Applications of Game Theory in Deep Learning
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 th...
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| Autors principals: | , , , |
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| Autor corporatiu: | |
| Format: | Electrònic eBook |
| Idioma: | anglès |
| Publicat: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edició: | 1st ed. 2024. |
| Col·lecció: | SpringerBriefs in Computer Science,
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| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/978-3-031-54653-2 |
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| 100 | 1 | |a Hazra, Tanmoy. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Applications of Game Theory in Deep Learning |h [electronic resource] / |c by Tanmoy Hazra, Kushal Anjaria, Aditi Bajpai, Akshara Kumari. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XII, 84 p. 8 illus.). |b 4 illus. in color. | ||
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| 505 | 0 | |a 1. 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Game theory. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Game Theory. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
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| 700 | 1 | |a Bajpai, Aditi. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Kumari, Akshara. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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