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Springer Federated and Transfer Learning - Adaptation, Learning, 27

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Description

Expand your technical library with Federated and Transfer Learning, published by Springer. This volume is part of the Adaptation, Learning, and Optimization series, specifically numbered as volume 27. It serves as a targeted resource for professionals and students interested in the specialized fields of computer science and artificial intelligence. As part of a recognized academic series, this book addresses complex topics within machine learning and optimization. It provides a structured approach to understanding how learning models can be adapted and optimized for various computational environments. This text is designed for those seeking deep knowledge in AI and machine learning applications. Whether you are researching advanced computing methods or studying the latest developments in data optimization, this Springer publication offers a focused look at modern algorithmic challenges. It is a valuable addition to any collection centered on computer science and internet technologies.

Key Features

Part of the professional Adaptation, Learning, and Optimization series by Springer.

Volume 27 in a specialized collection focused on advanced computing topics.

Targeted content for readers interested in AI and machine learning subjects.

Published by Springer, a recognized name in academic and technical literature.

Provides a technical foundation for studying computer science and internet technologies.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 October 2022
Listed Since
16 June 2022

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