For Java: Aside from the aforementioned Mahout, which focuses on Hadoop, a number of other other machine learning libraries for Java are in wide use. Weka, created by the University of Waikato in New Zealand, is a workbench-like app that adds visualizations and data-mining capabilities to the usual mix of algorithms. For people who want a front end for their work and plan on doing a good part of it in Java to begin with, Weka might be the best place to start. A more conventional library, the Java-ML, is also available, although it's meant for people already comfortable working with both Java and machine learning.
For JavaScript: The joke about JavaScript ("Atwood's Law") is that anything that can be written in JavaScript eventually will be. So it is for machine learning libraries. Granted, there's relatively little in this field available for JavaScript as of this writing -- most options consist of individual algorithms rather than whole libraries -- but a few useful tools have already surfaced. ConvNetJS lets you perform deep learning neural-network training directly in a browser, and the appropriately named brain provides neural networking as an NPM-installable module. Also worth noting is the Encog library, available for multiple platforms: Java, C#, C/C++, and JavaScript.
Read full article from 5 ways to add machine learning to Java, JavaScript, and more | JavaWorld
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