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Understanding Gradient Descent in ML by alimohamedali2033 is a document available to read on EtoBox.

Gradient Descent (GD) is an optimization algorithm used in machine learning to minimize the cost function of neural networks by adjusting model parameters in the direction of the negative gradient. The learning rate is a key hyperparameter that determines the step size during optimization, making its selection vital for effective training. An example of GD in action is linear regression, where it helps find the best-fitting coefficients for a linear model based on data points.

Author

alimohamedali2033

Language

EN