The main difference of course being that you are implementing it in silicon rather than carbon, and having no bloody grammar or conception of how in the heck to explain how 'X set of nodes with Y set of weights with Z activation threshold function'=something useful.
They called them Neural networks for a reason. It's been quite a mystery both on the biology (in terms of gray matter) and computing (in terms of ANN) front why it works at all. But it does.
I think I've read Bellman's work before, but I'll take a look. Thanks for the pointer.
Just can't find enough hours in the day to keep on top of the state-of-the-art, and unfortunately the career hasn't budged me in the direction of anywhere I could weasel work on it into my day.
C'est la vie.
The crux is that you are programming in such a manner that you can differentiate your function w.r.t. parameters which are then tuned.
The "neural network" machine learning technique is not a simulation of biology. It's a nice marketing phrase. The technique is just math, maybe "inspired" by someone thinking about neurons.
Don't be misled by branding. For a good explanation of why some of these fanciful science terms come about, read Bellman's explanation of why he called his research "dynamic programming".