Gradient descent is a method used to converge to a minimum by making small, gradual adjustments.
In gradient descent, you start with a guess and repeatedly adjust it, moving towards the lowest error or best fit for your data, like finding the lowest point in the valley. This technique is min commonly used in machine learning to improve predictions and decisions.
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