keras

Originally developed as an independent library by François Chollet, Keras became the official high-level API for TensorFlow 2.0 and has now evolved into Keras 3 (multi-backend Keras) that runs on top of TensorFlow, PyTorch, and JAXproviding a single high-level interface that works across all three major deep learning frameworks.

Keras's defining strength is its balance between ease of use and flexibility: the Sequential API allows building linear stacks of layers in a few lines of code, the Functional API enables complex multi-input, multi-output architectures through a graph composition model, and the Model subclassing API provides full flexibility for custom architectures and training loops.

Built-in training infrastructure (fit, evaluate, predict) handles batching, callbacks, metrics tracking, and distributed training automatically, while remaining extensible through custom layers, losses, metrics, and callbacks.

Practitioners at all experience levels use Kerasbeginners appreciate its readable syntax and immediate productivity, while experienced researchers use it for rapid prototyping even when they need custom training loops.

Its multi-backend support in Keras 3 is particularly significant: teams can prototype in one framework and deploy in another, or choose the backend based on specific hardware or ecosystem requirements without rewriting model code.

The extensive documentation, tutorials, and examples at keras.io make it one of the most accessible entry points to deep learning for the Python developer community.