We are getting closer with variational methods and kernel methods to achieving a more holistic framework for understanding machine learning (incl. traditional deep learning) training and inference. There is a deep unity in the fundamentals of machine learning, formed into a cohesive whole by applying the analytical techniques of statistical mechanics and Bayesian probability theory.