Mass Personalization of Deep Learning

We discuss training techniques, objectives and metrics toward mass personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. Generally, one can observe a trade-off between performance on data most relevant to an individual and a more general, broader dataset. In some cases a model optimized for general data (only) exhibits lower test accuracy on the general dataset and the data of an individual.