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Select Page
 * About
   * Mission
   * History
   * Code of Conduct
   * People
   * Finance
   * Annual Reports
   * Public Records & Legal Documents
 * Projects
   * Overview & How to Apply
   * Sponsored Projects
   * Affiliated Projects
   * Case Studies: Our Impact
   * Trademark Guidelines
 * Programs
   * PyData
   * Fiscal Sponsorship
   * Small Development Grants
   * Diversity & Inclusion
   * Google Summer of Code
   * NumFOCUS Jobs Board
 * Support
   * Volunteer
   * Contribute Code
   * Open Science Champions
   * Donation Acceptance Policy
   * Corporate Sponsorship
     * Benefits
     * Prospectus
     * Our Sponsors
     * Sponsor a Visiting Fellow
   * NumFOCUS Shop
 * Blog
 * Donate




MLPACK

NumFOCUS Sponsored Project since 2019

mlpack is a fast, flexible machine learning library suitable for both data
science prototyping and deployment.

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

INDUSTRY

Business & Industry Applications
Higher Education Research & Teaching



LANGUAGE

Python
C++
Julia



FEATURES

Big Data
Statistical Computing
Numerical Computing
Data Mining
Text Processing
Machine Learning

 * Technical Details
 * Applications

mlpack is a fast, flexible machine learning library, written in C++, that 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 (November 2019).


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Website
Donate To mlpack



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CONTACT US

NumFOCUS
P.O. Box 90596
Austin, TX 78709
info@numfocus.org*protected email*
+1  (512) 831-2870


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