Gradient descent serves as a first-order optimization algorithm designed to minimize differentiable cost functions by iteratively adjusting model parameters in the direction opposite to the gradient vector. This mechanism relies on the formal definition of multivariable calculus, where the negative gradient indicates the steepest decrease in function value within a high-dimensional parameter space defined by weights and biases. As a foundational concept in machine learning theory, it establishes how neural networks converge toward local minima without guaranteeing global optimality, distinguishing the process from supervised memorization when applied to structured data landscapes versus random noise.
Gradient descent serves as a first-order optimization algorithm designed to minimize differentiable cost functions by iteratively adjusting model parameters in the direction opposite to the gradient …