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Project Summary

Analyzed about 2 months ago. based on code collected about 2 months ago.

Apache Mahout's goal is to build scalable machine learning libraries. With scalable we mean:
Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms

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In a Nutshell, Apache Mahout...

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Java
84%
Scala
11%
4 Other
5%

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30 Day Summary

Jul 9 2016 — Aug 8 2016

12 Month Summary

Aug 8 2015 — Aug 8 2016
  • 307 Commits
    Up + 75 (32%) from previous 12 months
  • 8 Contributors
    Down -3 (27%) from previous 12 months

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