Machine Learning, Data Science, Mathematics, Artificial Intelligence, Deep Learning.
the square distances between each point’s y value and the candidate line’s y value (computed at mx + b). It’s conventional to square this distance to ensure that it is positive and to make our error function differentiable. Lines that fit our data better (where better is defined by our error