This is a pretty basic question.
So...backprop is an efficient algorithm for computing the gradients used by the optimizer to improve model parameters, no matter if SDG or something else. I get that.
The actual difference between classic gradient descent and stochastic gradient descent is the batchsize used for computing the gradients, thats why SGD is more efficient. I get that as well.
But if I now use backprop....where is then the difference between them? We move in the direction of the negative gradient, that holds for both of them. So again where is the difference?
Edit: To prevent misunderstanding. The difference between SGD and GD after use of backprop is meant, not the difference between backprop and SGD/GD.
Thanks for the contributions. The differentiation between backprop plus optimization and the learning process as a whole, which itself is also often called backprop, was the reason for my question. It implied for me, that if the backprop computes the gradients and the optimizer only modifies the parameters afterwards, that there had to be a difference in the way they do it except for the different gradients.