Author: Awais Farooq

Keras models can be easily exported to TensorFlow Hub for sharing and reuse, and they can also be converted to run in web browsers using TensorFlow.js.

Keras has a strong community with active contributors and extensive documentation, including tutorials and examples that help users get started with deep learning quickly.

Keras provides utilities for data preprocessing and data augmentation, which are essential for training deep learning models on image, text, and sequence data.

Keras is widely adopted in both industry and academia due to its simplicity, flexibility, and powerful capabilities.

New layers, loss functions, and metrics can be easily added to Keras, either by creating subclasses of existing classes or by writing them from scratch as functions.

Keras models are made up of building blocks called layers, which can be easily connected together to create complex neural network architectures.

TensorFlow adopted Keras as its official high-level API starting from TensorFlow 2.0, making Keras a part of the TensorFlow ecosystem.

Initially built on top of Theano and TensorFlow, Keras has evolved to support multiple backends, including TensorFlow 2.x, Theano (deprecated), and Microsoft Cognitive Toolkit (CNTK).

Keras focuses on user experience, ease of use, and modularity. It’s designed to enable fast experimentation with deep neural networks.

Keras is a high-level neural networks API, originally developed by François Chollet, that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). It allows for easy and fast prototyping of deep learning models.