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