Introduction to Transfer Learning: Algorithms and Practice

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.

Author: Jindong Wang,Yiqiang Chen
Binding Type: Paperback
Publisher: Springer
Published: 10/19/2024
Series: Machine Learning: Foundations, Methodologies, and Applications
Pages: 329
Weight: 1.09lbs
Size: 9.21h x 6.14w x 0.73d
ISBN: 9789811975868
2023 Edition