Summary
In this chapter, we have discussed the first out of four groups of hyperparameter-tuning methods, called the exhaustive search group. We have discussed manual search, grid search, and random search. We not only discussed the definition of those methods, but also how those methods work at both a high level and a technical level, and what are the pros and cons for each of them. From now on, you should be able to explain these exhaustive search methods with confidence when someone asks you about them and apply all of the exhaustive search methods with high confidence in practice.
In the next chapter, we will start discussing Bayesian optimization, the second group of hyperparameter-tuning methods. The goal of the next chapter is similar to this chapter, which is to give a better understanding of methods belonging to the Bayesian optimization group so that you can utilize those methods with high confidence in practice.