A small change in hyperparameters can lead to large swings in performance. Even something as simple as the learning rate can be the difference between a well-performing model and one that stalls early.

The challenge is that the right settings aren’t obvious upfront, and the search space grows quickly as you add more knobs.

This chapter focuses on how to explore that space efficiently, from simple baselines to more advanced methods that significantly reduce compute cost.