Transfer Learning

Transfer Learning involves reusing a pre-trained model on a new related problem. It leverages knowledge gained from one task to improve generalization in another, reducing the need for large amounts of data and computational resources. Methods include feature extraction, fine-tuning, and domain adaptation, making it particularly effective for scenarios with limited training data in the target domain.

Adapting image recognition models to medical imaging natural language processing tasks speech recognition domain adaptation personalized recommendations.