I'm not the OP, but personally I see NN's as being really really useful where the input data is unstructured (such as text or images). The deep approach (appears to) build better features than a human can, but I'm not convinced that they are _that_ much better (or indeed at all) than standard methods for tabular data.
Once upon a time, when I used to hire data people, I'd ask them to tell me about a recent data project. They'd normally mention some kind of complex model, and I'd ask them how much better it was than linear/logistic regression. A really large proportion of candidates (around 50%) couldn't answer this because they'd never compared their approach to anything simpler.
One person told me that linear regression wasn't in the top 10 Kaggle models, so they would never use it.
Once upon a time, when I used to hire data people, I'd ask them to tell me about a recent data project. They'd normally mention some kind of complex model, and I'd ask them how much better it was than linear/logistic regression. A really large proportion of candidates (around 50%) couldn't answer this because they'd never compared their approach to anything simpler.
One person told me that linear regression wasn't in the top 10 Kaggle models, so they would never use it.