In this chapter, we have learned how matching networks and relation networks are used in few-shot learning. We saw how a relation network learns the embeddings of the support and query sets and combines the embeddings and feeds them to the relation function to compute the relation score. We also saw how a matching network uses two different embedding functions to learn the embeddings of our support and query sets and how it predicts the class of the query set.

In the next chapter, we will learn how neural Turing machines and memory-augmented neural networks work by storing and retrieving information from the memory.