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Applications of Game Theory in Deep Learning

Applications of Game Theory in Deep Learning

Paperback

Series: Springerbriefs in Computer Science

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3031546520
ISBN13: 9783031546525
Publisher: Springer
Published: Mar 20 2024
Pages: 84
Weight: 0.33
Height: 0.20 Width: 6.14 Depth: 9.21
Language: English

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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General Computers