UFLDL Tutorial

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* [[Logistic Regression Vectorization Example]]
* [[Logistic Regression Vectorization Example]]
* [[Neural Network Vectorization]]
* [[Neural Network Vectorization]]
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* [[Using the MNIST Dataset]]
 
* [[Exercise:Vectorization]]
* [[Exercise:Vectorization]]

Revision as of 01:32, 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

K-means

Spatial pyramids / Multiscale

Slow Feature Analysis

ICA Style Models:

Tiled Convolution Networks

Code

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