Description: A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes:
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear Support Vector Machine, Non-Linear Support Vector Machine (with RBF kernel),
Feed-forward Neural Network, Embedding Neural Network, Convolutional Neural Network, Sparse Autoencoders, Denoising Autoencoders,
Contractive Autoencoders, Stacked Sparse Autoencoders, Self-Taught Learner and Restricted Boltzmann Machines are tested with this version.
- Rest of the methods are not tested hence not supplied and the progress is as follows:
+ Deep Belief Nets with Restricted Boltzmann Machines (not tested)
+ Bayes Nets (tested- refactoring)
+ Hidden Markov Models (tested- refactoring)
+ Conditional Random Fields (work in progress)
To Search:
File list (Check if you may need any files):
libORF-master
.............\.directory
.............\.gitattributes
.............\.gitignore
.............\@Color
.............\......\Color.m
.............\@ContractiveAutoencoder
.............\.......................\ContractiveAutoencoder.m
.............\.......................\private
.............\.......................\.......\computeNumericalGradient.m
.............\.......................\.......\contractiveAutoencoderCostBGD.m
.............\.......................\.......\contractiveAutoencoderCostSGD.m
.............\.......................\.......\dNonLinearity.m
.............\.......................\.......\nonLinearity.m
.............\@ConvUtils
.............\..........\.svn
.............\..........\....\all-wcprops
.............\..........\....\entries
.............\..........\....\text-base
.............\..........\....\.........\ConvUtils.m.svn-base
.............\..........\ConvUtils.m
.............\..........\private
.............\..........\.......\maxoutFprop.c
.............\..........\.......\maxoutFprop.mexa64
.............\@DenoisingAutoencoder
.............\.....................\DenoisingAutoencoder.m
.............\.....................\private
.............\.....................\.......\computeNumericalGradient.m
.............\.....................\.......\dNonLinearity.m
.............\.....................\.......\denoisingAutoencoderCostBGD.m
.............\.....................\.......\denoisingAutoencoderCostSGD.m
.............\.....................\.......\nonLinearity.m
.............\@Edge
.............\.....\Edge.m
.............\@EmbeddingNeuralNet
.............\...................\EmbeddingNeuralNet.m
.............\...................\private
.............\...................\.......\dNonLinearity.m
.............\...................\.......\embeddingNeuralNetCost.m
.............\...................\.......\feedForwardENN.m
.............\...................\.......\nonLinearity.m
.............\...................\.......\params2stack.m
.............\...................\.......\stack2params.m
.............\@Img
.............\....\Img.m
.............\....\private
.............\....\.......\display_img.m
.............\@LinearRegressor
.............\................\LinearRegressor.m
.............\................\private
.............\................\.......\costFunctionLinRegL2.m
.............\@LinearSVM
.............\..........\.svn
.............\..........\....\all-wcprops
.............\..........\....\entries
.............\..........\....\text-base
.............\..........\....\.........\LinearSVM.m.svn-base
.............\..........\LinearSVM.m
.............\..........\private
.............\..........\.......\.svn
.............\..........\.......\....\all-wcprops
.............\..........\.......\....\entries
.............\..........\.......\....\text-base
.............\..........\.......\....\.........\linearSVMcostL2.m.svn-base
.............\..........\.......\computeNumericalGradient.m
.............\..........\.......\linearSVMcostL2.m
.............\@LogisticRegressor
.............\..................\LogisticRegressor.m
.............\..................\private
.............\..................\.......\costFunction.m
.............\..................\.......\costFunctionLogRegL2.m
.............\@NaiveBayesGM
.............\.............\.svn
.............\.............\....\all-wcprops
.............\.............\....\entries
.............\.............\....\text-base
.............\.............\....\.........\NaiveBayesGM.m.svn-base
.............\.............\NaiveBayesGM.m
.............\@NeuralNet
.............\..........\.svn
.............\..........\....\all-wcprops
.............\..........\....\entries
.............\..........\....\text-base
.............\..........\....\.........\NeuralNet.m.svn-base
.............\..........\NeuralNet.m
.............\..........\private
.............\..........\.......\.svn
.............\..........\.......\....\all-wcprops
.............\..........\.......\....\entries
.............\..........\.......\....\text-base
.............\..........\.......\
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