Easy benchmarking of all publicly accessible implementations of convnets
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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.).
High-performance Piecewise Geometric Model (PGM) learned index implementation in pure Rust with strict epsilon error guarantees
A simple C++ 03/11/etc timer class for ~microsecond-precision cross-platform benchmarking. The implementation is as limited and as simple as possible to create the lowest amount of overhead.