UFLDL Tutorial

'''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 [http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning Machine Learning course] and complete
sections II, III, IV (up to Logistic Regression) first. 


'''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]]
* [[Exercise:Vectorization]]


'''Preprocessing: PCA and Whitening'''
* [[PCA]]
* [[Whitening]]
* [[Implementing PCA/Whitening]]
* [[Exercise:PCA in 2D]]
* [[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. 


'''Softmax Regression'''
* [[Softmax Regression]]
* [[Exercise:Softmax Regression]]


'''Self-Taught Learning and Unsupervised Feature Learning''' 
* [[Self-Taught Learning]]
* [[Exercise:Self-Taught Learning]]


'''Building Deep Networks for Classification'''
* [[Self-Taught Learning to Deep Networks | From Self-Taught Learning to Deep Networks]]
* [[Deep Networks: Overview]]
* [[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]]

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'''Miscellaneous''':

[[Linear Decoders]]

[[MATLAB Modules]]

[[Data Preprocessing]]

[[Style Guide]]

'''Advanced Topics''':

[[Restricted Boltzmann Machines]]

[[Deep Belief Networks]]

[[Denoising Autoencoders]]

[[Sparse Coding]]

[[K-means]]

[[Spatial pyramids / Multiscale]]

[[Slow Feature Analysis]]

ICA Style Models:
* [[Independent Component Analysis]]
* [[Topographic Independent Component Analysis]]

[[Tiled Convolution Networks]]

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