If it gets better w age, will java become compatible for machine learning and data science?

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/java

Our great sponsors
  • ONLYOFFICE ONLYOFFICE Docs — document collaboration in your environment
  • InfluxDB - Access the most powerful time series database as a service
  • Sonar - Write Clean Java Code. Always.
  • Deeplearning4j

    Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.

    On top of this several popular projects have been built. This includes tensorflow-java and our project eclipse deeplearning4j: https://github.com/eclipse/deeplearning4j

  • Tribuo

    Tribuo - A Java machine learning library

    The IJava notebook kernel works pretty well for data science on top of Java. We use it in Tribuo to write all our tutorials, and if you've got the jar file in the right folder everything is runnable. For example, this is our intro classification tutorial - https://github.com/oracle/tribuo/blob/main/tutorials/irises-tribuo-v4.ipynb.


    ONLYOFFICE Docs — document collaboration in your environment. Powerful document editing and collaboration in your app or environment. Ultimate security, API and 30+ ready connectors, SaaS or on-premises

  • awesome-java

    A curated list of awesome frameworks, libraries and software for the Java programming language.

    Or more recently Tribuo or others.

  • JavaCPP

    The missing bridge between Java and native C++

    As for our approach, we maintain a library called javacpp: https://github.com/bytedeco/javacpp which proves a python wheel like experience where we distribute natively optimized c/c++ code (and even cuda accelerated code) as jar files on maven central. We also are able to develop with a python like experience by passing pointers around and other low level constructs directly allowing optimizations that you typically only get in c/c++.

  • Deep Java Library (DJL)

    An Engine-Agnostic Deep Learning Framework in Java

    I think DJL also use use it for their tutorials - https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html.

  • InfluxDB

    Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts