Introduction to Transfer Learning: Algorithms and Practice - Couverture souple

Wang, Jindong; Chen, Yiqiang

Couverture souple

ISBN 10 : 981197585X ISBN 13 : 9789811975851

Editeur : Springer, 2023

Synopsis

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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 981197585X
• ISBN 13: 9789811975851
• Reliure: Broché
• Coordonnées du fabricant: KINESIS; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; 22391; Allemagne