Add slides to readme + group documentation together
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README.md
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@ -5,25 +5,27 @@ It implements machine learning algorithm under gradient boosting framework, incl
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Contributors: https://github.com/dmlc/xgboost/graphs/contributors
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Contributors: https://github.com/dmlc/xgboost/graphs/contributors
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Turorial and Documentation: https://github.com/dmlc/xgboost/wiki
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Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
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Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
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Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
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Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
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Examples Code: [Learning to use xgboost by examples](demo)
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Examples Code: [Learning to use xgboost by examples](demo)
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Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
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Distributed Version: [Distributed XGBoost](multi-node)
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Distributed Version: [Distributed XGBoost](multi-node)
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Notes on the Code: [Code Guide](src)
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Notes on the Code: [Code Guide](src)
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Turorial and Documentation: https://github.com/dmlc/xgboost/wiki
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Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
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Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
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Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
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* This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view.
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* This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view.
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* It present boosted tree learning as formal functional space optimization of defined objective.
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* It present boosted tree learning as formal functional space optimization of defined objective.
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* The model presented is used by xgboost for boosted trees
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* The model presented is used by xgboost for boosted trees
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Presention of a real use case of XGBoost to prepare tax audit in France: [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
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What's New
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What's New
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==========
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==========
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* [Distributed XGBoost now runs on YARN](multi-node/hadoop)!
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* [Distributed XGBoost now runs on YARN](multi-node/hadoop)!
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