All right, so where we left off, we had simplified our algebraic expression for the squared error to the line from the end data points. And we kind of visualized this expression right here would be a surface in, I guess you could view it as a surface in three dimensions. Where for any m and b is going to be a point on that surface that represents the squared error for that line. And our goal is to find the m and the b, which would define an actual line, to find the m and b that minimize the squared error. And the way that we do that is we find a point where the partial derivative of the squared error with respect to m is 0, and the partial derivative with respect to b is also equal to 0. So it's flat with respect to m. So that means that the slope in this direction is going to be flat. Let me do it in the same color.