Open-source, designed with speed, simplicity, and lightweight in mind.
Traditional machine learning frameworks for Java can be heavy, complex, and slow to deploy. This approach often leads to bloated applications and limits the agility developers need for modern development.
Brain4J works differently. It uses minimal external dependencies and a lightweight core architecture, ensuring fast deployment and reduced application footprint.
Processes data significantly faster than traditional Java machine learning frameworks through optimized algorithms.
Lightweight core with minimal external dependencies, ensuring fast deployment and reduced application footprint.
Intuitive API design with comprehensive documentation and examples for rapid development.
Brain4J's performance is comparable to much larger frameworks, whilst also being significantly faster and more lightweight.
| Benchmark | Brain4J | Tensorflow | Pytorch | DeepLearning4J |
|---|---|---|---|---|
| Seconds per epoch | ~0.92 | ~0.62 | ~1.11 | ~2.92 |
| Accuracy | 97.52% | 97.44% | 96.83% | 97.37% |
| Startup Time | — | — | — | — |
Meet the passionate team of developers and researchers building the future of lightweight machine learning for Java.
Lead Developer
Contributor
Web Developer
Brain4J is currently available as an open-source framework to help developers build faster, lighter ML applications. Join our community and start building today.