awesome-awesomeness/terminal/h2o

Awesome H2O !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome) !Powered by H2O.ai (https://img.shields.io/badge/powered%20by-h2oai-yellow.svg) 
                                                                                                             (https://github.com/h2oai/)

 (https://github.com/h2oai/h2o-3)

Below is a curated list of all the awesome projects, applications, research, tutorials, courses and books that use H2O (https://github.com/h2oai/h2o-3), an open source, distributed machine learning platform.  H2O offers parallelized 
implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models, Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive 
Bayes, Cox Proportional Hazards, K-means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (AutoML).

H2O.ai (http://www.h2o.ai/about/) produces many tutorials (https://github.com/h2oai/h2o-tutorials), blog posts (http://blog.h2o.ai/), presentations (https://github.com/h2oai/h2o-meetups) and videos (https://www.youtube.com/user/0xdata) about H2O,
but the list below is comprised of awesome content produced by the greater H2O user community.

We are just getting started with this list, so pull requests are very much appreciated!  🙏  Please review the contribution guidelines (contributing.md) before making a pull request.  If you're not a GitHub user and want to make a contribution, 
please send an email to community@h2o.ai.

If you think H2O is awesome too, please ⭐ the H2O GitHub repository (https://github.com/h2oai/h2o-3/).

Contents

- Blog Posts & Tutorials (#blog-posts--tutorials)
- Books (#books)
- Research Papers (#research-papers)
- Benchmarks (#benchmarks)
- Presentations (#presentations)
- Courses (#courses)
- Software (built using H2O) (#software)
- License (#license)

Blog Posts & Tutorials

- Using H2O AutoML to simplify training process (and also predict wine quality) (https://enjoymachinelearning.com/posts/h2o-auto-machine-learning/) Aug 4, 2020
- Visualizing ML Models with LIME (https://uc-r.github.io/lime)
- Parallel Grid Search in H2O  (https://www.pavel.cool/h2o-3/h2o-parallel-grid-search/) Jan 17, 2020
- Importing, Inspecting and Scoring with MOJO models inside H2O (https://www.pavel.cool/h2o-3/h2o-mojo-import/) Dec 10, 2019
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python (https://towardsdatascience.com/artificial-intelligence-made-easy-187ecb90c299) June 12, 2019
- Anomaly Detection With Isolation Forests Using H2O (https://dzone.com/articles/anomaly-detection-with-isolation-forests-using-h2o-1) Dec 03, 2018
- Predicting residential property prices in Bratislava using recipes - H2O Machine learning (https://www.michal-kapusta.com/post/2018-11-02-predicting-residential-property-prices-in-bratislava-using-recipes-h2o-machine-learning-part-ii/) Nov 25, 
2018
- Inspecting Decision Trees in H2O (https://dzone.com/articles/inspecting-decision-trees-in-h2o) Nov 07, 2018
- Gentle Introduction to AutoML from H2O.ai (https://medium.com/analytics-vidhya/gentle-introduction-to-automl-from-h2o-ai-a42b393b4ba2) Sep 13, 2018
- Machine Learning With H2O — Hands-On Guide for Data Scientists (https://dzone.com/articles/machine-learning-with-h2o-hands-on-guide-for-data) Jun 27, 2018
- Using machine learning with LIME to understand employee churn (http://www.business-science.io/business/2018/06/25/lime-local-feature-interpretation.html) June 25, 2018
- Analytics at Scale: h2o, Apache Spark and R on AWS EMR (https://redoakstrategic.com/h2oaws/) June 21, 2018
- Automated and unmysterious machine learning in cancer detection (https://kkulma.github.io/2017-11-07-automated_machine_learning_in_cancer_detection/) Nov 7, 2017
- Time series machine learning with h2o+timetk (http://www.business-science.io/code-tools/2017/10/28/demo_week_h2o.html) Oct 28, 2017
- Sales Analytics: How to use machine learning to predict and optimize product backorders (http://www.business-science.io/business/2017/10/16/sales_backorder_prediction.html) Oct 16, 2017
- HR Analytics: Using machine learning to predict employee turnover (http://www.business-science.io/business/2017/09/18/hr_employee_attrition.html) Sep 18, 2017
- Autoencoders and anomaly detection with machine learning in fraud analytics  (https://shiring.github.io/machine_learning/2017/05/01/fraud) May 1, 2017
- Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart (https://shiring.github.io/machine_learning/2017/02/27/h2o) Feb 27, 2017
- Predicting food preferences with sparklyr (machine learning) (https://shiring.github.io/machine_learning/2017/02/19/food_spark) Feb 19, 2017
- Moving largish data from R to H2O - spam detection with Enron emails (https://ellisp.github.io/blog/2017/02/18/svmlite) Feb 18, 2016
- Deep learning & parameter tuning with mxnet, h2o package in R (http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-in-r) Jan 30, 2017

Books

- Big data in psychiatry and neurology, Chapter 11: A scalable medication intake monitoring system (https://www.elsevier.com/books/big-data-in-psychiatry-and-neurology/moustafa/978-0-12-822884-5) Diane Myung-Kyung Woodbridge and Kevin Bengtson 
Wong. (2021)
- Hands on Time Series with R (https://www2.packtpub.com/big-data-and-business-intelligence/hands-time-series-analysis-r) Rami Krispin. (2019)
- Mastering Machine Learning with Spark 2.x (https://www.packtpub.com/product/mastering-machine-learning-with-spark-2-x/9781785283451) Alex Tellez, Max Pumperla, Michal Malohlava. (2017)
- Machine Learning Using R (https://www.amazon.com/Machine-Learning-Using-Karthik-Ramasubramanian/dp/1484223330) Karthik Ramasubramanian, Abhishek Singh. (2016)
- Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI (https://www.amazon.com/Practical-Machine-Learning-H2O-Techniques/dp/149196460X) Darren Cook. (2016)
- Disruptive Analytics (http://link.springer.com/book/10.1007/978-1-4842-1311-7) Thomas Dinsmore. (2016)
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (https://web.stanford.edu/~hastie/CASI/) Bradley Efron, Trevor Hastie. (2016)
- R Deep Learning Essentials (https://www.packtpub.com/big-data-and-business-intelligence/r-deep-learning-essentials) Joshua F. Wiley. (2016)
- Spark in Action (https://www.manning.com/books/spark-in-action) Petar Zečević, Marko Bonaći. (2016)
- Handbook of Big Data (https://www.crcpress.com/Handbook-of-Big-Data/Buhlmann-Drineas-Kane-van-der-Laan/p/book/9781482249071) Peter Bühlmann, Petros Drineas, Michael Kane, Mark J. van der Laan (2015)

Research Papers

- Automated machine learning: AI-driven decision making in business analytics (https://www.sciencedirect.com/science/article/pii/S2667305323000133) Marc Schmitt. (2023)
- Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques (https://www.mdpi.com/2073-4441/15/3/475) Hamza Ahmad Madni, Muhammad Umer, Abid Ishaq, Nihal Abuzinadah, Oumaima Saidani, Shtwai Alsubai, Monia Hamdi, Imran Ashraf. 
(2023)
- Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth 
(https://www.sciencedirect.com/science/article/abs/pii/S1352231022002291?dgcid=coauthor) Padmavati Kulkarnia, V.Sreekantha, Adithi R.Upadhyab, Hrishikesh ChandraGautama.  (2022)
-  Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department (https://pubmed.ncbi.nlm.nih.gov/35467566/) Noa Galtung, Eva Diehl-Wiesenecker, Dana Lehmann, 
Natallia Markmann, Wilma H Bergström, James Wacker, Oliver Liesenfeld, Michael Mayhew, Ljubomir Buturovic, Roland Luethy, Timothy E Sweeney , Rudolf Tauber, Kai Kappert, Rajan Somasundaram, Wolfgang Bauer. (2022)
- Depression Level Prediction in People with Parkinson’s Disease during the COVID-19 Pandemic (https://embc.embs.org/2021/)) Hashneet Kaur, Patrick Ka-Cheong Poon, Sophie Yuefei Wang, Diane Myung-kyung Woodbridge. (2021)
- Machine Learning-based Meal Detection Using Continuous Glucose Monitoring on Healthy Participants: An Objective Measure of Participant Compliance to Protocol (https://embc.embs.org/2021/) Victor Palacios, Diane Myung-kyung Woodbridge, Jean L. 
Fry. (2021)
- Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth (https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25565) Alex Luna, Joel Bernanke, Kakyeong Kim, Natalie Aw, Jordan 
D. Dworkin, Jiook Cha, Jonathan Posner (2021).
- Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of Endoscopic Surgery and new technologies (the CRAC study) (https://pubmed.ncbi.nlm.nih.gov/34219197/) Alberto Sartori, Mauro 
Podda, Emanuele Botteri, Roberto Passera, Ferdinando Agresta, Alberto Arezzo. (2021) 
- Forecasting Canadian GDP Growth with Machine Learning (https://carleton.ca/economics/wp-content/uploads/cewp21-05.pdf) Shafiullah Qureshi, Ba Chu, Fanny S. Demers. (2021)
- Morphological traits of reef corals predict extinction risk but not conservation status (https://onlinelibrary.wiley.com/doi/10.1111/geb.13321) Nussaïbah B. Raja, Andreas Lauchstedt, John M. Pandolfi, Sun W. Kim, Ann F. Budd, Wolfgang 
Kiessling. (2021)
- Machine Learning as a Tool for Improved Housing Price Prediction (https://openaccess.nhh.no/nhh-xmlui/bitstream/handle/11250/2739783/masterthesis.pdf?sequence=1) Henrik I W. Wolstad and Didrik Dewan. (2020)
- Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates (https://pubs.acs.org/doi/10.1021/acs.estlett.0c00206) Timothy J. Maguire, Scott O. C. Mundle. (2020)
- Predicting Risk of Delays in Postal Deliveries with Neural Networks and Gradient Boosting Machines (https://www.diva-portal.org/smash/get/diva2:1467609/FULLTEXT01.pdf) Matilda Söderholm. (2020)
- Stock Market Analysis using Stacked Ensemble Learning Method (https://github.com/malhartakle/MastersDissertation/blob/master/Research%20Project%20Report.pdf)  Malkar Takle. (2020)
- H2O AutoML: Scalable Automatic Machine Learning (https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf). Erin LeDell, Sebastien Poirier.  (2020)
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis (https://www.nature.com/articles/s41598-020-69358-4) Jin Sung Jang, Brian D. Juran, Kevin Y. Cunningham, Vinod K. Gupta, 
Young Min Son, Ju Dong Yang, Ahmad H. Ali, Elizabeth Ann L. Enninga, Jaeyun Sung & Konstantinos N. Lazaridis. (2020)
- Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190647/) Steven N. Hart, Eric C. Polley, Hermella Shimelis, Siddhartha Yadav, Fergus J. Couch. (2020)
- Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height (https://doi.org/10.1186/s40663-020-00226-3) İlker Ercanlı. (2020)
- An Open Source AutoML Benchmark (https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf) Peter Gijsbers, Erin LeDell, Sebastien Poirier, Janek Thomas, Berndt Bischl, Joaquin Vanschoren. (2019)
- Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (https://arxiv.org/abs/2002.04803) Sebastian Raschka, Joshua Patterson, Corey Nolet. (2019)
- Human actions recognition in video scenes from multiple camera viewpoints (https://www.sciencedirect.com/science/article/pii/S1389041718308970) Fernando Itano, Ricardo Pires, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandeza. (2019)
- Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm (https://ieeexplore.ieee.org/document/8489520/authors#authors)  Fernando Itano, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandez. (2018)
- askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men (https://doi.org/10.1016/j.eururo.2018.09.050) Gregory B. Auffenberg, Khurshid R. Ghani, 
Shreyas Ramani, Etiowo Usoro, Brian Denton, Craig Rogers, Benjamin Stockton, David C. Miller, Karandeep Singh. (2018)
- Machine Learning Methods to Perform Pricing Optimization.  A Comparison with Standard GLMs (http://www.variancejournal.org/articlespress/articles/Machine-Spedicato.pdf) Giorgio Alfredo Spedicato, Christophe Dutang, and Leonardo Petrini. (2018)
- Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions (https://arxiv.org/abs/1707.04940) Yuriy Kochura, Sergii Stirenko, Yuri Gordienko. (2017)
- Algorithmic trading using deep neural networks on high frequency data (https://link.springer.com/chapter/10.1007/978-3-319-66963-2_14) Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón. (2017)
- Generic online animal activity recognition on collar tags (https://dl.acm.org/citation.cfm?id=3124407) Jacob W. Kamminga, Helena C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)
- Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning (https://link.springer.com/content/pdf/10.1007%2Fs10705-017-9870-x.pdf) Tomislav Hengl, Johan G. B. Leenaars, Keith
D. Shepherd, Markus G. Walsh, Gerard B. M. Heuvelink, Tekalign Mamo, Helina Tilahun, Ezra Berkhout, Matthew Cooper, Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena. (2017)
- Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting (https://arxiv.org/pdf/1707.09021.pdf) Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng, and Mark J. van der
Laan. (2017)
- Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition (https://arxiv.org/abs/1707.02641) Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone. (2017)
- Using deep learning to predict the mortality of leukemia patients (https://qspace.library.queensu.ca/bitstream/handle/1974/15929/Muthalaly_Reena%20S_201707_MSC.pdf) Reena Shaw Muthalaly. (2017)
- Use of a machine learning framework to predict substance use disorder treatment success (http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0175383&type=printable) Laura Acion, Diana Kelmansky, Mark van der Laan, Ethan 
Sahker, DeShauna Jones, Stephan Arnd. (2017)
- Ultra-wideband antenna-induced error prediction using deep learning on channel response data (https://www.kn.e-technik.tu-dortmund.de/.cni-bibliography/publications/cni-publications/Tiemann2017a.pdf) Janis Tiemann, Johannes Pillmann, Christian 
Wietfeld. (2017)
- Inferring passenger types from commuter eigentravel matrices (http://www.tandfonline.com/doi/abs/10.1080/21680566.2017.1291377?journalCode=ttrb20) Erika Fille T. Legara, Christopher P. Monterola. (2017)
- Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (http://www.sciencedirect.com/science/article/pii/S0377221716308657) Christopher Krauss, Xuan Anh Doa, Nicolas Huckb. (2016)
- Identifying IT purchases anomalies in the Brazilian government procurement system using deep learning (http://ieeexplore.ieee.org/document/7838233/?reload=true) Silvio L. Domingos, Rommel N. Carvalho, Ricardo S. Carvalho, Guilherme N. Ramos. 
(2016)
- Predicting recovery of credit operations on a Brazilian bank (http://ieeexplore.ieee.org/abstract/document/7838243/) Rogério G. Lopes, Rommel N. Carvalho, Marcelo Ladeira, Ricardo S. Carvalho. (2016)
- Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering (http://ieeexplore.ieee.org/abstract/document/7838276/) Ebberth L. Paula, Marcelo Ladeira, Rommel N. Carvalho, Thiago Marzagão. (2016)
- Deep learning and association rule mining for predicting drug response in cancer (https://doi.org/10.1101/070490) Konstantinos N. Vougas, Thomas Jackson, Alexander Polyzos, Michael Liontos, Elizabeth O. Johnson, Vassilis Georgoulias, Paul 
Townsend, Jiri Bartek, Vassilis G. Gorgoulis. (2016)
- The value of points of interest information in predicting cost-effective charging infrastructure locations (http://www.rsm.nl/fileadmin/Images_NEW/ECFEB/The_value_of_points_of_interest_information.pdf) Stéphanie Florence Visser. (2016)
- Adaptive modelling of spatial diversification of soil classification units. Journal of Water and Land Development (https://www.degruyter.com/downloadpdf/j/jwld.2016.30.issue-1/jwld-2016-0029/jwld-2016-0029.xml) Krzysztof Urbański, Stanisław 
Gruszczyńsk. (2016)
- Scalable ensemble learning and computationally efficient variance estimation (http://www.stat.berkeley.edu/~ledell/papers/ledell-phd-thesis.pdf) Erin LeDell. (2015)
- Superchords: decoding EEG signals in the millisecond range (https://doi.org/10.7287/peerj.preprints.1265v1) Rogerio Normand, Hugo Alexandre Ferreira. (2015)
- Understanding random forests: from theory to practice (https://github.com/glouppe/phd-thesis) Gilles Louppe. (2014)

Benchmarks

- Are categorical variables getting lost in your random forests? (http://roamanalytics.com/2016/10/28/are-categorical-variables-getting-lost-in-your-random-forests/) - Benchmark of categorical encoding schemes and the effect on tree based models 
(Scikit-learn vs H2O). Oct 28, 2016
- Deep learning in R (http://www.rblog.uni-freiburg.de/2017/02/07/deep-learning-in-r/) - Benchmark of open source deep learning packages in R. Mar 7, 2016
- Szilard's machine learning benchmark (https://github.com/szilard/benchm-ml) - Benchmarks of Random Forest, GBM, Deep Learning and GLM implementations in common open source ML frameworks. Jul 3, 2015

Presentations

- Pipelines for model deployment (https://www.slideshare.net/rocalabern/digital-origin-pipelines-for-model-deployment) Apr 25, 2017
- Machine learning with H2O.ai (https://speakerdeck.com/szilard/machine-learning-with-h2o-dot-ai-la-h2o-meetup-at-at-and-t-jan-2017) Jan 23, 2017

Courses

- University of San Francisco (USF) Distributed Data System Class (MSDS 697) (https://github.com/dianewoodbridge/2020-msds697-example) - Master of Science in Data Science Program.
- University of Oslo: Introduction to Automatic and Scalable Machine Learning with H2O and R (https://www.ub.uio.no/english/courses-events/events/all-libraries/2019/research-bazaar-2019.html) - Research Bazaar 2019
- UCLA: Tools in Data Science (STATS 418) (https://github.com/szilard/teach-data-science-UCLA-master-appl-stats) - Masters of Applied Statistics Program.
- GWU: Data Mining (Decision Sciences 6279) (https://github.com/jphall663/GWU_data_mining) - Masters of Science in Business Analytics.
- University of Cape Town: Analytics Module (http://www.stats.uct.ac.za/stats/study/postgrad/honours) - Postgraduate Honors Program in Statistical Sciences.
- Coursera: How to Win a Data Science Competition: Learn from Top Kagglers (https://www.coursera.org/learn/competitive-data-science) - Advanced Machine Learning Specialization.

Software

- modeltime.h2o R package (https://business-science.github.io/modeltime.h2o/): Forecasting with H2O AutoML
- Evaporate (https://github.com/ML4LHS/Evaporate): Run H2O models in the browser via Javascript.  More info here (https://twitter.com/kdpsinghlab/status/1367992786239242248).
- splash R package (https://github.com/ML4LHS/splash): Splashing a User Interface onto H2O MOJO Files.  More info here (https://twitter.com/kdpsinghlab/status/1367809740705792008).
- h2oparsnip R package (https://github.com/stevenpawley/h2oparsnip): Set of wrappers to bind h2o algorthms with the parsnip (https://parsnip.tidymodels.org/) package.
- Spin up PySpark and PySparkling on AWS (https://github.com/kcrandall/EMR_Spark_Automation)
- Forecast the US demand for electricity (https://github.com/RamiKrispin/USelectricity): A real-time dashboard (https://ramikrispin.github.io/USelectricity/) of the US electricity demand (forecast using H2O GLM)
- h2o3-pam (https://github.com/navdeep-G/h2o3-pam): Partition Around Mediods (PAM) clustering algorithm in H2O-3
- h2o3-gapstat (https://github.com/navdeep-G/h2o3-gapstat): Gap Statistic algorithm in H2O-3

License

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To the extent possible under law, H2O.ai (http://h2o.ai) has waived all copyright and related or neighboring rights to this work.

h2o Github: https://github.com/h2oai/awesome-h2o