Gradient Descent in More Than 2 Dimensions

I've discussed gradient descent in 2 dimensions, but what happens when you add more tweakable parameters? Well, you have to increase the dimensionality of the loss function. In this video, I discuss how to conduct gradient decent in more than 2 dimensions by computing the gradient vector followed by the update rule. #machinelearning #ml