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@ -360,6 +360,8 @@ For a list of free machine learning books available for download, go [here](http
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<a name="julia-general-purpose" />
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#### General-Purpose Machine Learning
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* [MachineLearning](https://github.com/benhamner/MachineLearning.jl) - Julia Machine Learning library
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* [MLBase](https://github.com/JuliaStats/MLBase.jl) - A set of functions to support the development of machine learning algorithms
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* [PGM](https://github.com/JuliaStats/PGM.jl) - A Julia framework for probabilistic graphical models.
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* [DA](https://github.com/trthatcher/DA.jl) - Julia package for Regularized Discriminant Analysis
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* [Regression](https://github.com/lindahua/Regression.jl) - Algorithms for regression analysis (e.g. linear regression and logistic regression)
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@ -371,6 +373,7 @@ For a list of free machine learning books available for download, go [here](http
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* [Decision Tree](https://github.com/bensadeghi/DecisionTree.jl) - Decision Tree Classifier and Regressor
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* [Neural](https://github.com/compressed/neural.jl) - A neural network in Julia
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* [MCMC](https://github.com/doobwa/MCMC.jl) - MCMC tools for Julia
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* [Mamba](https://github.com/brian-j-smith/Mamba.jl) - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia
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* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized linear models in Julia
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* [Online Learning](https://github.com/lendle/OnlineLearning.jl)
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* [GLMNet](https://github.com/simonster/GLMNet.jl) - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
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@ -380,8 +383,9 @@ For a list of free machine learning books available for download, go [here](http
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* [Dimensionality Reduction](https://github.com/JuliaStats/DimensionalityReduction.jl) - Methods for dimensionality reduction
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* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization
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* [ANN](https://github.com/EricChiang/ANN.jl) - Julia artificial neural networks
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* [Mocha.jl](https://github.com/pluskid/Mocha.jl) - Deep Learning framework for Julia inspired by Caffe
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* [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl) - eXtreme Gradient Boosting Package in Julia
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* [Mocha](https://github.com/pluskid/Mocha.jl) - Deep Learning framework for Julia inspired by Caffe
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* [XGBoost](https://github.com/antinucleon/XGBoost.jl) - eXtreme Gradient Boosting Package in Julia
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* [ManifoldLearning](https://github.com/wildart/ManifoldLearning.jl) - A Julia package for manifold learning and nonlinear dimensionality reduction
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<a name="julia-nlp" />
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#### Natural Language Processing
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