Gradient Descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent direction, which is determined by the negative gradient of the function. In mathematical terms, if we have a function , the gradient points in the direction of the steepest increase, so to minimize , we update our variable using the formula:
where is the learning rate, a hyperparameter that controls how large a step we take on each iteration. The process continues until convergence, which can be defined as when the changes in are smaller than a predefined threshold. Gradient Descent is widely used in machine learning for training models, particularly in algorithms like linear regression and neural networks, making it a fundamental technique in data science. Its effectiveness, however, can depend on the choice of the learning rate and the nature of the function being minimized.
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