Introduction to Transfer Learning Algorithms and Practice /
Wang, Jindong.
Introduction to Transfer Learning Algorithms and Practice / [electronic resource] : by Jindong Wang, Yiqiang Chen. - 1st ed. 2023. - XXI, 329 p. 1 illus. in color. online resource. - Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 . - Machine Learning: Foundations, Methodologies, and Applications, .
Part I. Foundations of Transfer Learning -- Chapter 1. Introduction -- Chapter 2. From Machine Learning to Transfer Learning -- Chapter 3. Overview of Transfer Learning Algorithms -- Chapter 4. Instance Weighting Methods -- Chapter 5. Statistical Feature Transformation Methods -- Chapter 6. Geometrical Feature Transformation Methods -- Chapter 7. Theory, Evaluation, and Model Selection -- Part II. Modern Transfer Leaning -- Chapter 8. Pre-training and Fine-tuning -- Chapter 9. Deep Transfer Learning -- Chapter 10. Adversarial Transfer Learning -- Chapter 11. Generalization in Transfer Learning -- Chapter 12. Safe & Robust Transfer Learning -- Chapter 13. Transfer Learning in Complex Environments -- Chapter 14. Low-resource Learning -- Part III. Applications -- Chapter 15. Transfer Learning for Computer Vision -- Chapter 16. Transfer Learning for Natural language Processing -- Chapter 17. Transfer Learning for Speech Recognition -- Chapter 18. Transfer Learning for Activity Recognition -- Chapter 19. Federated Learning for Personalized Healthcare -- Chapter 20. Concluding Remarks.
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.
9789811975844
10.1007/978-981-19-7584-4 doi
Machine learning.
Computer science.
Computer vision.
Natural language processing (Computer science).
Machine Learning.
Theory and Algorithms for Application Domains.
Computer Vision.
Natural Language Processing (NLP).
Q325.5-.7
006.31
Introduction to Transfer Learning Algorithms and Practice / [electronic resource] : by Jindong Wang, Yiqiang Chen. - 1st ed. 2023. - XXI, 329 p. 1 illus. in color. online resource. - Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 . - Machine Learning: Foundations, Methodologies, and Applications, .
Part I. Foundations of Transfer Learning -- Chapter 1. Introduction -- Chapter 2. From Machine Learning to Transfer Learning -- Chapter 3. Overview of Transfer Learning Algorithms -- Chapter 4. Instance Weighting Methods -- Chapter 5. Statistical Feature Transformation Methods -- Chapter 6. Geometrical Feature Transformation Methods -- Chapter 7. Theory, Evaluation, and Model Selection -- Part II. Modern Transfer Leaning -- Chapter 8. Pre-training and Fine-tuning -- Chapter 9. Deep Transfer Learning -- Chapter 10. Adversarial Transfer Learning -- Chapter 11. Generalization in Transfer Learning -- Chapter 12. Safe & Robust Transfer Learning -- Chapter 13. Transfer Learning in Complex Environments -- Chapter 14. Low-resource Learning -- Part III. Applications -- Chapter 15. Transfer Learning for Computer Vision -- Chapter 16. Transfer Learning for Natural language Processing -- Chapter 17. Transfer Learning for Speech Recognition -- Chapter 18. Transfer Learning for Activity Recognition -- Chapter 19. Federated Learning for Personalized Healthcare -- Chapter 20. Concluding Remarks.
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.
9789811975844
10.1007/978-981-19-7584-4 doi
Machine learning.
Computer science.
Computer vision.
Natural language processing (Computer science).
Machine Learning.
Theory and Algorithms for Application Domains.
Computer Vision.
Natural Language Processing (NLP).
Q325.5-.7
006.31