Federated and Transfer Learning, Paperback by Razavi-far, Roozbeh (EDT); Wang…

Federated and Transfer Learning, Paperback by Razavi-far, Roozbeh (EDT); Wang…

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Federated and Transfer Learning, Paperback by Razavi-far, Roozbeh (EDT); Wang, Tian; Li, Bo; Gaur, Mudit; Li, Cheng (CON)

In the rapidly evolving field of machine learning, federated and transfer learning have become essential techniques for improving model performance and generalization across different domains. This comprehensive paperback, edited by Roozbeh Razavi-far and co-authored by Tian Wang, Bo Li, Mudit Gaur, and Cheng Li, provides a deep dive into the latest advancements in federated and transfer learning.

From the basics of federated learning, where models are trained across multiple decentralized devices, to transfer learning, which leverages knowledge from one domain to improve performance in another, this book covers it all. With practical examples, case studies, and theoretical discussions, readers can gain a thorough understanding of these cutting-edge techniques and how to apply them in their own projects.

Whether you are a seasoned machine learning practitioner or just starting out, Federated and Transfer Learning is a must-have resource for staying at the forefront of this rapidly evolving field. Pick up your copy today and take your machine learning skills to the next level!
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