gradient descent for linear regression
Gradient descent for linear regression is an iterative optimization algorithm that minimizes the error between predicted and actual values by adjusting the regression parameters based on the slope (gradient) of the error function. It starts with an initial set of parameters and updates them in small steps, following the direction that minimizes the error, until convergence is reached and the best-fitting line for the data is found.
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Similar Concepts
- accelerated gradient descent methods
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