Engineering Sandbox rollout · bundle-mounted ML essays

Fit the line, then watch the optimizer learn how to reach it.

This lesson moves from linear intuition to measurable fit quality, then into gradient descent and coefficient interpretation. The goal is not just to read the formula, but to see how the line, loss, and parameters move together.

Probe a prediction Tune the loss directly Watch gradient descent converge

How to use this page

Start with the fit story, then jump into the loss controls and the gradient-descent scene once the idea of “best line” feels concrete. The interpretation section is a good final pass once the mechanics are already familiar.

Play first: fit the line Jump to optimization

Loading Linear Regression lesson…