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, modularity, and extensibility. Keras provides a high-level interface that helps developers work efficiently with common neural-network components and workflows. Originally, Keras supported multiple backends (such as TensorFlow, Theano, and others). Over time, it became tightly focused on TensorFlow, but starting with Keras 3 it returned to a multi-backend approach, supporting TensorFlow, JAX, and PyTorch (and also adding support for OpenVINO). Keras includes many ready-to-use building blocks—like layers, activation functions, optimizers, and loss/objective functions—and supports both standard and specialized architectures such as convolutional and recurrent neural networks, along with tools for image and text data.

Key Points

• 1

  Keras is a user-friendly, modular Python library for building and training neural networks.

• 2

  Keras 3 is a rewrite that supports multiple backends (TensorFlow, JAX, PyTorch) with one codebase.

• 3

  It provides many built-in neural-network building blocks and utilities for common deep-learning tasks and data types.