It is a computationally clever application of the chain rule to minimize the amount of computation needed to compute gradients for all parameters in the network.
IMO backprop is the most trivial implementation of differentiation in neural networks. Do you know an easier way to compute gradients with larger overhead? If so, please share it.
My first forays into making neural networks used replacement rules to modify an expression tree until all the “D” operators went away, but that takes exponential complexity in network depth if you aren’t careful. Finite differences is linear in number of parameters, as is differentiation by Dual Numbers
I totally didn't realize this until these comments. Neat!
I went digging in wikipedia.. the Backpropagation article was created in 2005 and yet the mention of association/derivation from the chain rule wasn't mentioned until 2014, through a borrow from the German article