josephmisiti/awesome-machine-learning
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Merge pull request #97 from antinucleon/master

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Adding XGBoost and CXXNET
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joseph misiti 2014-11-25 15:46:44 -05:00
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@ -117,6 +117,8 @@ For a list of free machine learning books available for download, go [here](http
* [sofia-ml](https://code.google.com/p/sofia-ml/) - Suite of fast incremental algorithms.
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
* [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
* [CXXNET](https://github.com/antinucleon/cxxnet) - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
* [XGBoost](https://github.com/tqchen/xgboost) - A parallelized optimized general purpose gradient boosting library.
* [CUDA](https://code.google.com/p/cuda-convnet/) - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
* [Stan](http://mc-stan.org/) - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling
* [BanditLib](https://github.com/jkomiyama/banditlib) - A simple Multi-armed Bandit library.
@ -333,6 +335,7 @@ For a list of free machine learning books available for download, go [here](http
* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization
* [ANN](https://github.com/EricChiang/ANN.jl) - Julia artificial neural networks
* [Mocha.jl](https://github.com/pluskid/Mocha.jl) - Deep Learning framework for Julia inspired by Caffe
* [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl) - eXtreme Gradient Boosting Package in Julia
<a name="julia-nlp" />
#### Natural Language Processing
@ -535,6 +538,7 @@ on MNIST digits[DEEP LEARNING]
<a name="python-general-purpose" />
#### General-Purpose Machine Learning
* [XGBoost](https://github.com/tqchen/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python
* [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
@ -806,6 +810,7 @@ Angle Regression
* [SuperLearner](https://github.com/ecpolley/SuperLearner) and [subsemble](http://cran.r-project.org/web/packages/subsemble/index.html) - Multi-algorithm ensemble learning packages.
* [Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
* [fpc](http://cran.r-project.org/web/packages/fpc/index.html) - fpc: Flexible procedures for clustering
* [XGBoost.R](https://github.com/tqchen/xgboost/tree/master/R-package) - R binding for eXtreme Gradient Boosting (Tree) Library
<a name="r-data-analysis" />
#### Data Analysis / Data Visualization