Introduction to Transfer Learning: Algorithms and Practice - Machine Learning: Foundations, Methodologies, and Applications - Jindong Wang - 書籍 - Springer Verlag, Singapore - 9789811975837 - 2023年3月31日
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Jindong Wang
Introduction to Transfer Learning: Algorithms and Practice - Machine Learning: Foundations, Methodologies, and Applications 2023 edition

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Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning.

This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a "student's" perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.

409 pages, 40 Tables, color; 84 Illustrations, color; 25 Illustrations, black and white; X, 409 p. 1

メディア 書籍     Hardcover Book   (ハードカバー付きの本)
リリース済み 2023年3月31日
ISBN13 9789811975837
出版社 Springer Verlag, Singapore
ページ数 329
寸法 242 × 161 × 27 mm   ·   668 g
言語 英語  

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