I think there is no general answer for your question. Based on the problem, it can learn fast or slow. You can also test different learning rates. By using bigger values, the steps in gradient descent would be bigger but it may do not learn well. And by small values it would be slower and maybe stick in local optima. You need to try different values and compare them.
There are also other factors and hyperparameters that may affect that, such as the network size, or architecture, optimizer,  … .