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Merge pull request #471 from sab/patch-1

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CatBoost library information update
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@ -172,6 +172,7 @@ Further resources:
* [BanditLib](https://github.com/jkomiyama/banditlib) - A simple Multi-armed Bandit library.
* [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
* [CatBoost](https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
* [CNTK](https://github.com/Microsoft/CNTK) - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
* [CUDA](https://code.google.com/p/cuda-convnet/) - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
* [CXXNET](https://github.com/antinucleon/cxxnet) - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
@ -873,7 +874,7 @@ be
<a name="python-general-purpose"></a>
#### General-Purpose Machine Learning
* [CNTK](https://github.com/Microsoft/CNTK) - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found [here](https://docs.microsoft.com/cognitive-toolkit/).
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, LightGBM, and soon, deep learning.
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
* [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
* [XGBoost](https://github.com/dmlc/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.
@ -947,7 +948,7 @@ be
* [ML-From-Scratch](https://github.com/eriklindernoren/ML-From-Scratch) - Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
* [Edward](http://edwardlib.org/) - A library for probabilistic modeling, inference, and criticism. Built on top of TensorFlow.
* [xRBM](https://github.com/omimo/xRBM) - A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.
* [CatBoost](https://github.com/catboost/catboost) - Gradient boosting on decision trees library with categorical features support out of the box for Python, R.
* [CatBoost](https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, well documented and supports CPU and GPU (even multi-GPU) computation.
* [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Implementation of machine learning stacking technic as handy library in Python.
* [modAL](https://cosmic-cortex.github.io/modAL) - A modular active learning framework for Python, built on top of scikit-learn.
* [Cogitare](https://github.com/cogitare-ai/cogitare): A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
@ -1151,7 +1152,7 @@ be
* [C50](http://cran.r-project.org/web/packages/C50/index.html) - C50: C5.0 Decision Trees and Rule-Based Models.
* [caret](http://caret.r-forge.r-project.org/) - Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
* [caretEnsemble](http://cran.r-project.org/web/packages/caretEnsemble/index.html) - caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such models.
* [CatBoost](https://github.com/catboost/catboost) - Gradient boosting on decision trees library with categorical features support out of the box for Python, R.
* [CatBoost](https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box for R.
* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)
* [CORElearn](http://cran.r-project.org/web/packages/CORElearn/index.html) - CORElearn: Classification, regression, feature evaluation and ordinal evaluation.
* [CoxBoost](http://cran.r-project.org/web/packages/CoxBoost/index.html) - CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks