Using Keras offers several advantages, making it a popular choice for building and training deep learning models:

Simplicity : Keras provides a simple and intuitive API that abstracts away the complexities of deep learning, making it easy for both beginners and experienced developers to create and train neural networks. Its user-friendly interface allows users to quickly prototype and experiment with different architectures and ideas.

Modularity : Keras follows a modular design approach, allowing users to build neural network models by stacking modular building blocks called layers. This modularity makes it easy to construct complex architectures by simply adding or removing layers as needed.

Flexibility : Keras supports a wide range of neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. It also offers support for various types of layers, activation functions, optimizers, and loss functions, providing users with the flexibility to customize models according to their specific requirements.
Ease of use : Keras simplifies the process of building, training, and evaluating deep learning models by providing a consistent and well-documented API. Its high-level abstractions hide the low-level implementation details, allowing users to focus on the design and experimentation of neural network architectures rather than the underlying mathematics or programming complexities.

Integration with TensorFlow : Keras has been integrated into TensorFlow as its official high-level API starting from TensorFlow 2.0. 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 Ecosystem : 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 benefits from the broader TensorFlow ecosystem, which includes libraries, tools, and resources for various deep learning tasks.

Cross-platform Compatibility : Keras supports both CPU and GPU computation, allowing users to train and deploy models on a wide range of hardware platforms. It also provides support for multiple programming languages, including Python and R, making it accessible to a diverse range of users and environments.