So while doing the the first course of the DLS, i can see the implementation (mostly for the backward propagintion) is different than how you do it for normal logistic regression in the ML course (which makes sense).

But that made me wonder, if i should consider optimizers for normal logistic regression and deep learning as 2 similar concepts that is a bit different.

To give an example of what i mean. Lets say i have made the optimizers as their own class, to be used as general components. So should think of them as general compontens that can be shared or do an implementation for both regular ML and DL.