The loss function (or sometimes called error function) is a function which compares the output of an ML model as compared to the ground truth for a single training example, while the cost function is meant to be calculated over the entire training set (or mini-batch for mini-batch gradient descent).
Gradient descent is a method of minimizing the machine learning model cost function in linear regression models. In the gradient descent method, the (internal) parameters of the ML model are tuned over several training iterations by taking gradual "steps" down a slope in the function graph, aiming towards a minimum error value. In gradient descent, ... Read more
