A cookbook for neural nets on the JVM

What it does

This repository holds a collection of Maven projects demonstrating the Eclipse Deeplearning4J ecosystem. It covers the full pipeline: loading raw data with DataVec, building networks with the high-level DL4J API or the lower-level SameDiff graph framework, and running training on anything from a single CPU to an Apache Spark cluster or Android handset. It is essentially a set of runnable blueprints for deep learning on the JVM without writing Python glue.

The interesting bit

The breadth is unusual for a single examples repo. You get Keras and TensorFlow model imports, multi-GPU CUDA examples, reinforcement learning with RL4J, and even an Android project, all backed by a C++ linear algebra core accessed through JavaCPP. It is a rare attempt to treat the JVM as a first-class deep learning environment rather than a wrapper around PyTorch or TensorFlow.

Key highlights

• End-to-end examples from raw data ingestion to model training, organized into “quickstart” and “advanced” tracks
• Keras h5 and TensorFlow frozen .pb import examples, plus transfer learning APIs
• Distributed training on Apache Spark using a hybrid asynchronous SGD approach
• SameDiff graph-mode API for custom layers and automatic differentiation
• Hardware coverage spanning x86 AVX2/512, CUDA GPUs, ARM, and PowerPC

Caveats

• GitHub issues are explicitly not monitored; support happens on a separate community forum
• Several modules were removed after version 1.0.0-beta7 as the framework changed direction, so older tutorials may be broken
• This is a collection of examples and templates, not the core DL4J framework itself

Verdict

Worth bookmarking if you are committed to the JVM for production machine learning, or need to bridge Java data pipelines with neural networks. If you are already happy in Python, this is mostly curiosity reading.