In this chapter, we covered two very fascinating areas within the field of deep learning—transfer learning and meta learning—both of which hold the promise of furthering the field of not only deep learning but also artificial intelligence by enabling neural networks to learn additional tasks and generalize over unseen distributions. We explored several meta learning approaches, including model-based, metric-based, and optimization-based, and explored how they differ.
In the next chapter, we will learn about geometric deep learning.
