Jacobian Matrix refers to a mathematical matrix containing all first-order partial derivatives of a vector-valued function, representing how small changes in input variables affect the outputs of multivariate functions. In artificial intelligence and machine learning, Jacobian matrices are fundamental for understanding gradient-based optimization, neural network training, and sensitivity analysis, enabling algorithms to efficiently navigate high-dimensional