Keras

Keras is the landmark deep learning library “for humans,” with more than 64,000 stars on GitHub and 306 trending appearances — one of the most influential open-source AI projects ever.

Overview

Keras is a high-level, multi-backend deep learning framework designed to make neural networks approachable without giving up power. Its modern incarnation, Keras 3, runs on top of JAX, TensorFlow or PyTorch, so the same model code trains on whichever backend suits your hardware. The API is deliberately progressive: beginners start with the simple Sequential and Functional styles, while advanced users can subclass models, write custom training loops and drop to backend operations whenever they need control. Keras covers the full workflow from data preprocessing and modeling to distributed training and deployment, and it powers everything from classroom exercises to large-scale production systems. Maintained by the Keras team at Google and a broad community, it was actively developed in October 2026 under the Apache License 2.0.

Keras also became the friendly face of TensorFlow for years, and its design philosophy — human-centered APIs, progressive disclosure of complexity — influenced nearly every deep learning library that followed. Today Keras Hub (formerly KerasNLP and KerasCV) extends the same experience to pretrained transformers and vision models, so fine-tuning a state-of-the-art model can start from three lines of code.

Key Features

• Multi-backend by design – one API over JAX, TensorFlow and PyTorch.
• Progressive complexity – start simple, then customize layers, losses and training loops as needed.
• Rich model zoo – Keras Applications and Keras Hub ship pretrained vision and language models.
• Full workflow coverage – preprocessing layers, callbacks, distributed training and export.
• Ecosystem glue – interoperates with the TensorFlow, JAX and PyTorch tooling around each backend.
• Legendary documentation – guides and examples that taught a generation of practitioners.

💳 Pricing

Keras is completely free and open source under the Apache License 2.0, for research and commercial use alike.

Who Is It For

Students, researchers and ML engineers who want a humane, readable deep learning API that scales from a first neural network to distributed production training.