This document discusses transfer learning and domain adaptation techniques for training deep learning models when limited labeled data is available. It describes using pre-trained networks to extract features, fine-tuning networks on related tasks, and unsupervised domain adaptation methods. Transfer learning can outperform training from scratch by leveraging knowledge gained from large labeled datasets.