Neural Networks, Gradient Descent.
2xy) the gradient vector/Jacobian is Top Row: The partial derivatives of the first component of F(x, y): ∂(x² + y²) / ∂x and ∂(x² + y²) / ∂y giving [ 2x 2y] Bottom Row: The partial derivatives of the second component of F(x, y): ∂(2xy) / ∂x and ∂(2xy) / ∂y giving [2y 2x] The Jacobian for all the