⇒ some guidelines, some tips for how to systematically organize your hyperparameter tuning process, which hopefully will make it more efficient for you to converge on a good setting of the hyperparameters.
One of the painful things about training deepness is the sheer number of hyperparameters you have to deal with
<aside> 💡 But hopefully it does give you some rough sense of what hyperparameters might be more important than others, alpha, most important, for sure, followed maybe by the ones I've circle in orange, followed maybe by the ones I circled in purple.
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So let's say in this two-dimensional example that you sample these points, and maybe you found that this point work the best and maybe a few other points around it tended to work really well, then in the coarse to fine scheme what you might do is zoom in to a smaller region of the hyperparameters, and then sample more density within this space.