Keras is a high-level deep learning framework that provides a user-friendly interface for building and training neural networks. Here are five key points about Keras:

1. User-Friendly API: Keras offers a user-friendly and intuitive API that simplifies the process of building, training, and evaluating deep learning models. Its high-level interface allows users to define neural network architectures using a modular approach, making it easy to add layers, specify activation functions, and configure network parameters.

2. Neural Network Abstraction: Keras provides a powerful abstraction for defining neural networks. It allows users to construct models using either the Sequential API, which is suitable for linear stack models, or the Functional API, which supports more complex architectures with multiple inputs and outputs. This flexibility enables the creation of a wide range of network structures.

3. Broad Model and Layer Support: Keras supports a wide range of pre-defined neural network layers, including fully connected layers, convolutional layers, recurrent layers, and more. It also allows users to create custom layers, making it highly adaptable to different types of deep learning tasks. Keras provides a rich ecosystem of pre-trained models that can be readily used or fine-tuned for specific applications.

4. Backend Flexibility: Keras is designed to be backend-agnostic, meaning it can seamlessly integrate with different deep learning frameworks such as TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). This allows users to choose their preferred backend for computation, taking advantage of the specific features and optimizations provided by each framework.

5. Easy Experimentation and Prototyping: Keras provides a rapid prototyping environment that enables quick experimentation with different network architectures and hyperparameters. Its modular design allows for easy model iteration and exploration of various configurations, facilitating the process of model selection and optimization.