H2O - the optimized HTTP/1, HTTP/2, HTTP/3 server
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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.
Realtime Web Apps and Dashboards for Python and R
- app
- dashboard
- visualization
- python
tiny HTTP parser written in C (used in HTTP::Parser::XS et al.)
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 Python package for manipulating 2-dimensional tabular data structures
- python
- data-analysis
- data-structure
- performance
A curated list of gradient boosting research papers with implementations.
- gradient-boosting
- gradient-boosting-classifier
- gradient-boosting-machine
- gradient-boosted-trees
Sparkling Water provides H2O functionality inside Spark cluster
- h2o
- spark
- machine-learning
- integration
Presentations from H2O meetups & conferences by the H2O.ai team
- h2o
- presentations
- meetups
- h2oai
Materials for GWU DNSC 6279 and DNSC 6290.
A repository for deploying an AWS EMR cluster and submiting spark jobs on it. Boostrapping by default does inclues pysparkling so one can easily use h2o with python and spark.
Splashing a User Interface onto h2o MOJO Files
Estimating the number of clusters in a data set via the gap statistic. Implemented in H2O-3
- kmeans
- kmeans-clustering
- clustering
- gap-statistic
Implementation of Partitioning Around Medoids (PAM) in H2O-3
- clustering
- machine-learning
- h2o-3
- java