UFLDL Tutorial

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* [[Independent Component Analysis]]
* [[Independent Component Analysis]]
* [[Topographic Independent Component Analysis]]
* [[Topographic Independent Component Analysis]]
[[Tiled Convolution Networks]]
[[Tiled Convolution Networks]]

Revision as of 01:24, 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

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


Spatial pyramids / Multiscale

Slow Feature Analysis

ICA Style Models:

Tiled Convolution Networks


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