We started off by understanding what meta learning is and how one-shot, few-shot, and zero-shot learning is used in meta learning. We learned that the support set and query set are more like a train set and test set but with k data points in each of the classes. We also saw what n-way k-shot means. Later, we understood different types of meta learning techniques. Then, we explored learning to learn gradient descent by gradient descent where we saw how RNN is used as an optimizer to optimize the base network. Later, we saw optimization as a model for few-shot learning where we used LSTM as a meta learner for optimizing in the few-shot learning setting.
In the next chapter, we will learn about a metric-based meta learning algorithm called the Siamese network and we will see how to use a Siamese network for performing face and audio recognition.
