Since CNNs are different from the layering architecture's perspective, they have different requirements as well as tuning criteria. How do you know what combination of hyperparameters is the best for your task? Of course, you can use a grid search with cross-validation to find the right hyperparameters for linear machine learning models.

However, for CNNs, there are many hyperparameters to tune, and since training a neural network on a large dataset takes a lot of time, you will only be able to explore a tiny part of the hyperparameter space in a reasonable amount of time. Here are some insights that can be followed.