Awesome H2O

Below is a curated list of all the awesome projects, applications, research, tutorials, courses and books that use H2O. H2O.ai produces many tutorials, blog posts, presentations and videos 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 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.

Blog Posts & Tutorials
Books
Research Papers
Benchmarks
Presentations
Courses
Utilities
License

Blog Posts & Tutorials

Automated and unmysterious machine learning in cancer detection Nov 7, 2017
Time series machine learning with h2o+timetk Oct 28, 2017
Sales Analytics: How to use machine learning to predict and optimize product backorders Oct 16, 2017
HR Analytics: Using machine learning to predict employee turnover Sep 18, 2017
Autoencoders and anomaly detection with machine learning in fraud analytics May 1, 2017
Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart Feb 27, 2017
Predicting food preferences with sparklyr (machine learning) Feb 19, 2017
Moving largish data from R to H2O - spam detection with Enron emails Feb 18, 2016
Deep learning & parameter tuning with mxnet, h2o package in R Jan 30, 2017
Are categorical variables getting lost in your random forests? Oct 28, 2016

Books

Machine Learning Using R Karthik Ramasubramanian, Abhishek Singh. (2016)
Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI Darren Cook. (2016)
Disruptive Analytics Thomas Dinsmore. (2016)
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science Bradley Efron, Trevor Hastie. (2016)
R Deep Learning Essentials Joshua F. Wiley. (2016)
Spark in Action Petar Zečević, Marko Bonaći. (2016)
Handbook of Big Data Peter Bühlmann, Petros Drineas, Michael Kane, Mark J. van der Laan (2015)

Research Papers

Algorithmic trading using deep neural networks on high frequency data Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón. (2017)
Generic online animal activity recognition on collar tags Jacob W. Kamminga, Helena C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)
Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting 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 Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone. (2017)
Using deep learning to predict the mortality of leukemia patients Reena Shaw Muthalaly. (2017)
Use of a machine learning framework to predict substance use disorder treatment success 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 Janis Tiemann, Johannes Pillmann, Christian Wietfeld. (2017)
Inferring passenger types from commuter eigentravel matrices Erika Fille T. Legara, Christopher P. Monterola. (2017)
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 Christopher Krauss, Xuan Anh Doa, Nicolas Huckb. (2016)
Identifying IT purchases anomalies in the Brazilian government procurement system using deep learning Silvio L. Domingos, Rommel N. Carvalho, Ricardo S. Carvalho, Guilherme N. Ramos. (2016)
Predicting recovery of credit operations on a Brazilian bank 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 Ebberth L. Paula, Marcelo Ladeira, Rommel N. Carvalho, Thiago Marzagão. (2016)
Deep learning and association rule mining for predicting drug response in cancer 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 Stéphanie Florence Visser. (2016)
Adaptive modelling of spatial diversification of soil classification units. Journal of Water and Land Development Krzysztof Urbański, Stanisław Gruszczyńsk. (2016)
Scalable ensemble learning and computationally efficient variance estimation Erin LeDell. (2015)
Superchords: decoding EEG signals in the millisecond range Rogerio Normand, Hugo Alexandre Ferreira. (2015)
Understanding random forests: from theory to practice Gilles Louppe. (2014)

Benchmarks

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 - Benchmark of open source deep learning packages in R. Mar 7, 2016
Szilard's machine learning benchmark - Benchmarks of Random Forest, GBM, Deep Learning and GLM implementations in common open source ML frameworks. Jul 3, 2015