03479nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001700167072002300184072001600207100007600223245013800299250001800437264006900455300006200524336002600586337002600612338003600638347002400674490005100698505023600749520141500985650002202400650001702422650002902439650002202468650001702490650002902507700007802536700007602614700007802690710003402768773002602802776003602828776003602864830005102900856004602951978-3-031-54653-2DE-He21320251030111729.0cr nn 008mamaa240319s2024    sz |    s    |||| 0|eng d  a97830315465327 a10.1007/978-3-031-54653-22doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aHazra, Tanmoy.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aApplications of Game Theory in Deep Learningh[electronic resource] /cby Tanmoy Hazra, Kushal Anjaria, Aditi Bajpai, Akshara Kumari.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXII, 84 p. 8 illus., 4 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Computer Science,x2191-57760 a1. Introduction -- 2. Cooperative Game Theory -- 3. Noncooperative Game Theory -- 4. Applications of Game Theory in Deep Neural Networks -- 5. Case Studies and Different Applications -- 6. Conclusion and Future Research Directions.  aThis 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. 0aMachine learning. 0aGame theory. 0aArtificial intelligence.14aMachine Learning.24aGame Theory.24aArtificial Intelligence.1 aAnjaria, Kushal.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aBajpai, Aditi.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aKumari, Akshara.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303154652508iPrinted edition:z9783031546549 0aSpringerBriefs in Computer Science,x2191-577640uhttps://doi.org/10.1007/978-3-031-54653-2