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Keras is a widely used open-source deep learning framework that is known for its user-friendly interface, modularity, and ease of prototyping. It is designed to enable fast experimentation with neural networks and has gained popularity in the machine learning community for its simplicity and flexibility. Keras has been integrated with popular deep learning libraries such as TensorFlow and PyTorch, allowing users to leverage their respective strengths while benefiting from Keras' high-level abstractions. Key Features of Keras: 1. User-Friendly Interface: Keras is designed to be accessible to both beginners and experienced deep learning practitioners. Its high-level APIs enable rapid model prototyping and experimentation. 2. Modularity: Keras allows for the creation of complex neural network architectures through a modular approach. Layers, objectives, activation functions, and optimizers are all modular components that can be easily combined to build custom models. 3. Compatibility: Keras has been integrated with popular deep learning libraries, including TensorFlow and PyTorch, allowing users to benefit from the strengths of these frameworks while using Keras' intuitive interface. 4. Extensibility: Users can extend Keras by creating custom layers, loss functions, and metrics, enabling the implementation of specialized neural network components. 5. Support for Convolutional and Recurrent Networks: Keras provides support for building convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in computer vision and natural language processing tasks, respectively. 6. GPU Support: Keras is compatible with GPUs, allowing for accelerated training and inference on hardware with parallel processing capabilities. 7. Community and Documentation: Keras has a strong community of users and contributors, and it is well-documented with tutorials, guides, and examples. Overall, Keras is a powerful and versatile framework for deep learning, suitable for a wide range of applications and users, from beginners to experienced researchers and practitioners."

• User-Friendly Interface, Modularity, Compatibility, Extensibility, Support for Convolutional and Recurrent Networks, GPU Support, Community and Documentation