Explore data efficiency through transfer learning in this 50-minute conference talk by Eddie Du from Open Data Science. Learn how to apply cutting-edge academic research in transfer learning to real-world business problems, including the cold-start issue. Discover a hybrid instance-based transfer learning approach that outperforms baselines and uses probabilistic weighting to fuse information from source to target domains. Examine a framework for building differentially private aggregation approaches to transfer knowledge from existing models to new companies with limited data. Understand how these methods can increase customer trust and advance revenue. Delve into topics such as data aggregation, binary classification while preserving privacy, differential privacy in machine learning, and the effects of epsilon on performance.