In continuity with the previous chapter, we carry on our explanation and experimentation with gradient descent. As we have already defined both the mathematical formulation and their translation into Python code, using matrix notation, we don't need to worry if now we have to deal with more than one variable at a time. Having used the matrix notation allows us to easily extend our previous introduction and example to multiple predictors with just minor changes to the algorithm.
In particular, we have to take note that, by introducing more parameters to be estimated during the optimization procedure, we are actually introducing more dimensions to our line of fit (turning it into a hyperplane, a multidimensional surface) and such dimensions have certain communalities and differences to be taken into account.