In this method, we try to learn the optimizer. How do we generally optimize our neural network? We optimize our neural network by training on a large dataset and minimize the loss using gradient descent. But in the few-shot learning setting, gradient descent fails as we will have a smaller dataset. So, in this case, we will learn the optimizer itself. We will have two networks: a base network that actually tries to learn and a meta network that optimizes the base network. We will explore how exactly this works in the upcoming sections.