├── .idea └── encodings.xml ├── LICENSE ├── README.md ├── 新版教程英文原文勘误表.md ├── 无监督学习（Unsupervised Learning） ├── images │ ├── 240px-STL_SparseAE.png │ ├── 400px-Backpropagation_Method_Example_1.png │ ├── 400px-Backpropagation_Method_Example_3.png │ ├── 600px-Backpropagation_Method_Example_2.png │ ├── Autoencoder636.png │ ├── ExampleSparseAutoencoderWeights.png │ ├── KLPenaltyExample.png │ ├── PCA-rawdata.png │ ├── PCA-rotated.png │ ├── PCA-u1.png │ ├── PCA-whitened.png │ ├── PCA-xhat.png │ ├── PCA-xtilde.png │ ├── ZCA-whitened.png │ └── ZCA_Eigenvalues_Plot.png ├── 主成分分析白化（PCA Whitening）.md ├── 数据预处理（Data Preprocessing）.md ├── 独立成分分析重建（RICA）.md ├── 独立成分分析（ICA）.md ├── 用反向传导思想求导（Deriving gradients using the backpropagation idea）.md ├── 稀疏编码自编码表达（Sparse Coding: Autoencoder Interpretation）.md ├── 稀疏编码（Sparse Coding）.md ├── 稀疏自编码符号一览表（Sparse Autoencoder Notation Summary）.md ├── 线性解码器（Linear Decoders）.md ├── 练习：RICA（Exercise: RICA）.md ├── 练习：主成分分析白化（Exercise: PCA Whitening）.md ├── 练习：使用稀疏编码器学习颜色特征（Exercise:Learning color features with Sparse Autoencoders）.md ├── 练习：实现 2D 数据的主成分分析（Exercise:PCA in 2D）.md ├── 练习：稀疏编码（Exercise:Sparse Coding）.md └── 自动编码器（Autoencoders）.md ├── 监督卷积网络（Supervised Convolutional Neural Network） ├── images │ ├── Cnn_layer.png │ ├── Convolution_schematic.gif │ └── Pooling_schematic.gif ├── 优化方法：随机梯度下降（Optimization: Stochastic Gradient Descent）.md ├── 使用卷积进行特征提取（Feature Extraction Using Convolution）.md ├── 卷积神经网络（Convolutional Neural Network）.md ├── 池化（Pooling）.md ├── 练习：卷积和池化（Exercise: Convolution and Pooling）.md └── 练习：卷积神经网络（Excercise: Convolutional Neural Network）.md ├── 监督学习和优化（Supervised Learning and Optimization） ├── Softmax回归（Softmax Regression）.md ├── images │ ├── House_results.png │ └── Mnist_01.png ├── 向量化（Vectorization）.md ├── 检查：偏差和方差（Debugging：Bias and Variance）.md ├── 线性回归（Linear Regression）.md ├── 调试：优化器和目标（Debugging：Optimizers and Objectives）.md ├── 调试：梯度检查（Debugging：Gradient Checking）.md └── 逻辑斯特回归（Logistic Regression）.md ├── 监督神经网络（Supervised Neural Networks） ├── images │ ├── Activation_functions.png │ ├── Network331.png │ ├── Network3322.png │ └── SingleNeuron.png ├── 多层神经网络（Multi-Layer Neural Networks）.md ├── 神经网络向量化（Neural Network Vectorization）.md └── 练习： 监督神经网络（Exercise: Supervised Neural Networks）.md ├── 自我学习（Self-Taught Learning） ├── images │ ├── 400px-Stacked_Softmax_Classifier.png │ ├── 400px-Stacked_SparseAE_Features1.png │ ├── 400px-Stacked_SparseAE_Features2.png │ ├── 500px-Stacked_Combined.png │ ├── STL_SparseAE.png │ └── STL_SparseAE_Features.png ├── 微调多层自编码算法（Fine-tuning Stacked AEs）.md ├── 栈式自编码算法（Stacked Autoencoders）.md ├── 深度网络概览（Deep Networks: Overview）.md ├── 练习：自我学习（Exercise: Self-Taught Learning）.md └── 自我学习（Self-Taught Learning）.md └── 预备知识（Miscellaneous ） ├── MATLAB　文件指引（MATLAB Modules）.md ├── 代码风格（Style Guide）.md ├── 推荐读物（UFLDL Recommended Readings）.md └── 预备知识推荐（Useful Links）.md /.idea/encodings.xml: 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-------------------------------------------------------------------------------- 1 | #调试：优化器和目标（Debugging: Optimizers and Objectives） 2 | ## 3 | 这一节留下（大坑）待写（译者注：很多年了，该大坑一直没被填上）。 4 | -------------------------------------------------------------------------------- /监督学习和优化（Supervised Learning and Optimization）/调试：梯度检查（Debugging：Gradient Checking）.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ysh329/Chinese-UFLDL-Tutorial/HEAD/监督学习和优化（Supervised Learning and Optimization）/调试：梯度检查（Debugging：Gradient Checking）.md -------------------------------------------------------------------------------- /监督学习和优化（Supervised Learning and Optimization）/逻辑斯特回归（Logistic Regression）.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ysh329/Chinese-UFLDL-Tutorial/HEAD/监督学习和优化（Supervised Learning and Optimization）/逻辑斯特回归（Logistic Regression）.md 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