Название: Advanced Mathematics in Computing Communication and Security
Автор: Dipti Jadhav, Pritam Wani, Narendrakumar Dasre, Niranjanamurthy M., Biswadip Basu Mallik
Издательство: Wiley-Scrivener
Год: 2026
Страниц: 560
Язык: английский
Формат: pdf
Размер: 24.6 MB

Explore the cutting edge of scientific computing with this volume, which provides a comprehensive look at the interdependency between mathematics and Computer Science.

Within the evolving landscape of Computer Science, mathematics is increasingly playing a pivotal role. Disciplines like linear algebra, statistics, calculus, and discrete mathematics serve as the cornerstone for comprehension and innovation within various Computer Science domains. This book underscores the deep-seated interdependency between the realms of mathematics and scientific computing, exploring how each discipline mutually reinforces and advances the other. With its rich theoretical framework and analytical rigor, mathematics provides the bedrock upon which many computational concepts and methodologies are built. In turn, Computer Science offers a practical avenue for applying mathematical abstractions to tackle real-world problems efficiently and effectively. Cutting-edge technologies, such as scientific computing, Deep Learning, and Computer Vision, require not only a mastery of foundational mathematics, but a diverse interdisciplinary approach. This book sheds light on the burgeoning frontiers of Computer Science, bringing together researchers with expertise across multiple industries, making it an essential resource for beginners and experienced practitioners alike.

In the dynamic landscape of modern technology, mathematics has become so inextricably linked with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) that one seems incomplete without the other. In this regard, the chapter begins with an explanation of how the interrelation between Mathematics and these ultra-modern areas is deep-rooted, talking about the mathematical underpinnings as central tenets to the creation of intelligent systems. The better the understanding of the mathematical frameworks underlying AI, ML, and DL, the better the stakeholders will be positioned to take advantage of the unprecedented developments happening in these domains. Thus, the comprehensive overview attempts to make somewhat more explicit the complex associations among the mathematical concepts and their manifestations within the domains of AI, ML, and DL for researchers, practitioners, and enthusiasts. Hence, it rests on the core mathematical constructs that work as the bedrock for the intentional design and optimization of intelligent algorithms. Linear algebra, using the elegance in manipulation between vectors and matrices, furnishes well-needed tools in data representation, transformation, and manipulation relating to entities. From the convolutional operations in neural networks to singular value decomposition, which is a dimensionality-reducing technique, most concepts of liner algebra underpin AI. In fact, it forms the backbone for efficient computation and modeling of complex relationships within data in AI.






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