What are the main features of the Keras library?
Keras is a high-level neural network API used for building and training deep learning models, with the following key features:
- Easy to use: Keras offers a simple and intuitive API that makes it easy and fast to create, adjust, and train models.
- Modularity: Keras is organized in a modular way, allowing for easy combinations of different modules to build various types of neural network models.
- Keras supports multiple backends, allowing users to choose the most suitable one for their preferences, including TensorFlow, Theano, and CNTK.
- Scalability: Keras offers a variety of tools and interfaces that support custom layers, loss functions, optimizers, and evaluation metrics, allowing users to customize the construction and training of models.
- Parallel computing support: Keras allows for parallel computing on multiple GPUs to accelerate the training process of models.
- Extensive documentation and community support: Keras offers detailed documentation and examples, along with an active community where users can exchange and share experiences.
- Cross-platform: Keras can run on multiple operating systems such as Windows, Linux, and macOS, making it convenient for users to utilize in various environments.
- High performance: Keras utilizes efficient numerical computation libraries internally, providing high performance and computational efficiency.
