To learn about the one-/few-shot learning aspects of MAML, first, we need to learn certain terms. These are similar to what we learned when matching networks:
- T: This represents various tasks—for example, we want our model to learn to identify cats, dogs, horses, and so on, and
represents a training model to identify cats, for example. Here, 
. - P(T): This represents the probabilistic distribution across all tasks. Our aim is to learn P(T) through MAML.
- L(T): This represents the loss function generated by task, T, data points. For classification, ...
