Machine Learning for Humans, Part 2.1: Supervised Learning

The goal of gradient descent is to find the minimum of our model's loss function by iteratively getting a better and better approximation of it. Imagine yourself walking through a valley with a blindfold on. Your goal is to find the bottom of the valley. How would you do it? A reasonable approach would be to touch the ground around you and move in whichever direction the ground is sloping down most steeply.