A useful way to think about derivatives (and gradients/Jacobians more generally) is as the maps that give you the best affine approximation at a point. This is part of a series of videos for COS 302: Mathematics for Numerical Computation and Machine Learning, replacing lectures after the course went remote due to the COVID-19 pandemic.
Laboratory for Intelligent Probabilistic Systems
Princeton University Department of Computer Science