In the past few years, machine learning along with deep learning and natural language processing (NLP) has become a popular field of AI. Machine Learning brought a lot of opportunities across industries. The global market size of artificial intelligence is about to reach USD 209.91 billion by 2029 from USD 21.17 billion in 2022 at a compound annual growth rate (CAGR) of 38.8%.
Having skills and knowledge in machine learning is one of the trending ones in the tech market while Java is among the most popular languages for implementing AI and ML algorithms these days. Thus, making it essential to know about the libraries and tools in Java for machine learning.
But first, let’s understand what machine learning is.
Machine Learning: An Overview
Machine learning (ML) is a part of artificial intelligence that centers on using algorithms and data to mimic the way humans learn while improving their accuracy. From translating apps to recommendation engines, and spam filtering to autonomous vehicles, everything works around ML.
Read: AI-Powered Future: 5 Trends to Watch
Simply put, it is an algorithm software that makes insightful predictions using data to solve complex problems without being explicitly programmed to achieve so.
Some major ways in which Machine learning is classified are:
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Supervised Learning: Examples of inputs and expected outputs along with finding connections between them included in the supervised learning model. The core aim here is to use label datasets to train the machine learning model to accurately classify data and predict its result.
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Unsupervised Learning: In unsupervised learning, the model is left without label datasets to find patterns in its input to cluster data in different groups.
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Semi-Supervised Learning: This model is the combination of both supervised and unsupervised machine learning algorithms. It refers to the scenarios where the model is given a large amount of input data with a few labeled data.
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Reinforcement Learning: This ML model interacts with a dynamic environment where the program needs to perform certain tasks to receive rewards and punishments as feedback to improve its accuracy.
There are several popular languages used for developing machine learning projects, including but not limited to, Java, Python, R, JavaScript, Julia, and Lisp. Today, we are going to take a look at Java libraries and tools used for Machine Learning.
Top 15 Machine Learning Libraries and Tools in Java
For your convenience, we have enlisted the top 15 machine-learning libraries and tools in Java below.
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Weka
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DeepLearning4J
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Apache Mahout
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ADAMS
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ELKI
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JavaML
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JSAT
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Massive Online Analysis
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MALLET
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RapidMiner
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Apache Jena
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d3Web
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Powerloom
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Apache OpenNLP
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Neuroph
Java is among the most popular yet oldest and most reliable programming languages, having more than 9 million developers around the world. Java is also called the “Jack of all trades” as it provides several libraries, environments, and tools to work with machine learning.
Now that we have a vague idea of what tools and libraries get used in Java for ML, let’s understand each of them a little better.
1. Weka
It is an open-source machine-learning algorithm for tasks like data mining. Weka (an acronym for Waikato Environment for Knowledge Analysis) contains different tools for data classification, preparation, regression, association rules mining, clustering, and visualization. It comes with built-in help and a comprehensive manual for data mining using machine-learning tools and techniques. Weka can be accessed through Java API, standard terminal applications, or GUI.
2. DeepLearning4J
Eclipse DeepLearning4J is a Java suite of tools for JVM that supports deep learning algorithms. DeepLearning4J is the only framework using which you can train Java models while interoperating with python.
Some of the sub-modules that DeppLearning4J include:
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Nd4j: Has a combination of TensorFlow/PyTorch operations and NumPy operations.
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Samediff: A lower-level TensorFlow/PyTorch framework for complex graph execution.
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Python4j: A framework to execute python scripts to easily deploy Python scripts into a production environment.
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Libnd4j: A C++ library to run math code on different devices.
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Datavec: A library for data transformation that converts raw data into tensors to run neural networks.
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Apache Spark Integration: An Apache Spark framework that allows deep learning pipelines execution on spark.
3. Apache Mahout
It is an open-source, distributed Apache project that is used to develop scalable machine-learning algorithms. It is a linear algebra and mathematically expressive Scala DSL framework designed to implement ML algorithms quickly. Mahout operates alongside Hadoop which allows you to apply machine learning to distributed computing. Some of the core algorithms of Apache Mahout are data clustering, mining, and classification.
4. ADAMS
ADAMS, short for Advanced Data Analytics and Machine Learning System is a deep learning library for Java. It is used to build and maintain reactive, data-driven workflows easily. ADAMS offers a vast range of operators or actors that executes data mining, retrieval processing, and visualization. These actors connect implicitly in the tree structure.
5. ELKI
ELKI, aka Environment for Loping KDD-Applications Index-Structures, is another open-source data mining framework for Java. It focuses on algorithm research emphasizing unsupervised methods in outlier detection and cluster analysis. ELKI also includes data index structures to provide performance benefits. Besides, in ELKI both data management tasks and data mining algorithms are isolated to allow independent evaluation.
6. JavaML
JavaML is a collection of data mining and machine learning algorithms that offers common interfaces for each algorithm. It aims to be an extensible and readily available API for both research scientists and software developers. JavaML also comes with a well-documented source code with numerous code examples and tutorials. It references algorithm implementations that are defined in scientific literature.
7. JSAT
JSAT, short for Java Statistical Analysis Tool, is a machine-learning library written in Java. Each JSAT code is self-sufficient and does not require external dependencies. It is hosted on Google Code and can be used under GPL3. JSAT has the largest algorithm collections among other frameworks and is mainly used for specialized needs.
8. Massive Online Analysis
MOA is an open-source data stream mining framework. It provides a software environment to implement machine learning algorithms to run experiments for online learning from data streams. Some of the machine learning algorithms collections that MOA contains are data classification, regression, clustering, recommendation system, frequent pattern mining, and change detection.
9. MALLET
Machine Learning for Language Toolkit (MALLET) offers data clustering, statistical natural language processing, topic modeling, document classification, and other machine learning apps to text packages.
10. RapidMiner
It is a comprehensive data science platform that offers a set of products to help data analysts in creating data mining processes and set predictive models. It makes data analytics easier for users while remaining scalable, secure and governed.
11. Apache Jena
It is a semantic web framework designed for Java. Apache Jena is used for developing Linked Data and Semantic Web Applications. It comes with RDF API that can be used to create and read RDF (Resource Description Framework) graphs. Apart from RDF, Apache Jena provides a programming environment for RDFS, OWL, and SPARQL. It also supports RDG graph serialization to RDF/XML, relational database, Turtle, TriG, JSON-LD, and Notation 3.
12. d3Web
d3Web is an open-source, free knowledge-based system that is written in office-based formats for storage, and its core is in Java XML. Besides, its components are distributed under LGPL (Lesser General Public License). d3Web implements persistence and reasoning components to help solve problems like set-covering models, decision trees, diagnostic flowcharts, and heuristic rules. Some main applications of d3Web include the diagnosis of a technical fault, medical documentation, therapy, and monitoring of technical devices.
13. Powerloom
A successor of Loom, Powerloom is a knowledge representation system using which one can create intelligent, knowledge-based apps. It uses a logic-based, expressive language and natural deduction interface engine to derive what logically follows the rules and facts in the knowledge base. Powerloom is executed in Stella which can be later translated into Java, Lisp, and C++.
14. Apache OpenNLP
An open-source Java library for natural language processing that features an API for POS tagging, Named Entity Recognition, Sentence Detection, chunking, conference resolution, parsing, and Tokenization.
15. Neuroph
An object-oriented artificial neural network Java framework used to develop and train machine learning neural networks. It provides GUI tools and a Java class library to train these neural networks. Both GUI and Java library approaches rely on underlying hierarchical classes that create artificial neural networks from the neurons’ layers. Moreover, Neuroph classes correspond to concepts of networks such as neuron layer, artificial neuron, neuron connections, input functions, transfer functions, etc.
Takeaway
We have listed the top 20 Java Machine Learning libraries, tools, and frameworks that are mostly open-source. The choice of the library and tool will entirely depend on the implementation of the neural network and support for the algorithm.
We hope this blog has helped you understand the concept of machine learning along with Java libraries that are used to develop ML projects.
If you are a business owner interested in developing a machine learning software application for your business, then make sure that you hire developers with excellent skill sets and years of experience in the AI field.
You can also get in touch with Decipher Zone to share your idea and get a quote on the project.
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FAQs
Q1. Which Java library is used for machine learning?
ADAMS, JavaML, JSAT, Apache OpenNLP, and Neuroph are some of the Java Libraries that can be used for machine learning.
Q2. Can I do ML with Java?
Although Java is not a popular programming language choice for machine learning, using some third-party libraries and frameworks Java developers can implement machine learning and data science in their applications.
Q3. Can I use PyTorch with Java?
Yes, with an easy-to-use API provided by Amazon’s Deep Java Library (DJL), you can use PyTorch with Java.