Keras overview and purpose

Summary

Keras is an open-source Python library for building and training artificial neural networks. It was designed to make deep learning fast to experiment with by emphasizing user-friendliness, modular design, and extensibility. Keras originally started as an independent library, was later integrated into TensorFlow, and has since expanded to support additional deep-learning backends. Keras 3 is described as a full rewrite that can be used as a low-level cross-framework interface for developing custom components such as layers, models, and metrics. It supports native workflows across multiple ecosystems (notably JAX, TensorFlow, and PyTorch) and is intended to be the default Keras version for TensorFlow 2.16 and later, while still allowing Keras 2 to be used when needed. Overall, Keras provides many ready-made neural-network building blocks and tools to simplify common tasks in deep learning, including support for convolutional and recurrent networks and utilities for training and data handling.

Key Points

• 1

  Keras provides a Python interface for creating and training neural networks, focused on being user-friendly and modular.

• 2

  Keras 3 enables cross-framework development with one codebase, supporting backends such as TensorFlow, JAX, and PyTorch (and more).

• 3

  Keras includes implementations of common deep-learning components (layers, objectives, activations, optimizers) and tools for image/text workflows, plus utilities like dropout and batch normalization.