The goal of meta-learning is to distill knowledge from training tasks (such as the initialization of a neural network or representation learner network) and apply this knowledge to the distribution of unseen test tasks. This enables meta-learning to adjust to test tasks efficiently, even when only a few samples are available. The aim is to identify the parameters that minimize the loss function over the distribution of tasks. In this formulation, one task and the corresponding dataset are considered as one data point sampled from the distribution over tasks.