Calculus for Machine Learning

Derivatives, gradients and the chain rule are the machinery under every training run, and knowing them changes what you can debug. This path builds the derivative as a local linear model, then gradients, Jacobians and Hessians, then the chain rule as a product whose evaluation order decides the cost, and finally automatic differentiation itself. You will finish knowing why a billion-parameter gradient costs one backward pass, why conditioning sets your iteration count, and where autodiff answers the wrong question exactly.