Explain about gradient descent algorithm

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Accepted Answer

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the direction of steepest descent.

Gradient descent is commonly used in machine learning to optimize the parameters of a model by minimizing the cost function.
It works by calculating the gradient of the cost function with respect to each parameter and updating the parameters in the opposite direction of the gradient.
The learning rate determines the size of the steps taken during each iteration, too small a learning rate may result in slow convergence, while too large a learning rate may cause overshooting.
There are different variations of gradient descent such as stochastic gradient descent, mini-batch gradient descent, and batch gradient descent.
Example: In linear regression, gradient descent can be used to find the optimal values for the slope and intercept of the regression line.

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