MAML is pretty good at finding the optimal initial parameter, right? Now, we will see how can we use MAML in the supervised learning setting. Before going ahead, let's quickly define our loss functions. Loss function can be any function according to the task we are performing.
If we are performing regression, then we can use our loss function as a mean squared error:
If it is a classification task, then we can use a loss function such as cross-entropy loss:
Now let's see step-by-step, exactly how MAML is used in ...
