[pedrosorio]

Mentioning polynomials is a pretty poor way to explain it for two reasons:

- It requires some mathematical understanding so will exclude some part of the non-technical audience

- It is the incorrect analogy. Non-linearities in neural networks have nothing to do with polynomials. In fact, polynomial regression is a type of linear regression, and for the most part, it sucks.

Also, as someone mentioned, all the “serious” alternative ML methods prior to the deep learning revolution allow modeling non linearities (even if just through modification of linear regressions, like polynomial regression).

[_gmax0]

Thanks for the correction. It's been some time since I actively thought about the theory (evidently I didn't digest it correctly the first time!).