[uoaei]

Not ill-founded so much as jumping the gun.

To understand why neural networks work, you will have to understand how a whole host of smaller, simpler ML models work in excruciating detail. Multiple linear regression, logistic regression, etc. What they mean, how they work, what's really going on "inside", what the underlying probabilistic model represents, etc.

Neural networks are great because it takes basically all of those smaller ideas and concatenates them into a super flexible statistical machine. It's really cool to see the "in->out" but it's even cooler once you have a good grasp on what's going on in the intermediate steps.

In my experience, almost everyone working with neural networks don't have those details down. This goes 100-fold for non-research roles. They learned the Keras API and are happy stacking layers, and as long as the output looks nice they push to production. For most cases empirical validation is probably enough, because NNs usually can achieve some incremental improvement just by virtue of the fact they have so many damn degrees of freedom. But to get a well-performing, well-founded model, you need to know the ins and outs.