tensorflow or PI torch the mechanistic view is probably the one that makes it clearest the probabilistic view treats meta learning as the problem of extracting prior information from a set of meta training tasks that allows for efficient learning of new tasks this view says that learning a new task basically used this prior plus a small amount of training data to infer the most likely posterior parameters that will allow you to solve this task and this view of meta learning makes it easier to understand meta learning