H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
A curated list of gradient boosting research papers with implementations.
Sparkling Water provides H2O functionality inside Spark cluster
Presentations from H2O meetups & conferences by the H2O.ai team
Materials for GWU DNSC 6279 and DNSC 6290.
Implementation of Partitioning Around Medoids (PAM) in H2O-3