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Best Machine Learning Libraries For Java Development

These days having skills in deep learning and machine learning is one of the most trending things in the tech world. More and more businesses are looking to hire developers from top web development companies who possess good knowledge of machine learning.

In this post, we will list down some of the best libraries for implementing machine learning in existing Java applications. All these libraries have been compiled by the popularity level from various blogs, websites, and forums.

  • 1- Deeplearning4j

This machine learning library is specially designed for Java by providing a computer framework with wide support for deep learning algorithms. It is considered as one of the best contributors for Java when it comes to machine learning, it an open source deep learning library which brings deep neural networks and deep learning reinforcement together for the various business environment. It usually works as a DIY tool for Java and has the ability to handle all the limitless virtual concurrent tasks.

Moreover, this library is useful for identifying sentiment and patterns in a speech, text, and sound. It can also be used for finding out anomalies in time series data like financial data clearly shows that it can be used for business scenarios rather than a research tool.

  • 2- ELKI

ELKI stands for Environment for Developing KDD- Applications Supported by Index-structure, is another open source machine learning library designed for data mining in Java. Specially designed for researchers and students, it provides a large number of algorithmic parameters which are highly configurable.

It is mostly used by graduate students who are looking to create some sensible database. It aims to develop and evaluate advanced data mining algorithm and its interaction with database index structures. With ELKI Java developers can use arbitrary data types, file formats, or distance or similar measurements.

  • 3- JavaML

It is a Java library with a huge collection of machine learning and data mining algorithms. It is developed to be used by both research scientists and Java developers. There is no GUI for this library but it features clear interface for each type of algorithms.

When we compare it with other clustering algorithms it is very straightforward and allows easy implementation of any new algorithm. Most of the times, the implementation of algorithms is clearly written and properly documented, thus it can be used as a reference. The library is developed using Java.

 

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