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A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
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.
The Open Source Feature Store for AI/ML
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
One advanced and mature open-source MPP (Massively Parallel Processing) database. Open source alternative to Greenplum Database.
Sparkling Water provides H2O functionality inside Spark cluster
🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
oneAPI Data Analytics Library (oneDAL)
CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.
High-performance, time-indexed object storage for robotics and industrial IoT
Estimating the number of clusters in a data set via the gap statistic. Implemented in H2O-3
Implementation of Partitioning Around Medoids (PAM) in H2O-3