Meta learning is one of the most promising and trending research areas in the field of artificial intelligence right now. It is believed to be a stepping stone for attaining Artificial General Intelligence (AGI). In this chapter, we will learn about what meta learning is and why meta learning is the most exhilarating research in artificial intelligence right now. We will understand what is few-shot, one-shot, and zero-shot learning and how it is used in meta learning. We will also learn about different types of meta learning techniques. We will then explore the concept of learning to learn gradient descent by gradient descent where we understand how we can learn the gradient descent optimization using the meta learner. Going ahead, we will also learn about optimization as a model for few-shot learning where we will see how we can use meta learner as an optimization algorithm in the few-shot learning setting.

In this chapter, you will learn about the following:

• Meta learning
• Meta learning and few-shot
• Types of meta learning
• Learning to learn gradient descent by gradient descent
• Optimization as a model for few-shot learning