# UFLDL Tutorial

### From Ufldl

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* [[Exercise:PCA in 2D]] | * [[Exercise:PCA in 2D]] | ||

* [[Exercise:PCA and Whitening]] | * [[Exercise:PCA and Whitening]] | ||

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+ | Softmax Regression | ||

+ | * [[Softmax Regression]] | ||

+ | * [[Exercise:Softmax Regression]] | ||

Self-Taught Learning and Unsupervised Feature Learning | Self-Taught Learning and Unsupervised Feature Learning | ||

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* [[Self-Taught Learning]] | * [[Self-Taught Learning]] | ||

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* [[Exercise:Self-Taught Learning]] | * [[Exercise:Self-Taught Learning]] | ||

Building Deep Networks for Classification | Building Deep Networks for Classification | ||

- | * [[ | + | * [[Stacked Autoencoders]] |

- | * [[Exercise: | + | * [[Fine-tuning Stacked AEs]] |

+ | * [[Exercise: Implement deep networks for digit classification]] | ||

- | + | Working with Large Images | |

- | + | * Feature extraction using convolution | |

+ | * Pooling | ||

+ | * Multiple layers of convolution and pooling | ||

- | + | '''Advanced Topics''': | |

- | + | [[Restricted Boltzmann Machines]] | |

- | + | [[Deep Belief Networks]] | |

- | + | [[Sparse Coding]] | |

- | + | [[K-means]] | |

- | + | [[Spatial pyramids / Multiscale]] | |

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- | + | ||

- | + | ||

- | + | ||

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- | [[ | + | |

- | + | [[Slow Feature Analysis]] | |

- | + | ICA Style Models: | |

+ | * [[Independent Component Analysis]] | ||

+ | * [[Topographic Independent Component Analysis]] | ||

- | |||

- | + | [[Tiled Convolution Networks]] |

## 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

- Feature extraction using convolution
- Pooling
- Multiple layers of convolution and pooling

**Advanced Topics**:

ICA Style Models: