UFLDL Tutorial

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(Boldfaced the topic headings.)
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sections II, III, IV (up to Logistic Regression) first.  
sections II, III, IV (up to Logistic Regression) first.  
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Sparse Autoencoder
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'''Sparse Autoencoder'''
* [[Neural Networks]]
* [[Neural Networks]]
* [[Backpropagation Algorithm]]
* [[Backpropagation Algorithm]]
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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.  
'''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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Vectorized implementation
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'''Vectorized implementation'''
* [[Vectorization]]
* [[Vectorization]]
* [[Logistic Regression Vectorization Example]]
* [[Logistic Regression Vectorization Example]]
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Preprocessing: PCA and Whitening
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'''Preprocessing: PCA and Whitening'''
* [[PCA]]
* [[PCA]]
* [[Whitening]]
* [[Whitening]]
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Softmax Regression
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'''Softmax Regression'''
* [[Softmax Regression]]
* [[Softmax Regression]]
* [[Exercise:Softmax Regression]]
* [[Exercise:Softmax Regression]]
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Self-Taught Learning and Unsupervised Feature Learning  
+
'''Self-Taught Learning and Unsupervised Feature Learning'''
* [[Self-Taught Learning]]
* [[Self-Taught Learning]]
* [[Exercise:Self-Taught Learning]]
* [[Exercise:Self-Taught Learning]]
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Building Deep Networks for Classification
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'''Building Deep Networks for Classification'''
* [[Deep Networks: Overview]]
* [[Deep Networks: Overview]]
* [[Stacked Autoencoders]]
* [[Stacked Autoencoders]]
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Working with Large Images
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'''Working with Large Images'''
* [[Feature extraction using convolution]]
* [[Feature extraction using convolution]]
* [[Pooling]]
* [[Pooling]]

Revision as of 20:46, 22 April 2011

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



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.

Vectorized implementation


Preprocessing: PCA and Whitening


Softmax Regression


Self-Taught Learning and Unsupervised Feature Learning


Building Deep Networks for Classification


Working with Large Images




Advanced Topics:

Restricted Boltzmann Machines

Deep Belief Networks

Denoising Autoencoders

Sparse Coding

K-means

Spatial pyramids / Multiscale

Slow Feature Analysis

ICA Style Models:

Tiled Convolution Networks

Code

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