From what I have learnt so far, there are 2 Optimization techniques used for Training the Datasets (or Data Models) as follows:
a) Gradient Descent (esp. SGD i.e. Stochastic Gradient Descent) &
b) QP (Quadratric Programming) technique
Both the aforesaid Algorithms are used for Optimization as well as both function from back-end i.e. behind the scenes.
May I know what are the differences between SGD & QP techniques?