Summary

In this chapter, we saw various self-supervised strategies for leveraging data to learn the data distribution in the form of specialized embedding spaces, which in turn can be used for solving downstream tasks. We have looked at self-prediction, contrastive learning, and pretext tasks as specific approaches for self-supervision.

In the next chapter, we will look at reinforcement learning, an approach that uses rewards as a feedback mechanism to train models for specific tasks.