But it explains the wrong thing. It's a fundamentally ridiculous framing. A basic neural network layer is a linear transformation + constant offset + activation

But it explains the wrong thing. It's a fundamentally ridiculous framing. A basic neural network layer is a linear transformation + constant offset + activation function. Linear transformations are the elementary building block. And Fourier Transform is a quintessential example of a linear transform. But the article doesn't actually discuss that at all. If you don't understand this how will you ever deal with more complicated NN architectures?