Multivariate Regression and Gradient Descent

In a previous video, we used linear and logistic regressions as a means of testing the gradient descent algorithm. I was asked to do a video on logistic regression, when I realized the example I used in the gradient descent video was rather complex. Not only is the model more complicated than linear regression, I was using multiple features as well. This would be akin to fitting a plane to 3-d data rather than a line to 2-d in the case of linear regression. In addition there is a lot of matrix manipulation to vectorize the code. In this video, I want to go over that matrix manipulation using the simple case of linear regression and show how by doing this, we can not only get a speed benefit by vectorizing, but also generalize our code to handle multiple parameters with a single gradient function.