Difference between revisions of "Resources"

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(Hidden Markov Models)
(CTR Prediction resources)
 
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** [http://modelai.gettysburg.edu/2017/hmm Sravana Reddy's "Implementing a Hidden Markov Model Toolkit"]
 
** [http://modelai.gettysburg.edu/2017/hmm Sravana Reddy's "Implementing a Hidden Markov Model Toolkit"]
 
** [http://modelai.gettysburg.edu/2010/pacman/projects/tracking/busters.html John DeNero and Dan Klein's "The Pac-Man Projects" Project #4: Ghostbusters]
 
** [http://modelai.gettysburg.edu/2010/pacman/projects/tracking/busters.html John DeNero and Dan Klein's "The Pac-Man Projects" Project #4: Ghostbusters]
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=Click-Through Rate (CTR) Prediction=
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* [https://www.kaggle.com/ Kaggle] CTR Prediction Competitions:
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** [https://www.kaggle.com/c/criteo-display-ad-challenge Criteo]
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** [https://www.kaggle.com/c/avazu-ctr-prediction Avazu]
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** [https://www.kaggle.com/c/avito-context-ad-clicks Avito]
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** [https://www.kaggle.com/c/outbrain-click-prediction Outbrain]
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* [https://xgboost.readthedocs.io/en/latest/ XGBoost] - Extreme Gradient Tree Boosting
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** [https://dl.acm.org/citation.cfm?doid=2939672.2939785 XGBoost: A Scalable Tree Boosting System] 
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* [https://github.com/guestwalk/libffm libffm] - library for Field-aware Factorization Machines (FFMs)
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** [https://dl.acm.org/citation.cfm?doid=2959100.2959134 Field-aware factorization machines for CTR prediction]
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** [https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf Slides on winning approach to Criteo CTR Prediction competition]

Latest revision as of 14:31, 15 September 2018

Starting Places

General

Machine Learning

Neural Network Learning

Hidden Markov Models

Click-Through Rate (CTR) Prediction