An alternative to low-level frameworks like raw TensorFlow or PyTorch. It provides a consistent, human-centric interface for building neural networks across multiple backends.
Tech Stack
PythonAI, ML & Data
Why keras?
• User-friendly API
• Multi-backend compatibility
• Extensive documentation
Limitations
• Less control over low-level ops
• Debugging can be abstract
• Overhead on small models
3/6/2026
Last Update
19,714
Forks
279
Issues
Apache-2.0
License
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