Companies are competing for programmers who can write code for machine learning and deep learning. If you're interested in these topics, perhaps you should take a look at the best Java libraries for machine learning today?
Mini glossary
Machine learning is an approach in which artificial intelligence does not initially know how to solve a specific problem, but learns this process by solving similar problems. Various mathematical methods are used to build relationships. Deep or deep learning is a subset of machine learning that most often uses neural networks that imitate human decision making. Deep learning most often requires huge amounts of training data. For example, if you need to set up an algorithm to recognize a saxophone, the program will have to feed not only images of saxophones, but also other similar brass instruments, so that the algorithm does not confuse them in the future.
Is this promising?
The long winter of artificial intelligence development is over. Machine learning is now one of the hottest skills in technology, and experts who understand the topic are in high demand. So far, no language has become the absolute leader in the field of machine learning, but Java is clearly among the favorites. So if you are interested in science and want to do research rather than just coding, we suggest you take a look at the top five Java libraries for machine learning programming.
Weka
Library link: https://www.cs.waikato.ac.nz/ml/weka/index.html Weka is our absolute favorite and no surprise there. This product was conceived as a modern environment for developing machine learning methods and applying them to real data, an environment that will make machine learning methods available for widespread use. With Weka, an application scientist can use machine learning techniques to extract useful knowledge directly from possibly very large data. Weka 3 is free software written in Java. The product integrates well into other code, as it provides direct access to the library of algorithms implemented in it. That is, you will be able to use already written algorithms from other systems implemented in Java. In addition, Weka has its own Explorer user interface, the Knowledge Flow component interface, but you can also use the command line. In addition, an Experimenter application has been developed for Weka to compare the predictive ability of machine learning algorithms on a given set of problems.
“Weka's strength lies in classification. So it is ideal for applications that require automatic data classification. Weka also performs clustering, prediction, feature selection and anomaly detection tasks,” says Eibe Frank, professor of computer science at the University of Waikato in New Zealand. Weka's suite of machine learning algorithms can be applied directly to a database or called from your own Java code. It supports several standard data mining tasks, including data preprocessing, classification, clustering, visualization, regression analysis, and feature selection. Example of using Weka with Java code: https://weka.wikispaces.com/Use+WEKA+in+your+Java+code
Massive Online Analysis (MOA)
Library link: https://moa.cms.waikato.ac.nz/ MOA (Massive On-Line Analysis) is a framework for developing data streams. It includes evaluation tools and a set of machine learning algorithms that are time and memory efficient. This open source library is used directly for machine learning and collecting information from data streams in real time. MOA is written in Java, so it can be easily used together with Weka to solve large-scale and complex problems. MOA's suite of machine learning algorithms and scoring engines are useful for regression analysis, classification, outlier detection, clustering, recommender systems. MOA can also be useful for changing sets and flows of data, as well as data produced by Internet of Things (IoT) devices.
The workflow in MOA follows a simple scheme: first, a data stream (channel, generator) is selected and configured, then an algorithm (for example, a classifier) is selected and its parameters are set. In the third step, an assessment method or measure is selected, after which the result will be obtained.
To run an experiment using MOA, the user can choose between a graphical user interface (GUI) or the command line.
Deeplearning4
Library Link: https://deeplearning4j.org/ Deeplearning4j is one of the innovative contributors that is bringing significant innovation to the Java ecosystem. This is a very high-quality library, an open-source project in Java and Scala, offered to us by the excellent guys (and semi-intelligent robots!) Skymind. The idea behind Deep;earning4j is to combine deep neural networks and deep learning for the business environment.
Deeplearning4j is a great DIY tool for Java, Scala, and Clojure programmers who work with Hadoop, a framework for developing and running distributed programs running on clusters with thousands of nodes. Hadoop has enormous computing power and the ability to handle a virtually unlimited number of parallel tasks. Deep neural networks and deep reinforcement learning are capable of pattern recognition and targeted machine learning. This means that Deeplearning4j is very useful for identifying patterns and sentiments in speech, audio and text. Additionally, the library can be used to detect anomalies in time series data such as financial transactions.
MALLET
Library link: http://mallet.cs.umass.edu/ MALLET is an open-source Java toolkit created by Andrew McCallum and students at UMASS and UPenn. This library is designed primarily for text processing using machine learning. Explanation of the name of the library - MAchine Learning for LanguagE Toolkit.
This software package supports statistical natural language processing, clustering, document classification, information retrieval, modeling, and other advanced learning applications. MALLET's main strength is modern tools for document classification, such as efficient standard procedures for text transformation. It supports a wide range of algorithms, including Naive Bayes, Decision Trees, Maximum Decision Trees, and Maximum Entropy, as well as classifier performance analysis code.
ELKI
Library link: https://elki-project.github.io/ ELKI stands for Environment for Developing KDD-Applications Supported by Index Structures, which can be translated as “an environment for developing KDD applications supported by index structures.” In turn, KDD - Knowledge discovery in databases - is an abbreviation that hides the possibility of large-scale extraction of knowledge from data. ELKI's focus is on algorithmic research with an emphasis on unsupervised methods in cluster analysis and outlier detection. To achieve high performance and scalability, ELKI offers data index structures such as R*-trees (such data structures are used to index spatial information), which can provide significant performance gains. ELKI is easily extensible and aims to provide a large set of highly parameterizable algorithms to enable easy and fair evaluation of algorithms.
This means that ELKI is extremely useful in the field of data science. This tool was used to study the sounds of sperm whales, space flights and traffic forecasting. Perhaps you know of another Java machine learning library that hasn't been covered? Share with us in your comments, it’s interesting to find out what we might have missed! Based on materials from jaxenter
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