Hands-On Meta Learning with Python

Learning the optimizer

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.