MATLAB Modules

== MATLAB Modules ==

'''[[Exercise:Sparse_Autoencoder|Sparse autoencoder]]''' | [http://ufldl.stanford.edu/wiki/resources/sparseae_exercise.zip sparseae_exercise.zip]
* checkNumericalGradient.m - Makes sure that computeNumericalGradient is implmented correctly
* computeNumericalGradient.m - Computes numerical gradient of a function (to be filled in)
* display_network.m - Visualizes images or filters for autoencoders as a grid
* initializeParameters.m - Initializes parameters for sparse autoencoder randomly
* sampleIMAGES.m - Samples 8x8 patches from an image matrix (to be filled in)
* sparseAutoencoderCost.m - Calculates cost and gradient of cost function of sparse autoencoder
* train.m - Framework for training and testing sparse autoencoder


'''[[Using the MNIST Dataset]]''' |  [http://ufldl.stanford.edu/wiki/resources/mnistHelper.zip mnistHelper.zip]
* loadMNISTImages.m - Returns a matrix containing raw MNIST images
* loadMNISTLabels.m - Returns a matrix containing MNIST labels


'''[[Exercise:PCA_and_Whitening|PCA and Whitening]]''' |  [http://ufldl.stanford.edu/wiki/resources/pca_exercise.zip pca_exercise.zip]
* display_network.m - Visualizes images or filters for autoencoders as a grid
* pca_gen.m - Framework for whitening exercise
* sampleIMAGESRAW.m - Returns 8x8 raw unwhitened patches

 
'''[[Exercise:Softmax_Regression|Softmax Regression]]''' |  [http://ufldl.stanford.edu/wiki/resources/softmax_exercise.zip softmax_exercise.zip]

* checkNumericalGradient.m - Makes sure that computeNumericalGradient is implmented correctly
* display_network.m - Visualizes images or filters for autoencoders as a grid
* loadMNISTImages.m - Returns a matrix containing raw MNIST images
* loadMNISTLabels.m - Returns a matrix containing MNIST labels
* softmaxCost.m - Computes cost and gradient of cost function of softmax
* softmaxTrain.m - Trains a softmax model with the given parameters
* train.m - Framework for this exercise