Explicit, efficient error
backpropagation in arbitrary, discrete, possibly sparsely connected,
neural networks-like networks was first described in Linnainmaa's 1970 master's thesis, when he introduced the reverse mode of
automatic differentiation (AD), in order to efficiently compute the
derivative of a
differentiable composite function that can be represented as a
graph, by recursively applying the
chain rule to the building blocks of the function. Linnainmaa published it first, following Gerardi Ostrowski who had used it in the context of certain process models in chemical engineering some five years earlier, but didn't publish. ==Notes==