Roozbeh Razavi-Far - Böcker
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4 produkter
4 produkter
Del 217 - Intelligent Systems Reference Library
Generative Adversarial Learning: Architectures and Applications
Inbunden, Engelska, 2022
1 954 kr
Skickas inom 10-15 vardagar
This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life.
Del 217 - Intelligent Systems Reference Library
Generative Adversarial Learning: Architectures and Applications
Häftad, Engelska, 2023
1 954 kr
Skickas inom 10-15 vardagar
This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life.
Del 27 - Adaptation, Learning, and Optimization
Federated and Transfer Learning
Inbunden, Engelska, 2022
1 738 kr
Skickas inom 10-15 vardagar
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning.
Del 27 - Adaptation, Learning, and Optimization
Federated and Transfer Learning
Häftad, Engelska, 2023
1 738 kr
Skickas inom 10-15 vardagar
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.