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Deep Learning Frameworks can be thought of as toolkits designed to streamline the creation and implementation of deep learning models, including those based on Convolutional Neural Networks (CNNs). These frameworks offer pre-built components for tasks such as optimizing model parameters, handling large datasets, and visualizing results. They enable developers to build complex neural networks with relative ease and efficiency, while also providing opportunities for customization and experimentation. By offering a standardized set of tools and methods, deep learning frameworks help promote best practices, encourage collaboration, and reduce the barriers to entry for those interested in exploring the potential of CNNs and other deep learning models.