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H2Oai GPU Edition
data.table for Python
Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
Library of different Jenkins pipeline building blocks.
Tutorials and training material for the H2O Machine Learning Platform
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
reproducible speed and memory benchmark of database-like ops
Sparkling Water provides H2O functionality inside Spark cluster
H2OAI Driverless AI Code Samples and Tutorials
Web based interactive computing environment for H2O
Testing ground to ensure mojos backward compatibility
Machine Learning Interpretability Resources
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Presentations from H2O meetups & conferences by the H2O.ai team
Python implementation of Benford's Law tests.
pandas, scikit-learn, xgboost and seaborn integration
Python 2.7 & 3.5+ runtime type-checker
A curated list of research, applications and projects built using H2O Machine Learning
RSparkling: Use H2O Sparkling Water from R (Spark + R + Machine Learning)
Jupyter magics and kernels for working with remote Spark clusters