No, it is relatively few words to quickly touch on several different concepts that go well beyond basic approximation theory.

I can construct a Gaussian process model (essentially fancy linear regression) that will fit _all_ of my medical image data _exactly_, but it will perform like absolute rubbish for determining tumor presence compared to if I trained a convolutional neural network on the same data and problem _and_ perfectly fit the data.

I could even train a fully connected network on the same data and problem, get any degree of fit you like, and it would still be rubbish.

That isn't what they are saying at all, lol.