The Hessian matrix is a square matrix of second-order partial derivatives of a scalar-valued function. It provides important information about the local curvature of the function, which is essential in optimization techniques, especially gradient descent and its variants, where understanding how the function behaves around a point can significantly impact convergence speed and direction.
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