When training a model such as linear regression from scratch on a synthetic dataset, the true underlying parameters are perfectly known. This provides a unique opportunity to evaluate the success of the training loop by directly comparing the ground truth weights and bias with the parameters learned by the model. While exactly recovering the true parameters is generally difficult or impossible for deep networks because unique solutions often do not exist, stochastic gradient descent typically discovers parameter configurations that yield highly accurate predictions.