MATLAB Modules

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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
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