The softmax function transforms an N-dimensional vector into probabilities between 0 and 1 that sum to 1, with larger input values receiving proportionally larger outputs. It has wide application in multiclass classification tasks in machine learning. Computing its derivative requires partial derivatives with respect to each input-output pair, expressed as a Jacobian matrix.