Here is the special sauce: “We can consider the the discrete Fourier transform (DFT) to be an artificial neural network: it is a single layer network, with no bias, no activation function, and particular values for the weights. The number of output nodes is equal to the number of frequencies we evaluate.”
A single layer neural network is a sum of products, the basic Fourier equation is a sum of products.
In this view there are lots of single layer neural networks out there. For me, it’s the training algorithm (backprop) that sets apart the neural net.
A single layer neural network is a sum of products, the basic Fourier equation is a sum of products.
In this view there are lots of single layer neural networks out there. For me, it’s the training algorithm (backprop) that sets apart the neural net.