Implementing Convex Optimization Into H2O

h2oai / h2o-3

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.

Apache License 2.0

6.79k stars 1.99k forks source link

Many Data science researchers and practitioners needs reliable framework for convex optimization to be able modify the core of existing machine learning methods or create new machine learning methods.

Hence developing a scalable convex optimization framework in h2o can help many data scientist to use h2o in more advance way to tailor their data science solution to problems at hand or derive new machine learning methods by modifying existing optimization methods or adding new ones.

References http://www.jmlr.org/papers/v7/MLOPT-intro06a.html

h2oai / h2o-3

Implementing Convex Optimization Into H2O #12013