# UFLDL Tutorial

### From Ufldl

(Difference between revisions)

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Working with Large Images | Working with Large Images | ||

- | * Feature extraction using convolution | + | * [[Feature extraction using convolution]] |

- | * Pooling | + | * [[Pooling]] |

- | * Multiple layers of convolution and pooling | + | * [[Multiple layers of convolution and pooling]] |

'''Advanced Topics''': | '''Advanced Topics''': |

## Revision as of 00:57, 19 April 2011

Sparse Autoencoder

- Neural Networks
- Backpropagation Algorithm
- Gradient checking and advanced optimization
- Autoencoders and Sparsity
- Visualizing a Trained Autoencoder
- Sparse Autoencoder Notation Summary
- Exercise:Sparse Autoencoder

Vectorized implementation

- Vectorization
- Logistic Regression Vectorization Example
- Neural Network Vectorization
- Using the MNIST Dataset
- Exercise:Vectorization

Preprocessing: PCA and Whitening

Softmax Regression

Self-Taught Learning and Unsupervised Feature Learning

Building Deep Networks for Classification

- Stacked Autoencoders
- Fine-tuning Stacked AEs
- Exercise: Implement deep networks for digit classification

Working with Large Images

**Advanced Topics**:

ICA Style Models: