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.
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