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Deep Learning Frameworks encompass a variety of tools and libraries designed to streamline the process of building, training, and deploying deep learning models. These frameworks offer pre-built components and functions, making it easier for developers and researchers to implement complex neural networks without having to build everything from scratch. They often include features such as automatic differentiation, model parallelism, and distributed computing, enabling efficient utilization of hardware resources like GPUs and TPUs. By providing a standardized interface and simplifying the development process, Deep Learning Frameworks foster innovation, collaboration, and rapid prototyping in the field of artificial intelligence, specifically under the broader categories of Technology, Web Development, and Deep Learning.