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== 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 implmeented 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 '''[[Exercise:Vectorization|Vectorization]]''' [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 implmeented 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
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