Keras is a widely-used open-source deep learning framework written in Python. It is designed to be user-friendly, modular, and extensible, with a focus on enabling rapid experimentation and easy implementation of neural networks. Keras acts as an interface for other powerful deep learning libraries such as TensorFlow, Theano, and CNTK, providing a high-level API that simplifies the process of defining, training, and evaluating deep learning models.
The main objective of Keras is to offer a platform for beginners to start building deep learning models without requiring in-depth knowledge of the underlying mathematics and programming complexities. It allows users to define models through a simple, intuitive syntax, using a set of high-level building blocks such as layers, optimizers, and loss functions.
Keras provides a wide range of pre-built layers including convolutional layers, recurrent layers, pooling layers, and dense layers, which can be easily stacked together to construct complex neural network architectures. It also incorporates advanced features like model serialization and model checkpointing for efficient saving and loading of trained models.
The flexibility and versatility of Keras make it suitable for a variety of deep learning tasks including image classification, natural language processing, sequence generation, and more. Its ease of use and clear API design have contributed to its popularity among researchers, developers, and machine learning practitioners, making it one of the most widely adopted frameworks for building deep learning models in the industry.
