[dmritard96]

I am no expert but I think it allows for a higher order function to be arrived at. An example would be the output of a simple net, where the output is a linear combination of features. This would be extremely shallow and while this will work for some things, there are going to be some instances where this doesn't capture nuanced scenarios.

in a shallow net, maybe college student selection based on sat scores gets a heavy weight/low threshold/whatever. in a shallow linear combo, this will likely always carry a large weight.

in a deeper net, it might be able to learn that SATS are a great predictor except for when X Y Z or some combination of those are some particular value, in which case it might be wholly irrelavent. The deeper it is, the longer it will take to train, but the more it can handle exception cases/trends and approximate reality