An optimization algorithm used to minimize the error or loss function in machine learning models by iteratively adjusting model parameters. It works by calculating the gradient (slope) of the loss function and moving in the direction of steepest descent. This fundamental technique enables AI systems to learn from data by continuously improving their performance through parameter updates.
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