Here are some key features of Keras :

Modularity and Flexibility : Keras allows you to build neural network models by stacking modular building blocks called layers. These layers can be easily added, removed, or configured according to your requirements. This modular design facilitates the construction of complex neural network architectures with minimal code overhead.

User-Friendly API : Keras provides a simple and intuitive API that abstracts away the complexities of deep learning, making it easy for users to create, train, and evaluate deep learning models without needing an in-depth understanding of the underlying mathematics or implementation details.

Support for Multiple Backends : Keras offers support for multiple backends, including TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano. This allows users to choose the backend that best suits their requirements while still benefiting from Keras' high-level API and ease of use.

Extensibility and Customization : Keras is designed to be highly extensible, allowing users to easily create custom layers, loss functions, and metrics. This enables you to experiment with novel ideas, implement cutting-edge research papers, or adapt Keras to your specific use case with minimal effort.
Built-in Models and Utilities : Keras comes with a wide range of pre-built models, including popular architectures like VGG, ResNet, and Inception, as well as utility functions for common tasks such as data preprocessing, image augmentation, and model evaluation. These built-in models and utilities help speed up development and experimentation, especially for beginners.

Integration with TensorFlow : Starting from TensorFlow 2.0, Keras has been integrated into TensorFlow as its official high-level API. This integration provides seamless interoperability between Keras and TensorFlow, allowing users to leverage the advanced features and scalability of TensorFlow while still benefiting from Keras' simplicity and ease of use.

Community and Documentation : Keras has a large and active community of developers, researchers, and enthusiasts who contribute to its development, share tutorials and examples, and provide support through forums, mailing lists, and other channels. Additionally, Keras provides comprehensive documentation with detailed explanations, examples, and API references, making it easy to get started and learn more about deep learning with Keras.