As a chemical engineer who started learning deep learning after learning regular old regression-based empirical modeling, my interpretation of deep learning is that it's just high-dimensional non-linear interpolation.
If what you're trying to predict can't be represented as some combination of your existing data, it breaks immediately. Data drives everything; all models are wrong, but some are useful. (George Box)
Incidentally, humans aren't very good at extrapolation, either, but our ability to generate good hypotheses differentiates us strongly from these models.
If what you're trying to predict can't be represented as some combination of your existing data, it breaks immediately. Data drives everything; all models are wrong, but some are useful. (George Box)