Transfer Learning

Transfer learning leverages knowledge gained from solving one problem and applies it to a related problem. Instead of training from scratch, you start with a pre-trained model and adapt it. This dramatically reduces training time, data requirements, and computational costs. It is the principle behind fine-tuning and the foundation model paradigm.