not so clear what is the limit of the infinitely small learning rate that actually is well defined so so i'm just explaining it on this slide so if i define stochastic gradient descent using this variable s of t that would be you know one for some samples and zero for some other samples so if i do what is usually done in stochastic gradient descent is that every at every time step i randomly choose who is in the batch and who is not in the batch well then this doesn't have a well-defined limit of the learning