Stanford UFLDL教程 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 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 |mnistHelper.zip loadMNISTImages.m - Returns a matrix containing raw MNIST images loadMNISTLabels.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 - 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

from: http://ufldl.stanford.edu/wiki/index.php/MATLAB_Modules