Groups
Category
Level
Softmax turns arbitrary real-valued scores (logits) into probabilities that sum to one.
A smooth manifold is a space that looks like ordinary Euclidean space when you zoom in, glued together using charts that transition smoothly.
Lagrange multipliers let you optimize a function while strictly satisfying equality constraints by introducing auxiliary variables (the multipliers).
Implicit differentiation lets you find slopes and higher derivatives even when y is given indirectly by an equation F(x,y)=0.
The multivariable chain rule explains how rates of change pass through a pipeline of functions by multiplying the right derivatives (Jacobians) in the right order.
Partial derivatives measure how a multivariable function changes when you wiggle just one input while keeping the others fixed.
Matrix calculus extends single-variable derivatives to matrices so we can differentiate functions built from matrix multiplications, traces, and norms.
The determinant of a square matrix measures how a linear transformation scales volume and whether it flips orientation.