Difference between revisions of "Resources"

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*** [https://sites.ualberta.ca/~szepesva/RLBook.html Csaba Szepesvári. Algorithms for Reinforcement Learning]
 
*** [https://sites.ualberta.ca/~szepesva/RLBook.html Csaba Szepesvári. Algorithms for Reinforcement Learning]
 
*** [http://dl.acm.org/citation.cfm?id=2670001 Marco Wiering and Martijn van Otterlo. Reinforcement Learning: State-of-the-Art]
 
*** [http://dl.acm.org/citation.cfm?id=2670001 Marco Wiering and Martijn van Otterlo. Reinforcement Learning: State-of-the-Art]
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** [http://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html KDNuggets "10 Free Must-Read Books for Machine Learning and Data Science"]
 
* Online resources:
 
* Online resources:
 
** [https://www.coursera.org/learn/machine-learning Andrew Ng's free online Coursera Machine Learning course]
 
** [https://www.coursera.org/learn/machine-learning Andrew Ng's free online Coursera Machine Learning course]
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** [http://cs231n.github.io/convolutional-networks/ Stanford's CS 231n Convolutional Networks course website]
 
** [http://cs231n.github.io/convolutional-networks/ Stanford's CS 231n Convolutional Networks course website]
 
** various Wikipedia pages concerning [https://en.wikipedia.org/wiki/Artificial_neural_network artificial neural networks]
 
** various Wikipedia pages concerning [https://en.wikipedia.org/wiki/Artificial_neural_network artificial neural networks]
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** [http://playground.tensorflow.org/ TensorFlow Playground]
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** [https://distill.pub/2017/momentum/ Why Momentum Really Works]
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** [http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html Xiu-Shen Wei's Must Know Tips/Tricks in Deep Neural Networks]
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** [https://medium.com/towards-data-science/secret-sauce-behind-the-beauty-of-deep-learning-beginners-guide-to-activation-functions-a8e23a57d046 Medium.com guide: Understanding Activation Functions]
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** [https://github.com/williamFalcon/DeepRLHacks John Schulman's Deep RL Hacks (summarized by William Falcon)]
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** [https://blog.waya.ai/deep-residual-learning-9610bb62c355 Michael Dietz's Understand Deep Residual Networks]
 
*Online Courses
 
*Online Courses
−
** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course]
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** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course] and [https://www.coursera.org/specializations/deep-learning Coursera Deep Learning specialization]
 
** [https://www.coursera.org/learn/neural-networks Geoffrey Hinton's Neural Networks for Machine Learning course]
 
** [https://www.coursera.org/learn/neural-networks Geoffrey Hinton's Neural Networks for Machine Learning course]
 
** [http://www.dmi.usherb.ca/~larocheh/neural_networks Hugo Larochelle's graduate-level online Neural Network course]
 
** [http://www.dmi.usherb.ca/~larocheh/neural_networks Hugo Larochelle's graduate-level online Neural Network course]
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** [http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning Tim Dettmers' GPU comparison study]
 
** [http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning Tim Dettmers' GPU comparison study]
 
** [https://d4datascience.wordpress.com/2017/05/06/setting-up-a-gpu-based-deep-learning-machine/ Ved's d4datascience blog entry on setting up a GPU-based deep learning machine]
 
** [https://d4datascience.wordpress.com/2017/05/06/setting-up-a-gpu-based-deep-learning-machine/ Ved's d4datascience blog entry on setting up a GPU-based deep learning machine]
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=Hidden Markov Models=
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* Video Introductions
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** [https://youtu.be/mNSQ-prhgsw Daphne Koller's 12-minute video "Template Models: Hidden Markov Models - Stanford University"]
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** [https://youtu.be/jY2E6ExLxaw Nando de Freitas' 52-minute UBC lecture "undergraduate machine learning 9: Hidden Markov models - HMM"]
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* Texts and Articles
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** Russell and Norvig, Artificial Intelligence: a modern approach, 3rd ed, sections 15.3, 20.3.3.
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** [https://web.stanford.edu/~jurafsky/slp3/9.pdf Speech and Language Processing, 3rd ed, Chapter 9 by Daniel Jurafsky and James H. Martin]
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** [http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf Lawrence Rabiner tutorial "A tutorial on hidden Markov models and selected applications in speech recognition"]
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** Christopher Bishop's "Pattern Recognition and Machine Learning", Section 13.2
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** [https://stats.stackexchange.com/questions/3294/resources-for-learning-markov-chain-and-hidden-markov-models StackExchange thread "Resources for learning Markov chain and hidden Markov models"]
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* Other Resources
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** [https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 Udacity's `"Intro to Artificial Intelligence" course by Peter Norvig and Sebastian Thrun]
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** [https://www.udacity.com/course/probabalistic-models--cx27 Udacity's "Artificial Intelligence - Probabalistic Models"]
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** [https://www.coursera.org/learn/bioinformatics-pku/lecture/7pbUo/hidden-markov-model Coursera's Ge Gao lecture on HMMs]
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** [http://cs.jhu.edu/~jason/papers/eisner.hmm.xls Jason Eisner's spreadsheet for teaching the forward-backward algorithm] and [https://cs.jhu.edu/~jason/papers/eisner.tnlp02.pdf his paper on using it for teaching]
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** [https://www.quora.com/What-are-some-good-resources-for-learning-about-Hidden-Markov-Models Quora question "What are some good resources for learning about Hidden Markov Models?"]
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* [http://modelai.gettysburg.edu Model AI Assignments]
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** [http://modelai.gettysburg.edu/2017/hmm Sravana Reddy's "Implementing a Hidden Markov Model Toolkit"]
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** [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 13:31, 15 September 2018

Starting Places

General

Machine Learning

Neural Network Learning

Hidden Markov Models

Click-Through Rate (CTR) Prediction

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