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Sinopsis
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
Tanmoy Hazra: Tanmoy Hazra has currently serving as an Assistant Professor in the Department of Artificial Intelligence at Sardar Vallbhbhai Natinal Institute of Technology Surat. He held a PhD in applications of game theory from the Department of Computer Science and Engineering of the Defence Institute of Advanced Technology (DIAT-DRDO). He has almost six years of teaching experience. He published several research articles in international journals, international conferences and one research oriented book with Lambert Academic Publishing. He also received the Best Paper Award in 3rd ICACCS conference (IEEE). He is also serving as reviewer for several reputed journals and is also TPC member of various international conferences. His research area includes applications of game theory, machine learning, deep learning etc.
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.
De la contraportada
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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Applications of Game Theory in Deep Learning (SpringerBriefs in Computer Science)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Applications of Game Theory in Deep Learning
Publicado por
Springer Nature Switzerland, Springer Nature Switzerland Mär 2024, 2024
ISBN 10: 3031546520
ISBN 13: 9783031546525
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 96 pp. Englisch. Nº de ref. del artículo: 9783031546525
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Applications of Game Theory in Deep Learning
Publicado por
Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 3031546520
ISBN 13: 9783031546525
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783031546525
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Applications of Game Theory in Deep Learning (SpringerBriefs in Computer Science)
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Applications of Game Theory in Deep Learning
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ISBN 10: 3031546520
ISBN 13: 9783031546525
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 . Nº de ref. del artículo: 1362013418
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Applications of Game Theory in Deep Learning
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