Transfer Learning and Fine Tuning Questions

Understanding when and how to leverage pre trained models and representations to speed development and reduce labeled data needs. Topics include domain adaptation, fine tuning strategies such as linear probing and full fine tuning, parameter efficient approaches such as adapter modules and low rank adaptation, few shot learning techniques, mitigating catastrophic forgetting, diagnosing transferability mismatch, hyperparameter considerations for fine tuning, and production implications such as model size, inference cost, licensing, and privacy.