The Simplest Possible Neural Network, Explained

This text provides a simple explanation of neural networks using Python. The author guides readers through building a basic neural network to predict the slope and bias of a line, essentially performing linear regression. The process involves initializing parameters (weight and bias), making predictions, evaluating errors, and updating parameters iteratively until the predicted values match the true values. The text also briefly touches upon the mathematical principles behind parameter updates but acknowledges that explaining these concepts in detail would require going down a rabbit hole of complex calculus.