446 lines
23 KiB
HTML
446 lines
23 KiB
HTML
<h1 id="awesome-h2o-awesome-powered-by-h2o.ai">Awesome H2O <a
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href="https://github.com/sindresorhus/awesome"><img
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src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg"
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alt="Awesome" /></a> <a href="https://github.com/h2oai/"><img
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src="https://img.shields.io/badge/powered%20by-h2oai-yellow.svg"
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alt="Powered by H2O.ai" /></a></h1>
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<p><a
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href="https://github.com/h2oai/h2o-3"><img src="https://rawgit.com/h2oai/awesome-h2o/master/h2o_logo.png" align="right" width="100"></a></p>
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<p>Below is a curated list of all the awesome projects, applications,
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research, tutorials, courses and books that use <a
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href="https://github.com/h2oai/h2o-3">H2O</a>, an open source,
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distributed machine learning platform. H2O offers parallelized
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implementations of many supervised and unsupervised machine learning
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algorithms such as Generalized Linear Models, Gradient Boosting Machines
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(including XGBoost), Random Forests, Deep Neural Networks (Deep
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Learning), Stacked Ensembles, Naive Bayes, Cox Proportional Hazards,
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K-means, PCA, Word2Vec, as well as a fully automatic machine learning
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algorithm (AutoML).</p>
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<p><a href="http://www.h2o.ai/about/">H2O.ai</a> produces many <a
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href="https://github.com/h2oai/h2o-tutorials">tutorials</a>, <a
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href="http://blog.h2o.ai/">blog posts</a>, <a
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href="https://github.com/h2oai/h2o-meetups">presentations</a> and <a
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href="https://www.youtube.com/user/0xdata">videos</a> about H2O, but the
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list below is comprised of awesome content produced by the greater H2O
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user community.</p>
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<p>We are just getting started with this list, so pull requests are very
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much appreciated! 🙏 Please review the <a
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href="contributing.md">contribution guidelines</a> before making a pull
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request. If you’re not a GitHub user and want to make a contribution,
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please send an email to community@h2o.ai.</p>
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<p>If you think H2O is awesome too, please ⭐ the <a
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href="https://github.com/h2oai/h2o-3/">H2O GitHub repository</a>.</p>
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<h2 id="contents">Contents</h2>
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<ul>
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<li><a href="#blog-posts--tutorials">Blog Posts & Tutorials</a></li>
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<li><a href="#books">Books</a></li>
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<li><a href="#research-papers">Research Papers</a></li>
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<li><a href="#benchmarks">Benchmarks</a></li>
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<li><a href="#presentations">Presentations</a></li>
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<li><a href="#courses">Courses</a></li>
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<li><a href="#software">Software (built using H2O)</a></li>
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<li><a href="#license">License</a></li>
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</ul>
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<h2 id="blog-posts-tutorials">Blog Posts & Tutorials</h2>
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<ul>
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<li><a
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href="https://enjoymachinelearning.com/posts/h2o-auto-machine-learning/">Using
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H2O AutoML to simplify training process (and also predict wine
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quality)</a> Aug 4, 2020</li>
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<li><a href="https://uc-r.github.io/lime">Visualizing ML Models with
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LIME</a></li>
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<li><a
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href="https://www.pavel.cool/h2o-3/h2o-parallel-grid-search/">Parallel
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Grid Search in H2O</a> Jan 17, 2020</li>
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<li><a href="https://www.pavel.cool/h2o-3/h2o-mojo-import/">Importing,
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Inspecting and Scoring with MOJO models inside H2O</a> Dec 10, 2019</li>
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<li><a
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href="https://towardsdatascience.com/artificial-intelligence-made-easy-187ecb90c299">Artificial
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Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling
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with H2O.ai and AutoML in Python</a> June 12, 2019</li>
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<li><a
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href="https://dzone.com/articles/anomaly-detection-with-isolation-forests-using-h2o-1">Anomaly
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Detection With Isolation Forests Using H2O</a> Dec 03, 2018</li>
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<li><a
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href="https://www.michal-kapusta.com/post/2018-11-02-predicting-residential-property-prices-in-bratislava-using-recipes-h2o-machine-learning-part-ii/">Predicting
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residential property prices in Bratislava using recipes - H2O Machine
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learning</a> Nov 25, 2018</li>
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<li><a
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href="https://dzone.com/articles/inspecting-decision-trees-in-h2o">Inspecting
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Decision Trees in H2O</a> Nov 07, 2018</li>
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<li><a
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href="https://medium.com/analytics-vidhya/gentle-introduction-to-automl-from-h2o-ai-a42b393b4ba2">Gentle
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Introduction to AutoML from H2O.ai</a> Sep 13, 2018</li>
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<li><a
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href="https://dzone.com/articles/machine-learning-with-h2o-hands-on-guide-for-data">Machine
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Learning With H2O — Hands-On Guide for Data Scientists</a> Jun 27,
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2018</li>
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<li><a
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href="http://www.business-science.io/business/2018/06/25/lime-local-feature-interpretation.html">Using
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machine learning with LIME to understand employee churn</a> June 25,
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2018</li>
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<li><a href="https://redoakstrategic.com/h2oaws/">Analytics at Scale:
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h2o, Apache Spark and R on AWS EMR</a> June 21, 2018</li>
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<li><a
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href="https://kkulma.github.io/2017-11-07-automated_machine_learning_in_cancer_detection/">Automated
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and unmysterious machine learning in cancer detection</a> Nov 7,
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2017</li>
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<li><a
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href="http://www.business-science.io/code-tools/2017/10/28/demo_week_h2o.html">Time
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series machine learning with h2o+timetk</a> Oct 28, 2017</li>
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<li><a
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href="http://www.business-science.io/business/2017/10/16/sales_backorder_prediction.html">Sales
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Analytics: How to use machine learning to predict and optimize product
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backorders</a> Oct 16, 2017</li>
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<li><a
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href="http://www.business-science.io/business/2017/09/18/hr_employee_attrition.html">HR
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Analytics: Using machine learning to predict employee turnover</a> Sep
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18, 2017</li>
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<li><a
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href="https://shiring.github.io/machine_learning/2017/05/01/fraud">Autoencoders
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and anomaly detection with machine learning in fraud analytics</a> May
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1, 2017</li>
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<li><a
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href="https://shiring.github.io/machine_learning/2017/02/27/h2o">Building
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deep neural nets with h2o and rsparkling that predict arrhythmia of the
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heart</a> Feb 27, 2017</li>
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<li><a
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href="https://shiring.github.io/machine_learning/2017/02/19/food_spark">Predicting
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food preferences with sparklyr (machine learning)</a> Feb 19, 2017</li>
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<li><a href="https://ellisp.github.io/blog/2017/02/18/svmlite">Moving
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largish data from R to H2O - spam detection with Enron emails</a> Feb
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18, 2016</li>
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<li><a
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href="http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-in-r">Deep
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learning & parameter tuning with mxnet, h2o package in R</a> Jan 30,
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2017</li>
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</ul>
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<h2 id="books">Books</h2>
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<ul>
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<li><a
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href="https://www.elsevier.com/books/big-data-in-psychiatry-and-neurology/moustafa/978-0-12-822884-5">Big
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data in psychiatry and neurology, Chapter 11: A scalable medication
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intake monitoring system</a> Diane Myung-Kyung Woodbridge and Kevin
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Bengtson Wong. (2021)</li>
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<li><a
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href="https://www2.packtpub.com/big-data-and-business-intelligence/hands-time-series-analysis-r">Hands
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on Time Series with R</a> Rami Krispin. (2019)</li>
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<li><a
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href="https://www.packtpub.com/product/mastering-machine-learning-with-spark-2-x/9781785283451">Mastering
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Machine Learning with Spark 2.x</a> Alex Tellez, Max Pumperla, Michal
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Malohlava. (2017)</li>
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<li><a
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href="https://www.amazon.com/Machine-Learning-Using-Karthik-Ramasubramanian/dp/1484223330">Machine
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Learning Using R</a> Karthik Ramasubramanian, Abhishek Singh.
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(2016)</li>
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<li><a
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href="https://www.amazon.com/Practical-Machine-Learning-H2O-Techniques/dp/149196460X">Practical
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Machine Learning with H2O: Powerful, Scalable Techniques for Deep
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Learning and AI</a> Darren Cook. (2016)</li>
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<li><a
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href="http://link.springer.com/book/10.1007/978-1-4842-1311-7">Disruptive
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Analytics</a> Thomas Dinsmore. (2016)</li>
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<li><a href="https://web.stanford.edu/~hastie/CASI/">Computer Age
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Statistical Inference: Algorithms, Evidence, and Data Science</a>
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Bradley Efron, Trevor Hastie. (2016)</li>
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<li><a
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href="https://www.packtpub.com/big-data-and-business-intelligence/r-deep-learning-essentials">R
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Deep Learning Essentials</a> Joshua F. Wiley. (2016)</li>
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<li><a href="https://www.manning.com/books/spark-in-action">Spark in
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Action</a> Petar Zečević, Marko Bonaći. (2016)</li>
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<li><a
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href="https://www.crcpress.com/Handbook-of-Big-Data/Buhlmann-Drineas-Kane-van-der-Laan/p/book/9781482249071">Handbook
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of Big Data</a> Peter Bühlmann, Petros Drineas, Michael Kane, Mark J.
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van der Laan (2015)</li>
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</ul>
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<h2 id="research-papers">Research Papers</h2>
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<ul>
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<li><a
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href="https://www.sciencedirect.com/science/article/pii/S2667305323000133">Automated
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machine learning: AI-driven decision making in business analytics</a>
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Marc Schmitt. (2023)</li>
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<li><a href="https://www.mdpi.com/2073-4441/15/3/475">Water-Quality
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Prediction Based on H2O AutoML and Explainable AI Techniques</a> Hamza
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Ahmad Madni, Muhammad Umer, Abid Ishaq, Nihal Abuzinadah, Oumaima
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Saidani, Shtwai Alsubai, Monia Hamdi, Imran Ashraf. (2023)</li>
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<li><a
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href="https://www.sciencedirect.com/science/article/abs/pii/S1352231022002291?dgcid=coauthor">Which
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model to choose? Performance comparison of statistical and machine
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learning models in predicting PM2.5 from high-resolution satellite
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aerosol optical depth</a> Padmavati Kulkarnia, V.Sreekantha, Adithi
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R.Upadhyab, Hrishikesh ChandraGautama. (2022)</li>
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<li><a href="https://pubmed.ncbi.nlm.nih.gov/35467566/">Prospective
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validation of a transcriptomic severity classifier among patients with
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suspected acute infection and sepsis in the emergency department</a> Noa
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Galtung, Eva Diehl-Wiesenecker, Dana Lehmann, Natallia Markmann, Wilma H
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Bergström, James Wacker, Oliver Liesenfeld, Michael Mayhew, Ljubomir
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Buturovic, Roland Luethy, Timothy E Sweeney , Rudolf Tauber, Kai
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Kappert, Rajan Somasundaram, Wolfgang Bauer. (2022)</li>
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<li><a href="https://embc.embs.org/2021/">Depression Level Prediction in
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People with Parkinson’s Disease during the COVID-19 Pandemic</a>)
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Hashneet Kaur, Patrick Ka-Cheong Poon, Sophie Yuefei Wang, Diane
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Myung-kyung Woodbridge. (2021)</li>
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<li><a href="https://embc.embs.org/2021/">Machine Learning-based Meal
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Detection Using Continuous Glucose Monitoring on Healthy Participants:
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An Objective Measure of Participant Compliance to Protocol</a> Victor
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Palacios, Diane Myung-kyung Woodbridge, Jean L. Fry. (2021)</li>
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<li><a
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href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25565">Maturity
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of gray matter structures and white matter connectomes, and their
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relationship with psychiatric symptoms in youth</a> Alex Luna, Joel
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Bernanke, Kakyeong Kim, Natalie Aw, Jordan D. Dworkin, Jiook Cha,
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Jonathan Posner (2021).</li>
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<li><a href="https://pubmed.ncbi.nlm.nih.gov/34219197/">Appendectomy
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during the COVID-19 pandemic in Italy: a multicenter ambispective cohort
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study by the Italian Society of Endoscopic Surgery and new technologies
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(the CRAC study)</a> Alberto Sartori, Mauro Podda, Emanuele Botteri,
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Roberto Passera, Ferdinando Agresta, Alberto Arezzo. (2021)</li>
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<li><a
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href="https://carleton.ca/economics/wp-content/uploads/cewp21-05.pdf">Forecasting
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Canadian GDP Growth with Machine Learning</a> Shafiullah Qureshi, Ba
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Chu, Fanny S. Demers. (2021)</li>
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<li><a
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href="https://onlinelibrary.wiley.com/doi/10.1111/geb.13321">Morphological
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traits of reef corals predict extinction risk but not conservation
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status</a> Nussaïbah B. Raja, Andreas Lauchstedt, John M. Pandolfi, Sun
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W. Kim, Ann F. Budd, Wolfgang Kiessling. (2021)</li>
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<li><a
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href="https://openaccess.nhh.no/nhh-xmlui/bitstream/handle/11250/2739783/masterthesis.pdf?sequence=1">Machine
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Learning as a Tool for Improved Housing Price Prediction</a> Henrik I W.
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Wolstad and Didrik Dewan. (2020)</li>
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<li><a
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href="https://pubs.acs.org/doi/10.1021/acs.estlett.0c00206">Citizen
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Science Data Show Temperature-Driven Declines in Riverine Sentinel
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Invertebrates</a> Timothy J. Maguire, Scott O. C. Mundle. (2020)</li>
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<li><a
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href="https://www.diva-portal.org/smash/get/diva2:1467609/FULLTEXT01.pdf">Predicting
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Risk of Delays in Postal Deliveries with Neural Networks and Gradient
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Boosting Machines</a> Matilda Söderholm. (2020)</li>
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<li><a
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href="https://github.com/malhartakle/MastersDissertation/blob/master/Research%20Project%20Report.pdf">Stock
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Market Analysis using Stacked Ensemble Learning Method</a> Malkar Takle.
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(2020)</li>
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<li><a
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href="https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf">H2O
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AutoML: Scalable Automatic Machine Learning</a>. Erin LeDell, Sebastien
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Poirier. (2020)</li>
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<li><a
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href="https://www.nature.com/articles/s41598-020-69358-4">Single-cell
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mass cytometry on peripheral blood identifies immune cell subsets
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associated with primary biliary cholangitis</a> Jin Sung Jang, Brian D.
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Juran, Kevin Y. Cunningham, Vinod K. Gupta, Young Min Son, Ju Dong Yang,
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Ahmad H. Ali, Elizabeth Ann L. Enninga, Jaeyun Sung & Konstantinos
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N. Lazaridis. (2020)</li>
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<li><a
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href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190647/">Prediction
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of the functional impact of missense variants in BRCA1 and BRCA2 with
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BRCA-ML</a> Steven N. Hart, Eric C. Polley, Hermella Shimelis,
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Siddhartha Yadav, Fergus J. Couch. (2020)</li>
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<li><a href="https://doi.org/10.1186/s40663-020-00226-3">Innovative deep
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learning artificial intelligence applications for predicting
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relationships between individual tree height and diameter at breast
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height</a> İlker Ercanlı. (2020)</li>
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<li><a
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href="https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf">An
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Open Source AutoML Benchmark</a> Peter Gijsbers, Erin LeDell, Sebastien
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Poirier, Janek Thomas, Berndt Bischl, Joaquin Vanschoren. (2019)</li>
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<li><a href="https://arxiv.org/abs/2002.04803">Machine Learning in
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Python: Main developments and technology trends in data science, machine
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learning, and artificial intelligence</a> Sebastian Raschka, Joshua
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Patterson, Corey Nolet. (2019)</li>
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<li><a
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href="https://www.sciencedirect.com/science/article/pii/S1389041718308970">Human
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actions recognition in video scenes from multiple camera viewpoints</a>
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Fernando Itano, Ricardo Pires, Miguel Angelo de Abreu de Sousa, Emilio
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Del-Moral-Hernandeza. (2019)</li>
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<li><a
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href="https://ieeexplore.ieee.org/document/8489520/authors#authors">Extending
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MLP ANN hyper-parameters Optimization by using Genetic Algorithm</a>
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Fernando Itano, Miguel Angelo de Abreu de Sousa, Emilio
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Del-Moral-Hernandez. (2018)</li>
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<li><a href="https://doi.org/10.1016/j.eururo.2018.09.050">askMUSIC:
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Leveraging a Clinical Registry to Develop a New Machine Learning Model
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to Inform Patients of Prostate Cancer Treatments Chosen by Similar
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Men</a> Gregory B. Auffenberg, Khurshid R. Ghani, Shreyas Ramani, Etiowo
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Usoro, Brian Denton, Craig Rogers, Benjamin Stockton, David C. Miller,
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Karandeep Singh. (2018)</li>
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<li><a
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href="http://www.variancejournal.org/articlespress/articles/Machine-Spedicato.pdf">Machine
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Learning Methods to Perform Pricing Optimization. A Comparison with
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Standard GLMs</a> Giorgio Alfredo Spedicato, Christophe Dutang, and
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Leonardo Petrini. (2018)</li>
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<li><a href="https://arxiv.org/abs/1707.04940">Comparative Performance
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Analysis of Neural Networks Architectures on H2O Platform for Various
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Activation Functions</a> Yuriy Kochura, Sergii Stirenko, Yuri Gordienko.
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(2017)</li>
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<li><a
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href="https://link.springer.com/chapter/10.1007/978-3-319-66963-2_14">Algorithmic
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trading using deep neural networks on high frequency data</a> Andrés
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Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León,
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Arbey Aragón. (2017)</li>
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<li><a href="https://dl.acm.org/citation.cfm?id=3124407">Generic online
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animal activity recognition on collar tags</a> Jacob W. Kamminga, Helena
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C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)</li>
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<li><a
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href="https://link.springer.com/content/pdf/10.1007%2Fs10705-017-9870-x.pdf">Soil
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nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content
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at 250 m spatial resolution using machine learning</a> Tomislav Hengl,
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Johan G. B. Leenaars, Keith D. Shepherd, Markus G. Walsh, Gerard B. M.
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Heuvelink, Tekalign Mamo, Helina Tilahun, Ezra Berkhout, Matthew Cooper,
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Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena. (2017)</li>
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<li><a href="https://arxiv.org/pdf/1707.09021.pdf">Robust and flexible
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estimation of data-dependent stochastic mediation effects: a proposed
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method and example in a randomized trial setting</a> Kara E. Rudolph,
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Oleg Sofrygin, Wenjing Zheng, and Mark J. van der Laan. (2017)</li>
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<li><a href="https://arxiv.org/abs/1707.02641">Automated versus
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do-it-yourself methods for causal inference: Lessons learned from a data
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analysis competition</a> Vincent Dorie, Jennifer Hill, Uri Shalit, Marc
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Scott, Dan Cervone. (2017)</li>
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<li><a
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href="https://qspace.library.queensu.ca/bitstream/handle/1974/15929/Muthalaly_Reena%20S_201707_MSC.pdf">Using
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deep learning to predict the mortality of leukemia patients</a> Reena
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Shaw Muthalaly. (2017)</li>
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<li><a
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href="http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0175383&type=printable">Use
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of a machine learning framework to predict substance use disorder
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treatment success</a> Laura Acion, Diana Kelmansky, Mark van der Laan,
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Ethan Sahker, DeShauna Jones, Stephan Arnd. (2017)</li>
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<li><a
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href="https://www.kn.e-technik.tu-dortmund.de/.cni-bibliography/publications/cni-publications/Tiemann2017a.pdf">Ultra-wideband
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antenna-induced error prediction using deep learning on channel response
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data</a> Janis Tiemann, Johannes Pillmann, Christian Wietfeld.
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(2017)</li>
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<li><a
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href="http://www.tandfonline.com/doi/abs/10.1080/21680566.2017.1291377?journalCode=ttrb20">Inferring
|
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passenger types from commuter eigentravel matrices</a> Erika Fille T.
|
||
Legara, Christopher P. Monterola. (2017)</li>
|
||
<li><a
|
||
href="http://www.sciencedirect.com/science/article/pii/S0377221716308657">Deep
|
||
neural networks, gradient-boosted trees, random forests: Statistical
|
||
arbitrage on the S&P 500</a> Christopher Krauss, Xuan Anh Doa,
|
||
Nicolas Huckb. (2016)</li>
|
||
<li><a
|
||
href="http://ieeexplore.ieee.org/document/7838233/?reload=true">Identifying
|
||
IT purchases anomalies in the Brazilian government procurement system
|
||
using deep learning</a> Silvio L. Domingos, Rommel N. Carvalho, Ricardo
|
||
S. Carvalho, Guilherme N. Ramos. (2016)</li>
|
||
<li><a
|
||
href="http://ieeexplore.ieee.org/abstract/document/7838243/">Predicting
|
||
recovery of credit operations on a Brazilian bank</a> Rogério G. Lopes,
|
||
Rommel N. Carvalho, Marcelo Ladeira, Ricardo S. Carvalho. (2016)</li>
|
||
<li><a href="http://ieeexplore.ieee.org/abstract/document/7838276/">Deep
|
||
learning anomaly detection as support fraud investigation in Brazilian
|
||
exports and anti-money laundering</a> Ebberth L. Paula, Marcelo Ladeira,
|
||
Rommel N. Carvalho, Thiago Marzagão. (2016)</li>
|
||
<li><a href="https://doi.org/10.1101/070490">Deep learning and
|
||
association rule mining for predicting drug response in cancer</a>
|
||
Konstantinos N. Vougas, Thomas Jackson, Alexander Polyzos, Michael
|
||
Liontos, Elizabeth O. Johnson, Vassilis Georgoulias, Paul Townsend, Jiri
|
||
Bartek, Vassilis G. Gorgoulis. (2016)</li>
|
||
<li><a
|
||
href="http://www.rsm.nl/fileadmin/Images_NEW/ECFEB/The_value_of_points_of_interest_information.pdf">The
|
||
value of points of interest information in predicting cost-effective
|
||
charging infrastructure locations</a> Stéphanie Florence Visser.
|
||
(2016)</li>
|
||
<li><a
|
||
href="https://www.degruyter.com/downloadpdf/j/jwld.2016.30.issue-1/jwld-2016-0029/jwld-2016-0029.xml">Adaptive
|
||
modelling of spatial diversification of soil classification units.
|
||
Journal of Water and Land Development</a> Krzysztof Urbański, Stanisław
|
||
Gruszczyńsk. (2016)</li>
|
||
<li><a
|
||
href="http://www.stat.berkeley.edu/~ledell/papers/ledell-phd-thesis.pdf">Scalable
|
||
ensemble learning and computationally efficient variance estimation</a>
|
||
Erin LeDell. (2015)</li>
|
||
<li><a
|
||
href="https://doi.org/10.7287/peerj.preprints.1265v1">Superchords:
|
||
decoding EEG signals in the millisecond range</a> Rogerio Normand, Hugo
|
||
Alexandre Ferreira. (2015)</li>
|
||
<li><a href="https://github.com/glouppe/phd-thesis">Understanding random
|
||
forests: from theory to practice</a> Gilles Louppe. (2014)</li>
|
||
</ul>
|
||
<h2 id="benchmarks">Benchmarks</h2>
|
||
<ul>
|
||
<li><a
|
||
href="http://roamanalytics.com/2016/10/28/are-categorical-variables-getting-lost-in-your-random-forests/">Are
|
||
categorical variables getting lost in your random forests?</a> -
|
||
Benchmark of categorical encoding schemes and the effect on tree based
|
||
models (Scikit-learn vs H2O). Oct 28, 2016</li>
|
||
<li><a
|
||
href="http://www.rblog.uni-freiburg.de/2017/02/07/deep-learning-in-r/">Deep
|
||
learning in R</a> - Benchmark of open source deep learning packages in
|
||
R. Mar 7, 2016</li>
|
||
<li><a href="https://github.com/szilard/benchm-ml">Szilard’s machine
|
||
learning benchmark</a> - Benchmarks of Random Forest, GBM, Deep Learning
|
||
and GLM implementations in common open source ML frameworks. Jul 3,
|
||
2015</li>
|
||
</ul>
|
||
<h2 id="presentations">Presentations</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://www.slideshare.net/rocalabern/digital-origin-pipelines-for-model-deployment">Pipelines
|
||
for model deployment</a> Apr 25, 2017</li>
|
||
<li><a
|
||
href="https://speakerdeck.com/szilard/machine-learning-with-h2o-dot-ai-la-h2o-meetup-at-at-and-t-jan-2017">Machine
|
||
learning with H2O.ai</a> Jan 23, 2017</li>
|
||
</ul>
|
||
<h2 id="courses">Courses</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/dianewoodbridge/2020-msds697-example">University
|
||
of San Francisco (USF) Distributed Data System Class (MSDS 697)</a> -
|
||
Master of Science in Data Science Program.</li>
|
||
<li><a
|
||
href="https://www.ub.uio.no/english/courses-events/events/all-libraries/2019/research-bazaar-2019.html">University
|
||
of Oslo: Introduction to Automatic and Scalable Machine Learning with
|
||
H2O and R</a> - Research Bazaar 2019</li>
|
||
<li><a
|
||
href="https://github.com/szilard/teach-data-science-UCLA-master-appl-stats">UCLA:
|
||
Tools in Data Science (STATS 418)</a> - Masters of Applied Statistics
|
||
Program.</li>
|
||
<li><a href="https://github.com/jphall663/GWU_data_mining">GWU: Data
|
||
Mining (Decision Sciences 6279)</a> - Masters of Science in Business
|
||
Analytics.</li>
|
||
<li><a
|
||
href="http://www.stats.uct.ac.za/stats/study/postgrad/honours">University
|
||
of Cape Town: Analytics Module</a> - Postgraduate Honors Program in
|
||
Statistical Sciences.</li>
|
||
<li><a
|
||
href="https://www.coursera.org/learn/competitive-data-science">Coursera:
|
||
How to Win a Data Science Competition: Learn from Top Kagglers</a> -
|
||
Advanced Machine Learning Specialization.</li>
|
||
</ul>
|
||
<h2 id="software">Software</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://business-science.github.io/modeltime.h2o/">modeltime.h2o R
|
||
package</a>: Forecasting with H2O AutoML</li>
|
||
<li><a href="https://github.com/ML4LHS/Evaporate">Evaporate</a>: Run H2O
|
||
models in the browser via Javascript. More info <a
|
||
href="https://twitter.com/kdpsinghlab/status/1367992786239242248">here</a>.</li>
|
||
<li><a href="https://github.com/ML4LHS/splash">splash R package</a>:
|
||
Splashing a User Interface onto H2O MOJO Files. More info <a
|
||
href="https://twitter.com/kdpsinghlab/status/1367809740705792008">here</a>.</li>
|
||
<li><a href="https://github.com/stevenpawley/h2oparsnip">h2oparsnip R
|
||
package</a>: Set of wrappers to bind h2o algorthms with the <a
|
||
href="https://parsnip.tidymodels.org/">parsnip</a> package.</li>
|
||
<li><a href="https://github.com/kcrandall/EMR_Spark_Automation">Spin up
|
||
PySpark and PySparkling on AWS</a></li>
|
||
<li><a href="https://github.com/RamiKrispin/USelectricity">Forecast the
|
||
US demand for electricity</a>: A real-time <a
|
||
href="https://ramikrispin.github.io/USelectricity/">dashboard</a> of the
|
||
US electricity demand (forecast using H2O GLM)</li>
|
||
<li><a href="https://github.com/navdeep-G/h2o3-pam">h2o3-pam</a>:
|
||
Partition Around Mediods (PAM) clustering algorithm in H2O-3</li>
|
||
<li><a
|
||
href="https://github.com/navdeep-G/h2o3-gapstat">h2o3-gapstat</a>: Gap
|
||
Statistic algorithm in H2O-3</li>
|
||
</ul>
|
||
<h2 id="license">License</h2>
|
||
<p><a href="https://creativecommons.org/publicdomain/zero/1.0/"><img
|
||
src="https://upload.wikimedia.org/wikipedia/commons/6/69/CC0_button.svg"
|
||
alt="CC0" /></a></p>
|
||
<p>To the extent possible under law, <a href="http://h2o.ai">H2O.ai</a>
|
||
has waived all copyright and related or neighboring rights to this
|
||
work.</p>
|
||
<p><a href="https://github.com/h2oai/awesome-h2o">h2o.md Github</a></p>
|