Stanford UFLDL Tutorial MATLAB Modules_stanford

Matlab Modules matlab Modules

Sparse Autoencoder |sparseae_exercise.zip Checknumericalgradient.m-makes sure that Computenumericalgradient is implmented correctly computenumericalgradient.m-computes numerical gradient of a function (to is filled in) DISPLAY_NETW ork.m-visualizes images or filters for autoencoders as a grid initializeparameters.m-initializes parameters for sparse Autoencoder randomly sampleimages.m-samples 8x8 patches from the image matrix (to is filled in) sparseautoencodercost.m -Calculates cost and gradient of the cost function of sparse autoencoder train.m-framework for training and testing sparse Autoencoder

The Using the mnist Dataset |mnisthelper.zip loadmnistimages.m-returns a matrix containing raw mnist images. M-returns a matrix containing mnist labels

PCA and whitening |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

Softmax regression |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-retur NS a matrix containing raw mnist images loadmnistlabels.m-returns a matrix containing mnist, labels Softmaxcost.m-compu TES cost and gradient's cost function of Softmax softmaxtrain.m-trains a Softmax model with the given parameters. M-framework for this exercise

From:http://ufldl.stanford.edu/wiki/index.php/matlab_modules