Keras

Accelerate machine learning development with a flexible, high-level API that supports multiple backend frameworks and scales to large industrial applications.
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Keras is an API for deep learning that's designed to make it faster and easier to create machine learning models. It's optimized for speed of debugging, code quality and maintainability, which means it's a good choice for developers who want to get machine learning-powered apps out the door fast.

Keras has a number of features that make it stand out from other deep learning frameworks. Its API is designed to be consistent and easy to use, requiring fewer actions from the developer for common use cases and offering helpful and actionable error messages. That means less cognitive load so developers can concentrate on the important parts of their project instead of getting lost in the weeds of implementation details.

One of the biggest benefits of Keras is that it can run on multiple backend frameworks, including JAX, TensorFlow and PyTorch. That means developers can take advantage of the strengths of each framework and know their models will run on a variety of surfaces, including servers, phones, browsers and embedded systems.

Keras is also designed to scale to large industrial applications. It can handle exascale machine learning, meaning it can handle enormous amounts of data and run on thousands of GPUs or entire TPU pods. That's why it's popular at places like CERN, NASA and NIH, where high-performance computing is critical.

The framework also includes optional high-level convenience features to accelerate experimentation cycles, which is why it's popular among researchers and scientists. And Keras has abundant documentation and tutorials, including more than 150 code examples in notebooks, to help you get started and get the most out of its abilities.

Keras can be used for a variety of tasks, including computer vision, natural language processing and generative AI. Its high-level design and intuitive APIs make it accessible to developers of all levels, from beginners to machine learning engineers.

By offering a clear, consistent and very flexible API, Keras makes it easier to develop and deploy machine learning models. Whether you're creating custom models from scratch or using pre-trained models, Keras is a powerful and efficient option for a broad range of deep learning tasks.

To get started with Keras, you can install it with pip (pip install keras) and then select a backend framework (JAX, TensorFlow, or PyTorch) to use. The official documentation includes detailed guides and tutorials to help you get started and learn more about what Keras can do.

Published on June 24, 2024

Related Questions

I'm looking for a deep learning framework that's easy to use and allows me to focus on my project's logic rather than implementation details. Can you suggest a machine learning API that supports multiple backend frameworks and can run on various surfaces, including servers and mobile devices? I need a solution that can handle large-scale industrial machine learning applications with enormous amounts of data. Is there a framework that provides high-level convenience features to accelerate experimentation cycles for researchers and scientists?

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