This and the next several videos will provide you with clear and colorful examples of all of the most important differentiation rules. We kick these rules off with the Constant Rule.

The derivative rules are critical to machine learning as they allow us to find the derivatives of cost functions. These cost-function derivatives are concatenated into the "gradient" that we descend to allow ML models to learn.

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.