The Calculus That Powers Neural Networks

Here is my guide to the calculus concepts that actually matter for ML 1. Derivatives and Gradients At its heart, training a model is an optimization problem. We define a Loss Function (L) that tells us how "bad" our model is. We want to minimize L. T...