  * [预备知识推荐（Useful Links）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86%EF%BC%88Miscellaneous%20%EF%BC%89/%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86%E6%8E%A8%E8%8D%90%EF%BC%88Useful%20Links%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.phssp/Useful_Links)\]\[无\]\[无\]

  * [推荐读物（UFLDL Recommended Readings）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86%EF%BC%88Miscellaneous%20%EF%BC%89/%E6%8E%A8%E8%8D%90%E8%AF%BB%E7%89%A9%EF%BC%88UFLDL%20Recommended%20Readings%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Recommended_Readings)\]\[无\]\[无\]

* **监督学习与优化（Supervised Learning and Optimization）**

  *  [线性回归（Linear Regression）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%EF%BC%88Linear%20Regression%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression)\]\[无\]

  *  [逻辑斯特回归（Logistic Regression）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/%E9%80%BB%E8%BE%91%E6%96%AF%E7%89%B9%E5%9B%9E%E5%BD%92%EF%BC%88Logistic%20Regression%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Logistic_Regression_Vectorization_Example)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/LogisticRegression)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92%E7%9A%84%E5%90%91%E9%87%8F%E5%8C%96%E5%AE%9E%E7%8E%B0%E6%A0%B7%E4%BE%8B)\]

  *  [向量化（Vectorization）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/%E5%90%91%E9%87%8F%E5%8C%96%EF%BC%88Vectorization%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Vectorization)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/Vectorization)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%9F%A2%E9%87%8F%E5%8C%96%E7%BC%96%E7%A8%8B)\]

  *  [调试：梯度检查（Debugging: Gradient Checking）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/%E8%B0%83%E8%AF%95%EF%BC%9A%E6%A2%AF%E5%BA%A6%E6%A3%80%E6%9F%A5%EF%BC%88Debugging%EF%BC%9AGradient%20Checking%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/DebuggingGradientChecking)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)\]

  *  [Softmax 回归（Softmax Regression）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/Softmax%E5%9B%9E%E5%BD%92%EF%BC%88Softmax%20Regression%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Softmax_Regression)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/Softmax%E5%9B%9E%E5%BD%92)\]

  *  [调试：偏差和方差（Debugging: Bias and Variance）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/%E6%A3%80%E6%9F%A5%EF%BC%9A%E5%81%8F%E5%B7%AE%E5%92%8C%E6%96%B9%E5%B7%AE%EF%BC%88Debugging%EF%BC%9ABias%20and%20Variance%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/DebuggingBiasAndVariance)\]\[无\]

  *  [调试：优化器和目标（Debugging: Optimizers and Objectives）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%E5%92%8C%E4%BC%98%E5%8C%96%EF%BC%88Supervised%20Learning%20and%20Optimization%EF%BC%89/%E8%B0%83%E8%AF%95%EF%BC%9A%E4%BC%98%E5%8C%96%E5%99%A8%E5%92%8C%E7%9B%AE%E6%A0%87%EF%BC%88Debugging%EF%BC%9AOptimizers%20and%20Objectives%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/DebuggingOptimizersAndObjectives)\]\[无\]

* **监督神经网络（Supervised Neural Networks）**

  *  [多层神经网络（Multi-Layer Neural Networks）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Neural%20Networks%EF%BC%89/%E5%A4%9A%E5%B1%82%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Multi-Layer%20Neural%20Networks%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Neural_Networks)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C)\]

   *  [神经网络向量化（Neural Network Vectorization）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Neural%20Networks%EF%BC%89/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%90%91%E9%87%8F%E5%8C%96%EF%BC%88Neural%20Network%20Vectorization%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Neural_Network_Vectorization)\]\[无\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%90%91%E9%87%8F%E5%8C%96#.E5.8F.8D.E5.90.91.E4.BC.A0.E6.92.AD)\]

   *  [练习：监督神经网络（Exercise: Supervised Neural Network）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Neural%20Networks%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%20%E7%9B%91%E7%9D%A3%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Exercise:%20Supervised%20Neural%20Networks%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/ExerciseSupervisedNeuralNetwork)\]\[无\]

* **监督卷积网络（Supervised Convolutional Neural Network）**

  *  [使用卷积进行特征提取（Feature Extraction Using Convolution）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Convolutional%20Neural%20Network%EF%BC%89/%E4%BD%BF%E7%94%A8%E5%8D%B7%E7%A7%AF%E8%BF%9B%E8%A1%8C%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96%EF%BC%88Feature%20Extraction%20Using%20Convolution%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E5%8D%B7%E7%A7%AF%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96)\]

  *  [池化（Pooling）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Convolutional%20Neural%20Network%EF%BC%89/%E6%B1%A0%E5%8C%96%EF%BC%88Pooling%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Pooling)\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/Pooling)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E6%B1%A0%E5%8C%96)\]

   * [练习：卷积和池化（Exercise: Convolution and Pooling）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Convolutional%20Neural%20Network%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E5%8D%B7%E7%A7%AF%E5%92%8C%E6%B1%A0%E5%8C%96%EF%BC%88Exercise:%20Convolution%20and%20Pooling%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/ExerciseConvolutionAndPooling)\]\[无\]

  *  [优化方法：随机梯度下降（Optimization: Stochastic Gradient Descent）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Convolutional%20Neural%20Network%EF%BC%89/%E4%BC%98%E5%8C%96%E6%96%B9%E6%B3%95%EF%BC%9A%E9%9A%8F%E6%9C%BA%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%EF%BC%88Optimization:%20Stochastic%20Gradient%20Descent%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent)\]\[无\]

  *  [卷积神经网络（Convolutional Neural Network）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Convolutional%20Neural%20Network%EF%BC%89/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Convolutional%20Neural%20Network%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork)\]\[无\]

   * [练习：卷积神经网络（Excercise: Convolutional Neural Network）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E7%9B%91%E7%9D%A3%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%EF%BC%88Supervised%20Convolutional%20Neural%20Network%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88Excercise:%20Convolutional%20Neural%20Network%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/supervised/ExerciseConvolutionalNeuralNetwork)\]\[无\]

* **无监督学习（Unsupervised Learning）**

  * [自动编码器（Autoencoders）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E8%87%AA%E5%8A%A8%E7%BC%96%E7%A0%81%E5%99%A8%EF%BC%88Autoencoders%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Autoencoders_and_Sparsity)\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E8%87%AA%E7%BC%96%E7%A0%81%E7%AE%97%E6%B3%95%E4%B8%8E%E7%A8%80%E7%96%8F%E6%80%A7)\]

   * [线性解码器（Linear Decoders）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BA%BF%E6%80%A7%E8%A7%A3%E7%A0%81%E5%99%A8%EF%BC%88Linear%20Decoders%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Linear_Decoders)][无][[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%BA%BF%E6%80%A7%E8%A7%A3%E7%A0%81%E5%99%A8)]

   * [练习：使用稀疏编码器学习颜色特征（Exercise:Learning color features with Sparse Autoencoders）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E4%BD%BF%E7%94%A8%E7%A8%80%E7%96%8F%E7%BC%96%E7%A0%81%E5%99%A8%E5%AD%A6%E4%B9%A0%E9%A2%9C%E8%89%B2%E7%89%B9%E5%BE%81%EF%BC%88Exercise:Learning%20color%20features%20with%20Sparse%20Autoencoders%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:Learning_color_features_with_Sparse_Autoencoders)][无][无]

   * [主成分分析白化（PCA Whitening）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E7%99%BD%E5%8C%96%EF%BC%88Exercise:%20PCA%20Whitening%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Implementing_PCA/Whitening)\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E5%AE%9E%E7%8E%B0%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E5%92%8C%E7%99%BD%E5%8C%96)\]

   * [练习：实现 2D 数据的主成分分析（Exercise:PCA in 2D）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E5%AE%9E%E7%8E%B0%202D%20%E6%95%B0%E6%8D%AE%E7%9A%84%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%EF%BC%88Exercise:PCA%20in%202D%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:PCA_in_2D)][无][无]

   * [练习：主成分分析白化（Exercise: PCA Whitening）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E7%99%BD%E5%8C%96%EF%BC%88Exercise:%20PCA%20Whitening%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:PCA_and_Whitening)\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/ExercisePCAWhitening)\]\[无\]

   * [稀疏编码（Sparse Coding）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%A8%80%E7%96%8F%E7%BC%96%E7%A0%81%EF%BC%88Sparse%20Coding%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Sparse_Coding)\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/SparseCoding/)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%A8%80%E7%96%8F%E7%BC%96%E7%A0%81)\]

   * [稀疏自编码符号一览表（Sparse Autoencoder Notation Summary）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%A8%80%E7%96%8F%E8%87%AA%E7%BC%96%E7%A0%81%E7%AC%A6%E5%8F%B7%E4%B8%80%E8%A7%88%E8%A1%A8%EF%BC%88Sparse%20Autoencoder%20Notation%20Summary%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Sparse_Autoencoder_Notation_Summary)\]\[无\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%A8%80%E7%96%8F%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8%E7%AC%A6%E5%8F%B7%E4%B8%80%E8%A7%88%E8%A1%A8)\]

   * [稀疏编码自编码表达（Sparse Coding: Autoencoder Interpretation）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%A8%80%E7%96%8F%E7%BC%96%E7%A0%81%E8%87%AA%E7%BC%96%E7%A0%81%E8%A1%A8%E8%BE%BE%EF%BC%88Sparse%20Coding:%20Autoencoder%20Interpretation%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Sparse_Coding:_Autoencoder_Interpretation)][无][[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%A8%80%E7%96%8F%E7%BC%96%E7%A0%81%E8%87%AA%E7%BC%96%E7%A0%81%E8%A1%A8%E8%BE%BE)]

   * [练习：稀疏编码（Exercise:Sparse Coding）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9A%E7%A8%80%E7%96%8F%E7%BC%96%E7%A0%81%EF%BC%88Exercise:Sparse%20Coding%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:Sparse_Coding)][无][无]

   * [独立成分分析（ICA）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%8B%AC%E7%AB%8B%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%EF%BC%88ICA%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Independent_Component_Analysis)\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/ICA)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%8B%AC%E7%AB%8B%E6%88%90%E5%88%86%E5%88%86%E6%9E%90)\]

   * [练习：独立成分分析（Exercise:Independent Component Analysis）](./无监督学习（Unsupervised Learning）/练习：独立成分分析（Exercise:Independent Component Analysis）.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:Independent_Component_Analysis)\]\[无\]\[无\]

   * [RICA（RICA）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%8B%AC%E7%AB%8B%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E9%87%8D%E5%BB%BA%EF%BC%88RICA%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/RICA)\]\[无\]

   * [练习：RICA（Exercise: RICA）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%BB%83%E4%B9%A0%EF%BC%9ARICA%EF%BC%88Exercise:%20RICA%EF%BC%89.md)\[无\]\[[new](http://ufldl.stanford.edu/tutorial/unsupervised/ExerciseRICA)\]\[无\]

   * 附1：[数据预处理（Data Preprocessing）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86%EF%BC%88Data%20Preprocessing%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing)\]\[无\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86)\]

   * 附2：[用反向传导思想求导（Deriving gradients using the backpropagation idea）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%88Unsupervised%20Learning%EF%BC%89/%E7%94%A8%E5%8F%8D%E5%90%91%E4%BC%A0%E5%AF%BC%E6%80%9D%E6%83%B3%E6%B1%82%E5%AF%BC%EF%BC%88Deriving%20gradients%20using%20the%20backpropagation%20idea%EF%BC%89.md)\[[old](http://ufldl.stanford.edu/wiki/index.php/Deriving_gradients_using_the_backpropagation_idea)\]\[无\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E7%94%A8%E5%8F%8D%E5%90%91%E4%BC%A0%E5%AF%BC%E6%80%9D%E6%83%B3%E6%B1%82%E5%AF%BC)\]

* **自我学习（Self-Taught Learning）**

  * [自我学习（Self-Taught Learning）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E8%87%AA%E6%88%91%E5%AD%A6%E4%B9%A0%EF%BC%88Self-Taught%20Learning%EF%BC%89/%E8%87%AA%E6%88%91%E5%AD%A6%E4%B9%A0%EF%BC%88Self-Taught%20Learning%EF%BC%89.md)\[[old](http://deeplearning.stanford.edu/wiki/index.php/Self-Taught_Learning)\]\[[new](http://ufldl.stanford.edu/tutorial/selftaughtlearning/SelfTaughtLearning)\]\[[旧](http://ufldl.stanford.edu/wiki/index.php/%E8%87%AA%E6%88%91%E5%AD%A6%E4%B9%A0)\]

   * [练习：自我学习（Exercise: Self-Taught Learning）](./自我学习（Self-Taught Learning）/练习：自我学习（Exercise: Self-Taught Learning）.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:Self-Taught_Learning)][[new](http://ufldl.stanford.edu/tutorial/selftaughtlearning/ExerciseSelfTaughtLearning)][无]

  * [深度网络概览（Deep Networks: Overview）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E8%87%AA%E6%88%91%E5%AD%A6%E4%B9%A0%EF%BC%88Self-Taught%20Learning%EF%BC%89/%E6%B7%B1%E5%BA%A6%E7%BD%91%E7%BB%9C%E6%A6%82%E8%A7%88%EF%BC%88Deep%20Networks:%20Overview%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Deep_Networks:_Overview)][无][[旧](http://ufldl.stanford.edu/wiki/index.php/%E6%B7%B1%E5%BA%A6%E7%BD%91%E7%BB%9C%E6%A6%82%E8%A7%88)]

  * [栈式自编码算法（Stacked Autoencoders）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E8%87%AA%E6%88%91%E5%AD%A6%E4%B9%A0%EF%BC%88Self-Taught%20Learning%EF%BC%89/%E6%A0%88%E5%BC%8F%E8%87%AA%E7%BC%96%E7%A0%81%E7%AE%97%E6%B3%95%EF%BC%88Stacked%20Autoencoders%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders)][无][[旧](http://ufldl.stanford.edu/wiki/index.php/%E6%A0%88%E5%BC%8F%E8%87%AA%E7%BC%96%E7%A0%81%E7%AE%97%E6%B3%95)]

  * [微调多层自编码算法（Fine-tuning Stacked AEs）](https://github.com/ysh329/Chinese-UFLDL-Tutorial/blob/online/%E8%87%AA%E6%88%91%E5%AD%A6%E4%B9%A0%EF%BC%88Self-Taught%20Learning%EF%BC%89/%E5%BE%AE%E8%B0%83%E5%A4%9A%E5%B1%82%E8%87%AA%E7%BC%96%E7%A0%81%E7%AE%97%E6%B3%95%EF%BC%88Fine-tuning%20Stacked%20AEs%EF%BC%89.md)[[old](http://ufldl.stanford.edu/wiki/index.php/Fine-tuning_Stacked_AEs)][无][[旧](http://ufldl.stanford.edu/wiki/index.php/%E5%BE%AE%E8%B0%83%E5%A4%9A%E5%B1%82%E8%87%AA%E7%BC%96%E7%A0%81%E7%AE%97%E6%B3%95)]

   * 练习：用深度网络实现数字分类（Exercise: Implement deep networks for digit classification）[[old](http://ufldl.stanford.edu/wiki/index.php/Exercise:_Implement_deep_networks_for_digit_classification)][无][无]

* **其它官方暂未写完的小节（Others）**

  * 卷积训练（Convolutional training）

  * 受限玻尔兹曼机（Restricted Boltzmann Machines）

  * 深度置信网络（Deep Belief Networks）

  * 降噪自编码器（Denoising Autoencoders）

  * K 均值（K-means）

  * 空间金字塔/多尺度（Spatial pyramids / Multiscale）

  * 慢特征分析（Slow Feature Analysis）

  * 平铺卷积网络（Tiled Convolution Networks）