Artificial intelligence (AI) has existed for a long time but it has become one of the hottest topics in the market.In this article, we will cover AI libraries in Java.
Artificial Intelligence is no longer a tool for scientists and researchers.It’s becoming one of the hottest technology. AI used by some of the best-known tech giants like Google, Amazon etc.Artificial Intelligence (AI) is a wide field and this post is not to give an overview or understanding of AI.Let’s cover some of the popular Artificial Intelligence (AI) in Java.
1. Machine Learning
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
1.2 MOA (Massive On-line Analysis)
MOA (Massive On-line Analysis) is a framework for data stream mining. It includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, it is also written in Java, while scaling to more demanding problems.
1.3 Encog Machine Learning Framework
Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms.
Java-ML is an open source Java framework which provides various machine learning algorithms specifically for programmers.
1.5 MLlib (Spark)
MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction.
H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Flow notebook/web interface, and works seamlessly with big data technologies like Hadoop and Spark.
Apache SINGA is an open-source machine-learning library capable of distributed training, with a focus on healthcare applications.
RapidMiner is a data science platform that supports various machine and deep-learning algorithms through its GUI and Java API. It has a very big community, many available tutorials, and an extensive documentation.
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
2. Neural Network Library
Deeplearning4j is an Open source, distributed and commercial-grade deep-learning library for JVM.
Neuroph is lightweight Java neural network framework to develop common neural network architectures.Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.
Arbiter is a hyperparameter optimization library designed to automate hyperparameter tuning of deep neural net training. It is the equivalent of Google Tensorflow’s Vizier or the Python library Spearmint.
3. NLP -Natural Language Processing
3.1 Apache OpenNLP
Apache OpenNLP is a machine-learning toolkit for processing natural language; i.e. text. The official website provides API documentation with information on how to use the library.
3.1 Stanford CoreNLP
Stanford CoreNLP is the most popular Java natural-language processing framework. It provides various tools for NLP tasks. The official website provides tutorials and documentation with information on how to use this framework.
4. Rule-Based System
Tweety is a collection of various Java libraries that add approaches to different areas of artificial intelligence. In particular, it provides a general interface layer for doing research and working with different knowledge representation formalisms such as classical logic, conditional logic, probabilistic logic, and argumentation.
Drools is a business rules management system backed by Red Hat.
d3web provides problem-solving knowledge to solve diagnosis tasks, where observations are entered into the system and proper diagnoses are returned as a result.
5. Bonus Reading
There are numbers of sites which run challenges and competitions online.If you are working with AI and want to enhance and test your knowledge, have a look at the following sites.
In this article, we covered various Java AI frameworks which can be used in everyday work.