Explore transfer learning in deep learning with Hugo Larochelle's 44-minute conference talk from KDD2020. Delve into the importance of transfer learning, few-shot learning, and modern fine-tuning techniques. Examine prototypical networks, model-agnostic meta-learning, and the concept of training a fine-tuning procedure. Evaluate the meta-dataset approach and investigate universal representations and their transformers. Gain insights into the latest results in the field and discuss remaining challenges in transfer learning for deep neural networks.