UFLDL Tutorial

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* [[Exercise:PCA in 2D]]
* [[Exercise:PCA in 2D]]
* [[Exercise:PCA and Whitening]]
* [[Exercise:PCA and Whitening]]
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'''Note''': The sections above this line are stable.  The sections below are still under construction, and may change without notice.  Feel free to browse around however, and feedback/suggestions are welcome.
 
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* [[Fine-tuning Stacked AEs]]
* [[Fine-tuning Stacked AEs]]
* [[Exercise: Implement deep networks for digit classification]]
* [[Exercise: Implement deep networks for digit classification]]
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'''Linear Decoders with Autoencoders'''
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* [[Linear Decoders]]
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* [[Exercise:Learning color features with Sparse Autoencoders]]
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* [[Feature extraction using convolution]]
* [[Feature extraction using convolution]]
* [[Pooling]]
* [[Pooling]]
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* [[Multiple layers of convolution and pooling]]
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* [[Exercise:Convolution and Pooling]]
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'''Note''': The sections above this line are stable.  The sections below are still under construction, and may change without notice.  Feel free to browse around however, and feedback/suggestions are welcome.
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'''Miscellaneous''':
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'''Miscellaneous'''
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* [[MATLAB Modules]]
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[[Linear Decoders]
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* [[Style Guide]]
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* [[Useful Links]]
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[[MATLAB Modules]]
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[[Data Preprocessing]]
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[[Style Guide]]
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'''Miscellaneous Topics'''
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* [[Data Preprocessing]]
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* [[Deriving gradients using the backpropagation idea]]
'''Advanced Topics''':
'''Advanced Topics''':
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[[Restricted Boltzmann Machines]]
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'''Sparse Coding'''
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* [[Sparse Coding]]
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* [[Sparse Coding: Autoencoder Interpretation]]
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* [[Exercise:Sparse Coding]]
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[[Deep Belief Networks]]
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'''ICA Style Models'''
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[[Denoising Autoencoders]]
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[[Sparse Coding]]
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[[K-means]]
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[[Spatial pyramids / Multiscale]]
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[[Slow Feature Analysis]]
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ICA Style Models:
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* [[Independent Component Analysis]]
* [[Independent Component Analysis]]
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* [[Topographic Independent Component Analysis]]
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* [[Exercise:Independent Component Analysis]]
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[[Tiled Convolution Networks]]
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'''Others'''
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* [[Convolutional training]]
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* [[Restricted Boltzmann Machines]]
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* [[Deep Belief Networks]]
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* [[Denoising Autoencoders]]
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* [[K-means]]
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* [[Spatial pyramids / Multiscale]]
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* [[Slow Feature Analysis]]
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* [[Tiled Convolution Networks]]
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Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen
Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen
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{{Languages|UFLDL教程|中文}}

Latest revision as of 18:22, 7 April 2013

Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.


Sparse Autoencoder


Vectorized implementation


Preprocessing: PCA and Whitening


Softmax Regression


Self-Taught Learning and Unsupervised Feature Learning


Building Deep Networks for Classification


Linear Decoders with Autoencoders


Working with Large Images


Note: The sections above this line are stable. The sections below are still under construction, and may change without notice. Feel free to browse around however, and feedback/suggestions are welcome.

Miscellaneous

Miscellaneous Topics

Advanced Topics:

Sparse Coding

ICA Style Models

Others


Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen


Language : 中文

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