Introduction to Transfer Learning - Algorithms and Practice (Hardcover, 1st ed. 2023)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Machine Learning: Foundations, Methodologies, and Applications
Release date:	February 2023
First published:	2023
Authors:	Jindong Wang • Yiqiang Chen
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	409
Edition:	1st ed. 2023
ISBN-13:	978-981-19-7583-7
Languages:	English
Subtitles:	Chinese
Categories:	Books > Computing & IT > General theory of computing > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	981-19-7583-3
Barcode:	9789811975837

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