NumFOCUS is pleased to announce the newest addition to our fiscally sponsored projects: mlpack.
Mlpack is a fast, flexible machine learning library suitable for both data science prototyping and deployment. Written in C++, mlpack aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack provides these algorithms as simple command-line programs, Python bindings, and C++ classes which can then be integrated into larger-scale machine learning solutions.
Mlpack was originally developed as a vehicle to implement and test fast machine learning algorithms published at top conferences, like ICML, KDD, and NeurIPS. This academic background has led to mlpack being used in many scientific publications both inside the machine learning community and in adjacent fields. Mlpack is an increasingly popular choice for general data science applications, with over 3000 stars on GitHub at the time of this writing.
The mlpack leadership body for NumFOCUS consists of Ryan Curtin, Marcus Edel, Shikhar Jaiswal, Mikhail Lozhnikov and Sumedh Ghaisas.
With the addition of mlpack, the NumFOCUS fiscal sponsorship program now encompasses 30 open source scientific computing projects.