Open Source Java Machine Learning Software
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Java Machine Learning Software
View 446 business solutionsBrowse free open source Java Machine Learning Software and projects below. Use the toggles on the left to filter open source Java Machine Learning Software by OS, license, language, programming language, and project status.
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Weka
Machine learning software to solve data mining problems
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. -
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Wurst Client
Minecraft Wurst Hacked Client v7
Wurst7 is an open-source modified Minecraft client that includes a large collection of gameplay modifications commonly referred to as “hacks” or cheat modules. The project provides a custom client environment where players can enable various automated tools, overlays, and gameplay enhancements that alter how the game behaves. These features may include movement enhancements, automation utilities, and visualization tools that provide additional information about the game world. Wurst7 is typically installed as a Fabric mod and runs alongside the standard Minecraft Java Edition client. The client includes a graphical interface that allows users to toggle different modules and configure their behavior during gameplay. It is designed primarily for experimentation, testing, or gameplay modifications in environments where such tools are permitted. -
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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MIT Deep Learning Book
MIT Deep Learning Book in PDF format by Ian Goodfellow
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville. An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville. Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. This is not available as PDF download. So, I have taken the prints of the HTML content and bound them into a flawless PDF version of the book, as suggested by the website itself. Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well. -
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Kodezi Chronos
Kodezi Chronos is a debugging-first language model
Kodezi Chronos is a research project focused on developing a specialized language model designed specifically for debugging software and understanding large code repositories. Unlike general-purpose language models that focus primarily on code generation, Chronos is built to diagnose and repair bugs by analyzing complex relationships across files within a codebase. The project introduces architectural techniques such as Adaptive Graph-Guided Retrieval, which allows the system to navigate large repositories and retrieve relevant debugging information from multiple sources. Another component, Persistent Debug Memory, allows the system to learn patterns from past debugging sessions and apply that knowledge to future problems. The repository mainly contains research documentation, evaluation benchmarks, and experimental frameworks rather than the full proprietary model implementation. -
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Clustering Variation looks for a good subset of attributes in order to improve the classification accuracy of supervised learning techniques in classification problems with a huge number of attributes involved. It first creates a ranking of attributes based on the Variation value, then divide into two groups, last using Verification method to select the best group.
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This project contains weka packages of neural networks algorithms implementations like Learning Vector Quantizer (LVQ) and Self-organizing Maps (SOM). For more information about weka, please visit http://www.cs.waikato.ac.nz/~ml/weka/
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides native Java development experience and functions like any other regular Java library. You don't have to be a machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. -
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GROBID
A machine learning software for extracting information
GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. Header extraction and parsing from article in PDF format. The extraction here covers the usual bibliographical information (e.g. title, abstract, authors, affiliations, keywords, etc.). References extraction and parsing from articles in PDF format, around .87 F1-score against on an independent PubMed Central set of 1943 PDF containing 90,125 references, and around .89 on a similar bioRxiv set of 2000 PDF (using the Deep Learning citation model). All the usual publication metadata are covered (including DOI, PMID, etc.). -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
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MODLEM
rule-based, WEKA compatible, Machine Learning algorithm
This project is a WEKA (Waikato Environment for Knowledge Analysis) compatible implementation of MODLEM - a Machine Learning algorithm which induces minimum set of rules. These rules can be adopted as a classifier (in terms of ML). It is a sequential covering algorithm, which was invented to cope with numeric data without discretization. Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule induction. In result numeric attribute's conditions are more precise and closely describe the class. This algorithm contains some aspects of Rough Set Theory: the class definition can be described accordingly to its lower or upper approximation. For more information, see: Stefanowski, Jerzy. The rough set based rule induction technique for classification problems. In: Proc. 6th European Congress on Intelligent Techniques and Soft Computing, vol. 1. Aachen, 1998. s. 109-113. -
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TorchServe
Serve, optimize and scale PyTorch models in production
TorchServe is a performant, flexible and easy-to-use tool for serving PyTorch eager mode and torschripted models. Multi-model management with the optimized worker to model allocation. REST and gRPC support for batched inference. Export your model for optimized inference. Torchscript out of the box, ORT, IPEX, TensorRT, FasterTransformer. Performance Guide: built-in support to optimize, benchmark and profile PyTorch and TorchServe performance. Expressive handlers: An expressive handler architecture that makes it trivial to support inferencing for your use case with many supported out of the box. Out-of-box support for system-level metrics with Prometheus exports, custom metrics and PyTorch profiler support. -
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exchange-core
Ultra-fast matching engine written in Java based on LMAX Disruptor
Exchange-core is an open-source market exchange core based on LMAX Disruptor, Eclipse Collections (ex. Goldman Sachs GS Collections), Real Logic Agrona, OpenHFT Chronicle-Wire, LZ4 Java, and Adaptive Radix Trees. Designed for high scalability and pauseless 24/7 operation under high-load conditions and providing low-latency responses. Single order book configuration is capable to process 5M operations per second on 10-years old hardware (Intel® Xeon® X5690) with moderate latency degradation. HFT optimized. Priority is a limit-order-move operation mean latency (currently ~0.5µs). Cancel operation takes ~0.7µs, placing new order ~1.0µs. Disk journaling and journal replay support, state snapshots (serialization) and restore operations, LZ4 compression. Lock-free and contention-free order matching and risk control algorithms. Matching engine and risk control operations are atomic and deterministic. -
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MEKA
A Multi-label Extension to Weka
Multi-label classifiers and evaluation procedures using the Weka machine learning framework. -
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sgmweka
Weka wrapper for the SGM toolkit for text classification and modeling.
Weka wrapper for the SGM toolkit for text classification and modeling. Provides Sparse Generative Models for scalable and accurate text classification and modeling for use in high-speed and large-scale text mining. Has lower time complexity of classification than comparable software due to inference based on sparse model representation and use of an inverted index. The provided .zip file is in the Weka package format, giving access to text classification. Other functions are usable through either Java command-line commands or class inclusion into Java projects. -
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jMIR
Music research software
jMIR is an open-source software suite implemented in Java for use in music information retrieval (MIR) research. It can be used to study music in the form of audio recordings, symbolic encodings and lyrical transcriptions, and can also mine cultural information from the Internet. It also includes tools for managing and profiling large music collections and for checking audio for production errors. jMIR includes software for extracting features, applying machine learning algorithms, applying heuristic error error checkers, mining metadata and analyzing metadata. -
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JCLAL
A Java Class Library for Active Learning
JCLAL is a general purpose framework developed in Java for the active learning research area. JCLAL framework is open source software and it is distributed under the GNU general public license. It is constructed with a high-level software environment, with a strong object oriented design and use of design patterns, which allow to the developers reuse, modify and extend the framework. If you want to refer to JCLAL in a publication, please cite the following JMLR paper. The full citation is: Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura. JCLAL: A Java Framework for Active Learning. Journal of Machine Learning Research, 17(95):1-5, 2016. -
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BagaturChess
Java Chess Engine
This is UCI Chess Engine writen in Java. Since version 1.4 (inclusive) the project was moved to https://github.com/bagaturchess/Bagatur -
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openModeller is a complete C++ framework for species potential distribution modelling. The project also includes a graphical user interface, a web service interface and an API for Python.
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Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. It's engine derived's from the Java Neural Network Framework - Neuroph and as such it can be used as a standalone project or a Neuroph plug in.
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DSTK - DataScience ToolKit
DSTK - DataScience ToolKit for All of Us
DSTK - DataScience ToolKit is an opensource free software for statistical analysis, data visualization, text analysis, and predictive analytics. Newer version and smaller file size can be found at: https://sourceforge.net/projects/dstk3/ It is designed to be straight forward and easy to use, and familar to SPSS user. While JASP offers more statistical features, DSTK tends to be a broad solution workbench, including text analysis and predictive analytics features. Of course you may specify JASP for advanced data editing and RapidMiner for advanced prediction modeling. DSTK is written in C#, Java and Python to interface with R, NLTK, and Weka. It can be expanded with plugins using R Scripts. We have also created plugins for more statistical functions, and Big Data Analytics with Microsoft Azure HDInsights (Spark Server) with Livy. License: R, RStudio, NLTK, SciPy, SKLearn, MatPlotLib, Weka, ... each has their own licenses. -
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ADAMS
ADAMS is a workflow engine for building complex knowledge workflows.
ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA) and image processing (ImageJ, JAI, BoofCV, LIRE and Gnuplot). R available using Rserve. WEKA webservice allows other frameworks to use WEKA models. Fast prototyping with Groovy and Jython. Read/write support for various databases and spreadsheet applications. -
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Open Pandora's Box
Pandora is an artificial intelligent web based bot
Pandora is an artificial intelligent web based bot written in Java. Pandora is a component based AI architecture including, database memory, XML, voice, voice rec, chat, IRC, HTTP, Wiktionary, Freebase, consciousness, language, GUI, applet, web, jsp, Android -
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android-activity-miner
Activity-Miner for Android
A mobile application to create accelerometer based activity recognition models directly on the phone. The configuration of the segmentation and feature extraction process chain requires expert knownledge. The prototype was developed in 2012 in a bachelor thesis at the University of Kassel and was optimized and enhanced for an experiment in 2015. -
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Moara is a biological text mining tool and consists of a Java library and some auxiliary MySQL databases for gene/protein training and extraction of mentions and its further normalization and disambiguation.
