i don’t understand the role of taking the learning rate and maximum iterations in gradient descent function.
Linear regression
Gradient descent is the approach in which don’t get the optimal solution directly, we take multiple jumps to reach the optimal solution, which can be considered same as number of epochs.
Learning rate is the factor by which we multiply jump distance (theoretically gradients calculated) so that jump distance doesn’t become that large, that we skip our optimal solution and jump on other part.
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