From Machine Learning to Deep Learning | Part3 Machine Learning Libraries in Java
In these tutorial series we will learn about machine learning algorithms and implementation of them in java. After learning completely about machine learning we will learn how we can improve the result of some problems with deep learning.
Waikato Environment for Knowledge Analysis (WEKA)is a machine learning library that was developed at the University of Waikato, New Zealand, and is probably the most well-known Java library. It is a general purpose library that is
able to solve a wide variety of machine learning tasks, such as classification, regression, and clustering. It features a rich graphical user interface, command-line interface, and Java API.
The Java Machine Learning Library (Java-ML) is a collection of machine learning algorithms with a common interface for algorithms of the same type. It only features the Java API, and so it is primarily aimed at software engineers and
programmers. Java-ML contains algorithms for data preprocessing, feature selection, classification, and clustering
Massive Online Analysis is the most popular open source framework for data stream mining. MOA is used specifically for machine learning and data mining on data streams in real time. Its Java machine learning algorithms and tools for evaluation are useful for classification, regression, clustering, outlier detection, concept drift detection, and recommendation systems.
ELKI stands for the Environment for Developing KDD-Applications Supported by Index Structures. The open source data mining software is written in Java. It is designed for researchers and is often used by graduate students looking to create a sensible database.
Encog is a machine learning framework in Java/C# that was developed by Jeff Heaton, a data scientist. It supports normalizing and processing data and a variety of advanced algorithm such as SVM, Neural Networks, Bayesian Networks,
Hidden Markov Models, Genetic Programming, and Genetic Algorithms. It has been actively developed since 2008.
The Machine Learning for Language Toolkit (MALLET) is a large library of natural language processing algorithms and utilities. It can be used in a variety of tasks such as document classification, document clustering, information extraction, and topic modelling. It features a command-line interface as well as a
Java API for several algorithms such as Naive Bayes, HMM, Latent Dirichlet topic models, logistic regression, and conditional random fields.
It’s the first commercial-grade, open-source distributed deep learning library written in Java. DL4J is compatible with other JVM languages, e.g., Scala, or Kotlin. Integrated with Hadoop and Spark, it’s meant to be a DIY tool for the programmers.
The mission of DL4J is to bring deep neural networks and deep reinforcement learning together for business environments rather than research. DL4J provides API for neural network creation and supports various neural network structures: feedforward neural networks, RBM, convolutional neural nets, deep belief networks, autoencoders, etc.
Apache Spark is a platform for large-scale data processing built atop Hadoop. Spark’s module MLlib is a scalable machine learning library. Written in Scala, MLib is usable in Java, Python, R, and Scala. MLlib can be easily plugged into Hadoop workflows and use both Hadoop-based data sources and local files. The supported algorithms include classification, regression, collaborative filtering, clustering, dimensionality reduction, and optimization.
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From Machine Learning to Deep Learning | Part3 Machine Learning Libraries in Java
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