Java developers must see! Machine learning development library selection!

In fact, Java has become the standard language for developing new machine learning algorithms. There are many benefits to learning the Java language, and it is widely accepted in the machine learning community. Its advantages include: easy maintenance, commercialization, readability, portability, and so on. According to data provided by TIOBE-index, the programming language leaderboard of June 2018, Java is currently the most popular and widely used computer programming language, including PHP, C, C++, Python, and JavaScript. If a company wants to integrate a machine learning business into an existing Java business application, then it must hire a Java developer.

Below we will list some of the best libraries for implementing machine learning in some existing Java applications. All of these libraries are written through the popularity of various blogs, websites, and forums.

Deeplearning4j

This machine learning library is designed specifically for Java and is a computer framework that provides extensive support for developing deep learning algorithms. When it comes to machine learning, Deeplearning4j is considered one of Java's most prominent contributors. It is an open source deep learning library that brings deep neural networks and enhanced deep learning to a variety of business environments. It is also often used as a Java DIY tool, but it also has the ability to handle all of these unrestricted virtual concurrent tasks.

In addition, the library is useful for recognizing emotions and patterns in speech, text, and sound. It can also be used to discover anomalies in time series data, such as financial data, to make it clear that it can be used in actual business scenarios rather than simply as a research tool.

ELKI

ELKI represents the environment for developing KDD applications. The KDD application is supported by an indexing structure, another open source machine learning library developed by Java for data mining. Designed for researchers and students, ELKI provides a large number of algorithm parameters that can be configured in detail.

ELKI is primarily used by postgraduate students seeking to establish some relevant databases. Its purpose is to develop and evaluate advanced data mining algorithms and their cooperation and interaction with database indexing structures. Java developers using ELKI can use arbitrary data types, file formats, and more.

JavaML

JavaML is a Java class library with a large number of machine learning and data mining algorithms developed by research staff and Java developers. This library does not contain a GUI, but it provides a clear interface for each type of algorithm. When we compare JavaML with other clustering algorithms, we find it to be very straightforward and provide a simple implementation of any new algorithm. In more cases, the implementation of these algorithms requires a simple and straightforward degree of code and the addition of appropriate comments, so it can serve as a good reference. This library was developed in Java.

JSAT

JSAT provides a Java statistical analysis tool, a Java-based library for solving machine learning problems. It can be used under the GPL3 license, and all source code is included without any external dependencies. JSAT has one of the largest collections of machine learning algorithms in the library and is considered one of the fastest running Java machine learning libraries, providing efficiency and flexibility. All machine learning algorithms in the library are implemented using an object-oriented framework.

MALLET

The MALLET library is a language toolkit for machine learning. It is a collection of Java source code. Applications include statistical NLP, topic modeling, cluster analysis, document classification, and various other machine learning applications for text. MALLET can also be called the Java Machine Learning Toolkit for Text, developed by students at the University of Massachusetts and the University of Pennsylvania, and supports algorithms such as decision trees, naive Bayes, and maximum entropy models. .

Mahout

Mahout is a machine learning framework with integrated machine learning algorithms that help developers create their own algorithm implementations. Mahout is also an algebra-related framework designed to help data scientists, mathematicians, and analysts run their own algorithms. This machine learning library is extensible and provides a rich set of components that allow you to develop a custom recommendation system for a variety of machine learning algorithms. Mahout delivers high performance, flexibility and scalability while also being designed for commercial use.

Weka

Weka is another very popular Java-based machine learning library that can be used for data mining and data analysis, where algorithms can be applied directly to a data set, or new algorithms can be created using Java source code. This Java library contains many tools such as regression, clustering, classification, and visualization. The library is not only free, but also easy to use, supporting feature selection, anomaly detection, time series prediction, and more. Weka's full name is the Waikato Environment for Knowledge Analysis, which can be defined as a collection of algorithms and tools for predictive modeling and data analysis and graphical user interfaces (GUIs).

Conclusion

In the past few years, there has been a new trend in the field of machine learning. In fact, most of the above libraries are open source, which means that developers can easily grasp the development capabilities of machine learning and get relevant technical information, and all developers must consider using these libraries. What can be done. Java-based machine learning technology will revolutionize the workings of applications.