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How can we find maximums and minimums for complicated functions with an enormous number of variables like you might get when studying neural networks or machine learning? In this video we are going to talk about a topic from nonlinear optimization called Gradient Descent (or Gradient Ascent if you want maximums) where you stepbystep approach an extremum by stepping in the direction of the gradient vector. We're going to see the basic algorithm, see some common pitfalls, and then upgrade it using a method called line searches to improve the efficiency.

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