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- Autoencoders and Sparsity
- Backpropagation Algorithm
- Backpropagation vectorization hints
- Code
- Data Preprocessing
- Deep Networks: Overview
- Deriving gradients using the backpropagation idea
- Exercise:Convolution and Pooling
- Exercise:Independent Component Analysis
- Exercise:Learning color features with Sparse Autoencoders
- Exercise:Sparse Coding
- Exercise:Vectorization
- Exercise: Implement deep networks for digit classification
- Feature extraction using convolution
- Fine-tuning Stacked AEs
- Fminlbfgs Details
- Gradient checking and advanced optimization
- Implementing PCA/Whitening
- Independent Component Analysis
- Linear Decoders
- Logistic Regression Vectorization Example
- MATLAB Modules
- Main Page
- Neural Network Vectorization
- Neural Networks
- Neural Networks CN
- PCA
- Pooling
- SOFTMAX回归
- Sandbox
- Self-Taught Learning
- Self-Taught Learning to Deep Networks
- Softmax Regression
- Softmax回归
- Sparse Autoencoder Notation Summary
- Sparse Coding
- Sparse Coding: Autoencoder Interpretation
- Stacked Autoencoders
- Style Guide
- UFLDL Recommended Readings
- UFLDL Tutorial
- UFLDL Tutorial CN
- UFLDL教程
- Useful Links
- Using the MNIST Dataset
- Vectorization
- Visualization with PCA/Whitening
- Visualizing a Trained Autoencoder
- Whitening
- Wiki documentation