I'm certain that this is a very naive question, but I am just beginning to look more deeply at neural networks, having only used decision tree approaches in the past. Also, my formal mathematics training is more than 30 years in the past, so please be kind. :)
As I'm reading François Chollet's book on Deep Learning with Python, I'm struck that it appears that we are effectively treating the weights (kernel and biases) as terms in the standard linear equation ($y=mx+b$). At page 72 of the book, the author writes
output = dot(W, input) + b output = (output < 0 ? 0 : output)
Am I reading too much into this, or is this correct (and so fundamental I shouldn't be asking about it)?