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Gradient descent serves as a fundamental algorithm in machine learning. It aids models to adjust their parameters by iteratively minimizing the error. This strategy involves estimating the gradient of the error metric, which signals the direction of steepest ascent. By shifting the parameters in the inverse direction of the gradient, the model appr