Our great sponsors
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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
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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.
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Mergify
Automate your Pull Request with Mergify. Mergify is the most powerful merge queue tool that offers speculative checks, batch merges, and multiple queueing options. Coordinated with any CI and fully integrated to GitHub. Get started or request a demo!
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awesome-java
A curated list of awesome frameworks, libraries and software for the Java programming language.
Or more recently Tribuo or others.
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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++.
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I think DJL also use use it for their tutorials - https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html.