Modern Approaches to Transfer Learning and Domain Adaptation presents advanced methods that help AI models perform reliably across varied domains and tasks. It explains core concepts, types of domain shifts, and modern techniques including fine-tuning, feature alignment, adversarial and self-supervised approaches. The book covers unsupervised, partial, open-set, and source-free adaptation with practical examples in vision, NLP, and healthcare. Ethical issues like fairness and interpretability are highlighted, making it a valuable guide for students, researchers, and professionals building adaptable AI systems.
