An iterative optimization algorithm, gradient descent minimizes errors in machine learning models by adjusting parameters step-by-step. It calculates the slope of the loss function, moving toward the lowest point. Data scientists, AI engineers, and researchers use it to train neural networks, improve predictions, and fine-tune algorithms for tasks like image recognition and natural language processing.
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