[test6554]

It's been a while, but I remember backprop starting at the end of the neural net, and working backwards. Each weight that contributed to a wrong answer had its weight value weakened or even reversed by some small factor. And each weight that contributed to a correct answer had its weight value strengthened by some small factor.

So it's probably as simple as

newWeight = oldWeight +/- (stepValue * someFactor)

[jorgemf]

exactly, the someFactor is the error of the next layer calculated with the derivades of the functions (as you would calculate the minimum of a funtion using the derivades). The tricky part is to calculate the derivades, but since auto differentiation we can do a lot of cool stuff.

[tostitos1979]

The differentiation is probably why I wouldn't have bothered to hack it myself. Curious how you/others would tackle it? What do you mean by auto differentiation?

[jorgemf]

https://en.wikipedia.org/wiki/Automatic_differentiation