Compare SGD and SVRG in the batching tutorials

[BalzaniEdoardo]

Expand the tutorial on batching comparing SGD and SVRG:

1. Show that, if you set the stepsize of the right size, the algorithm converge to the correct estimate
2. Compare with SGD, which won't converge without a decreasing stepsize schedule carefully calibrated.
3. Compare the iteration number, showing that SVRG requires less epochs to get to same log-likelihood level than SGD

Ideally, when we will have a run_iterator method we should showcase a simplified interface that will look something like:

# add a batcher parameter to GLM init, optional, if set it will try to run the algorithm using the iterator.
model = GLM(solver_name="SVRG", batcher=my_iterator)
model.fit(X, y)  # X could be lazy loaded

[billbrod]

As part of this, need to show what extra steps are required for SVRG beyond just calling update (and update the update docstring to point users here. @bagibence said this is already shown in a test -- which one was that?

[BalzaniEdoardo]

The docstrings of the update method of SVRG should point to our tutorial on how to construct the external loop. Eventually, no external loop will be needed, since we will implement a run iterator method.