GPT Meets Game Theory
Training and Optimizing Generative AI Models
Seiten
2026
CRC Press (Verlag)
978-1-041-12409-2 (ISBN)
CRC Press (Verlag)
978-1-041-12409-2 (ISBN)
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This book explores a new way to understand and employ neural networks through the lens of game theory. This is an illuminating read for computer science, engineering, and mathematics researchers interested in the mathematical underpinnings of deep learning models.
This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today’s most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way.
As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to “mean-field” models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved.
This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.
This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today’s most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way.
As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to “mean-field” models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved.
This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.
Hamidou Tembine is a professor of machine intelligence at the University of Quebec in Trois-Rivieres, Canada, and the co-founder of Timadie, which is a platform of platforms that brings together companies, laboratories, and professional associations.
1. Deep Learning Meets Game Theory 2. Mathematics of Transformers 3. Extremely Large Transformers 4. Mean-Field-Type Transformers 5. Mean-Field-Type Learning 6. Strategic Deep Learning
| Erscheint lt. Verlag | 11.3.2026 |
|---|---|
| Zusatzinfo | 28 Tables, black and white; 50 Line drawings, black and white; 50 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Analysis | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| ISBN-10 | 1-041-12409-0 / 1041124090 |
| ISBN-13 | 978-1-041-12409-2 / 9781041124092 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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