backprop is a very simple algorithm, nothing to fear there. The problems are to calculate the derivates if you want to be flexible building your model. But for feedforward networks with sigmoid activation, the equations to update the weights are a joke.
But I meant, if you see the equations and the steps without understanding completely the insights, it is a joke of an algorithm. It just does some multiplications and applies the new gradients, move to the previous layer and repeat.
You won't regret. One of the best explanation of backprop on the internet.
So it's probably as simple as
newWeight = oldWeight +/- (stepValue * someFactor)