Applications of Game Theory in Deep Learning (Paperback)
Idioma: inglés
Editorial: Springer International Publishing AG, Cham, 2024
- Tapa blanda
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de AbeBooks desde el 12 de octubre de 2005
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Paperback. This book aims to unravel the complex tapestry that interweaves strategic decision-making models with the forefront of deep learning techniques. Applications of Game Theory in Deep Learning provides an extensive and insightful exploration of game theory in deep learning, diving deep into both the theoretical foundations and the real-world applications that showcase this intriguing intersection of fields. Starting with the essential foundations for comprehending both game theory and deep learning, delving into the individual significance of each field, the book culminates in a nuanced examination of Game Theory's pivotal role in augmenting and shaping the development of Deep Learning algorithms. By elucidating the theoretical underpinnings and practical applications of this synergistic relationship, we equip the reader with a comprehensive understanding of their combined potential. In our digital age, where algorithms and autonomous agents are becoming more common, the combination of game theory and deep learning has opened a new frontier of exploration. The combination of these two disciplines opens new and exciting avenues. We observe how artificial agents can think strategically, adapt to ever-shifting environments, and make decisions that are consistent with their goals and the dynamics of their surroundings. This book presents case studies, methodologies, and real-world applications. Applications of Game Theory in Deep Learning provides an extensive and insightful exploration of game theory in deep learning, diving deep into both the theoretical foundations and the real-world applications that showcase this intriguing intersection of fields. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de ref. del artículo 9783031546525
- Título
- Applications of Game Theory in Deep Learning (Paperback)
- Autor
- Tanmoy Hazra
- Editorial
- Springer International Publishing AG, Cham
- Año de publicación
- 2024
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 3031546520
- ISBN 13
- 9783031546525
- Serie
- Libro 52 de 103: SpringerBriefs in Computer Science
This book aims to unravel the complex tapestry that interweaves strategic decision-making models with the forefront of deep learning techniques. Applications of Game Theory in Deep Learning provides an extensive and insightful exploration of game theory in deep learning, diving deep into both the theoretical foundations and the real-world applications that showcase this intriguing intersection of fields. Starting with the essential foundations for comprehending both game theory and deep learning, delving into the individual significance of each field, the book culminates in a nuanced examination of Game Theory's pivotal role in augmenting and shaping the development of Deep Learning algorithms. By elucidating the theoretical underpinnings and practical applications of this synergistic relationship, we equip the reader with a comprehensive understanding of their combined potential. In our digital age, where algorithms and autonomous agents are becoming more common, the combination of game theory and deep learning has opened a new frontier of exploration. The combination of these two disciplines opens new and exciting avenues. We observe how artificial agents can think strategically, adapt to ever-shifting environments, and make decisions that are consistent with their goals and the dynamics of their surroundings. This book presents case studies, methodologies, and real-world applications.
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Acerca del autor
Kushal Anjaria: He is an assistant professor in the IT and Systems area at the Institute of Rural Management, Anand (IRMA). He held a PhD in information systems and security from the Department of Computer Science and Engineering of the Defence Institute of Advanced Technology (DIAT-DRDO). He has around five years of academic experience and a year of industrial experience. He teaches subjects like management information systems (MIS) and enterprise resource planning. He has authored over 25 research papers, published them in refereed journals and book chapters, and presented them at several conferences. He has served as a consultant on several water-related projects for the government and non-governmental organizations, like "Conducting a Comparative Study concerning Policy, Programs, and Schemes for Water Management in Five Tribally Significant States of Madhya Pradesh, Gujarat, Maharashtra, Jharkhand, and Chhattisgarh." He developed three case studies pertaining to loan taking patterns of the artisans in the consultancy project related to SHGs of leather artisans by discussing with three different PSBs
Aditi Bajpai: Aditi Bajpai is a passionate and dedicated professional currently serving as a Full time Research Scholar at NIT Raipur, specializing in the captivating realm of Artificial Intelligence for processors. With a profound academic journey, she earned her Master of Technology in Computer Science & Engineering with a focus on Artificial Intelligence from the prestigious Indian Institute of Information Technology, Pune, graduated in 2023. Driven by a fervent interest in advancing technology, she is actively engaged in cutting-edge research, exploring the dynamic intersection of Artificial Intelligence and processors. As an active participant in cutting-edge research, she is passionate about exploring the limitless possibilities that AI holds for the future.
Akshara Kumari: She is in the final semester of MTech programme from the prestigious Indian Institute of Information Technology, Pune, specializing in the Internet of things. She is currently working in Philips as a Research & Development Electronic Intern in Healthcare Innovation Centre, Pune, India. Her area of interest is hospital patients monitors and working in a measurement team.
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