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Gradient descent is an optimization algorithm widely utilized in the fields of machine learning and deep learning for identifying the optimal parameters of a model. It involves iteratively adjusting the parameter values in the negative gradient direction of the loss function, which measures the disparity between the predicted output of the model and the actual output.

Mathematics is the study of patterns and relationships. By exploring these patterns and relationships, mathematicians can create equations and models that accurately predict the behavior of natural phenomena. This is why mathematics is such an important tool in fields such as physics, engineering, and chemistry.