Gradient Descent


Gradient Descent is an iterative optimization algorithm used to minimize the cost function of a machine learning model by adjusting its parameters. It works by calculating the gradient of the cost function with respect to the parameters and updating the parameters in the opposite direction of the gradient. This process is repeated until the algorithm converges to a minimum of the cost function. Gradient Descent is widely used in various machine learning algorithms such as linear regression, logistic regression, and neural networks.


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