Keras is a high-level neural network API written in Python, which allows users to easily build, train, and deploy deep learning models. It provides a user-friendly, modular and intuitive way of building deep learning models in Python. Keras was developed with the aim of making deep learning more accessible and user-friendly.
Keras is built on top of lower-level libraries such as TensorFlow, Theano, and CNTK, which handle the mathematical computations of the neural network. Keras provides a simple and consistent interface to these libraries, allowing users to easily design and train complex neural networks.
One of the main benefits of Keras is its ease of use. Keras provides a wide range of pre-built layers, loss functions, and optimizers, which can be easily combined to create custom models. It also provides a modular architecture, allowing users to easily add or remove layers, and connect different layers to form complex neural networks. This flexibility allows users to experiment with different model architectures, and to easily adapt models to different tasks and datasets.
Another benefit of Keras is its performance and scalability. It is designed to run on both CPUs and GPUs, and can be easily scaled up to handle large datasets and complex models. Keras also provides support for distributed computing, allowing users to train models on multiple machines simultaneously.
Keras is also a highly portable framework, which can run on a wide range of platforms, from desktops to mobile devices and the web. This makes it an ideal choice for developing and deploying deep learning models in a variety of settings.
Overall, Keras is a powerful and user-friendly deep learning framework that is ideal for developers and researchers who want to quickly prototype and build deep learning models. Its simplicity, flexibility, and performance make it a popular choice for a wide range of applications, from image and speech recognition to natural language processing and more.