Top 6 Java Machine Learning/Deep Learning frameworks you can’t miss

The data science tech market is buzzing with new and interesting Machine Learning libraries and tools almost everyday. In an increasingly growing market, it becomes difficult to choose the right tool or set of tools. More importantly, Artificial Intelligence and Deep Learning based projects require a different approach than traditional programming which makes things tricky to zero-in on one library or a framework. The choice of a framework is largely based upon the type of problem, one is expecting to solve. But there are other considerations too. Speed is one such factor that more or less would always play an important role in decision making. Other reasons could be how open-ended it is, architecture, functions, complexity of use, support for algorithms, and so on.

Here, we present to you six Java libraries for your next Deep Learning and Artificial Intelligence project you shouldn’t miss if you are a Java loyalist or simply a web developer who wants to enter the world of deep learning.

DeepLearning4j (DL4J)

One of the first, commercial grade, and most popular deep learning frameworks developed in Java. It also supports other JVM languages (Java, Clojure, Scala). What’s interesting about the DL4J, is that it comes with an in-built GPU support for the training process. It also supports Hadoop YARN for distributed application management. It is popular for solving problems related to image recognition, fraud detection and NLP.

MALLET

Mallet (Machine Learning for Language Toolkit) is an open source Java Machine Learning toolkit. It supports NLP, clustering, modelling, and classification. The most important capability of Mallet is its support for a wide variety of algorithms such as Naive Bayes and Decision Trees. Another useful feature it has is topic modelling toolkit. Topic models are useful when analyzing large collections of unlabelled texts.

Massive Online Analysis (MOA)

MOA is an open source data streaming and mining framework for real time analytics. It has a strong and growing community and is similar and related to Weka. It also has the ability to deal with massive data streams.

Encog

This framework supports a wide array of algorithms and neural networks such as Artificial Neural Network, Bayesian Network, Genetic Programming and algorithms.

Neuroph

Neuroph as the name suggests offers great simplicity when working on neural networks. The main USP of Neuroph is its incredibly useful GUI (Graphical User Interface) tool that helps in creating and training neural networks. Neuroph is a good choice of framework when you have a quick project on hand and you don’t want to spend hours learning the theory. Neuroph helps you quickly set up and running in putting neural networks to work for your project.

Java Machine Learning Library

The Java Machine Learning Library offers a great set of reference implementation of algorithms that you can’t miss for your next Machine Learning project. Some of the key highlights are support vector machines and clustering algorithms.

These are a few key frameworks and tools you might want to consider when working on your next research work. The Java ML library ecosystem is vast with many tools and libraries to support, and we just touched the tip of that iceberg in this article. One particular tool that deserve an honourable mention is Environment for Developing KDD-Applications Supported by Index-Structure (ELKI). It is designed particularly with researchers and research students kept in mind. The main focus of ELKI is its broad coverage of data algorithms which makes it a natural fit for research work.

What’s really important while choosing any of the above or tools outside of the list is a good understanding of the requirements and the problems you intend to solve. To reiterate, some of the key considerations to bear in mind before zeroing in on a tool would be - support for algorithms, implementation of neural networks, dataset size (small, medium, large), and speed.