Applying Stochastic Gradient Descent (SGD) to a single-feature linear regression problem

SGD is an optimisation algorithm that updates model parameters using the gradient of the loss function w.r.t. the parameters on a small and random subset of data. In this blog post, I'll explain how SGD works by applying it to a basic ML scenario. In the future I hope to apply it to more complex scenarios such as CNNs and reinforcement learning. 1. Single-feature Linear Regression (Easy) Our goal…