Gradient Descent in Linear Regression | ML

Gradient Descent is an optimization algorithm used to find the optimum values of a model's parameters by minimizing a cost function. It works by iteratively updating parameters in the direction opposite to the gradient, which points to the steepest increase of the function. The algorithm adjusts the parameters based on the learning rate and gradient computed from the cost function. This process continues until the parameters converge to their optimal values. Gradient Descent is widely used in machine learning to train models and improve their accuracy.

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