Название: Introduction to Transfer Learning: Algorithms and PracticeАвтор: Jindong Wang, Yiqiang Chen
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2023
Страниц: 333
Язык: английский
Формат: pdf (true), epub
Размер: 50.4 MB
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.
Machine Learning is a kind of important learning methodology of Artificial Intelligence, which has gained great proliferation in the past decades. Machine Learning makes it possible to learn knowledge from the data. Transfer learning, as an important branch of Machine Learning, focuses on the process of leveraging the learned knowledge to facilitate the learning of new ability, which increases the effectiveness and efficiency.
Concretely speaking, in the field of machine learning, transfer learning can be generally defined as (informal): Transfer learning aims to solve the new problem by leveraging the similarity of data (task or models) between the old problem and the new one to perform knowledge (experience, rules, etc.) transfer.
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