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"Deep Dive into Deep Learning Frameworks: A Mathematical Exploration" offers an in-depth exploration of the fundamental mathematical concepts that power contemporary deep learning methodologies. This book serves as a comprehensive guide for researchers, practitioners, and enthusiasts seeking to understand the intricate mathematical underpinnings of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced models like generative adversarial networks (GANs) and autoencoders.