Calculus For Machine Learning — Pdf Fixed

In the world of Machine Learning (ML), Calculus is not just an abstract mathematical discipline—it is the engine that drives learning itself. Every time a neural network adjusts its weights, or a linear regression model finds the best-fit line, calculus is working behind the scenes.

| Function | Derivative | Where it appears in ML | | :--- | :--- | :--- | | x^n | n*x^(n-1) | L2 Regularization | | e^x | e^x | Softmax / Cross-entropy | | log(x) | 1/x | Log Loss, MLE | | σ(x) = 1/(1+e^-x) (Sigmoid) | σ(x)*(1-σ(x)) | Output of binary classifier | | tanh(x) | 1 - tanh^2(x) | Hidden layer activation | | ReLU = max(0,x) | 0 if x<0 else 1 | Most common activation | calculus for machine learning pdf