# MATLAB Modules

### From Ufldl

## 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

Retrieved from "http://deeplearning.stanford.edu/wiki/index.php/MATLAB_Modules"

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