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Keras is a high-level neural networks API that runs on top of TensorFlow, CNTK, or Theano. It is designed to enable fast experimentation with deep neural networks and supports both convolutional networks and recurrent networks, as well as their combinations.
Key points about Keras in the context of deep learning:
1. Abstraction Layer: Keras provides a user-friendly and intuitive abstraction layer on top of lower-level deep learning libraries like TensorFlow, CNTK, or Theano. This allows developers to focus on building and training models without getting bogged down in the implementation details.
2. Model APIs: Keras offers two main model APIs: the Sequential model and the Functional API. The Sequential model is a linear stack of layers, while the Functional API allows for the creation of more complex model architectures.
3. Modularity: Keras is designed to be highly modular. It provides a wide range of pre-built layers, optimizers, loss functions, and other utilities that can be easily combined to create complex deep learning models.
4. Ease of Use: Keras is known for its simplicity and ease of use, making it a popular choice for both beginners and experienced deep learning practitioners. The clean and concise syntax allows for rapid prototyping and iteration.
5. Cross-platform Compatibility: Keras runs on top of several deep learning backends, including TensorFlow, CNTK, and Theano. This allows for portability and flexibility in choosing the most suitable backend for a given project.
6. Flexible and Extensible: While Keras provides a high-level abstraction, it is also flexible and extensible. Developers can easily create custom layers, models, and other components to fit their specific needs.
7. Rapid Experimentation: Keras's focus on simplicity and modularity enables quick experimentation and iteration, which is crucial in the iterative process of building and refining deep learning models.
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