Exploring the Role of Mathematics in Deep Learning A Comprehensive Review

Author(s): Thejaswi Nandyala, Sailaja M, Kalpana, Saranya C, Dr. Sivagami and Manoshankari

Abstract:

Deep learning, a subfield of machine learning, has revolutionized various domains with its exceptional capabilities in learning intricate patterns from vast amounts of data. At its core lies a foundation deeply rooted in mathematical principles, encompassing concepts from linear algebra, calculus, probability theory, optimization, and more. This review paper delves into the fundamental mathematical underpinnings of deep learning, elucidating how mathematical frameworks enable the design, training, and interpretation of deep neural networks. Beginning with an overview of the mathematical prerequisites, we explore key concepts such as gradient descent, backpropagation, activation functions, convolutional operations, and recurrent networks, elucidating their mathematical formulations and significance in deep learning. Furthermore, we investigate recent advancements at the intersection of mathematics and deep learning, including graph neural networks, attention mechanisms, and reinforcement learning. Throughout the review, we highlight the role of mathematics in shaping the theoretical understanding, practical implementations, and ongoing research directions in deep learning. By providing a comprehensive synthesis of the mathematical foundations of deep learning, this paper serves as a valuable resource for researchers, practitioners, and enthusiasts seeking to deepen their understanding of this transformative field.

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Pages: 52-55     |    2 View     |    0 Download