Hands-On Meta Learning with Python

Optimization as a model for few-shot learning

We know that, in few-shot learning, we learn from lesser data points, but how can we apply gradient descent in a few-shot learning setting? In a few-shot learning setting, gradient descent fails abruptly due to very few data points. Gradient descent optimization requires more data points to reach the convergence and minimize loss. So, we need a better optimization technique in the few-shot regime. Let's say we have a  model parameterized by some parameter . We initialize this parameter with some ...