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Introduction to transfer learningalgorithms and practice /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introduction to transfer learningby Jindong Wang, Yiqiang Chen.
其他題名:	algorithms and practice /
作者:	Wang, Jindong.
其他作者:	Chen, Yiqiang.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xxi, 329 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Transfer learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-981-19-7584-4
ISBN:	9789811975844$q(electronic bk.)

Introduction to transfer learningalgorithms and practice /
Wang, Jindong.

Introduction to transfer learningalgorithms and practice /[electronic resource] :by Jindong Wang, Yiqiang Chen. - Singapore :Springer Nature Singapore :2023. - xxi, 329 p. :ill., digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

Part I. Foundations of Transfer Learning -- Chapter 1. Introduction -- Chapter 2. From Machine Learning to Transfer Learning -- Chapter 3. Overview of Transfer Learning Algorithms -- Chapter 4. Instance Weighting Methods -- Chapter 5. Statistical Feature Transformation Methods -- Chapter 6. Geometrical Feature Transformation Methods -- Chapter 7. Theory, Evaluation, and Model Selection -- Part II. Modern Transfer Leaning -- Chapter 8. Pre-training and Fine-tuning -- Chapter 9. Deep Transfer Learning -- Chapter 10. Adversarial Transfer Learning -- Chapter 11. Generalization in Transfer Learning -- Chapter 12. Safe & Robust Transfer Learning -- Chapter 13. Transfer Learning in Complex Environments -- Chapter 14. Low-resource Learning -- Part III. Applications -- Chapter 15. Transfer Learning for Computer Vision -- Chapter 16. Transfer Learning for Natural language Processing -- Chapter 17. Transfer Learning for Speech Recognition -- Chapter 18. Transfer Learning for Activity Recognition -- Chapter 19. Federated Learning for Personalized Healthcare -- Chapter 20. Concluding Remarks.

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.

ISBN: 9789811975844$q(electronic bk.)

Standard No.: 10.1007/978-981-19-7584-4doiSubjects--Topical Terms:

949877
Transfer learning (Machine learning)

LC Class. No.: Q325.787

Dewey Class. No.: 006.31

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520 $a 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.
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