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What is Understanding Learning Rate in Gradient Descent about?

The learning rate, alpha, is crucial for the effectiveness of gradient descent, as a poorly chosen rate can lead to slow convergence or divergence. If alpha is too small, the descent will be slow, requiring many tiny steps to reach the minimum, while if it is too large, it may overshoot the minimum and fail to converge. Additionally, if the parameter W is already at a local minimum, further gradient descent steps will not change W, as the derivative at that point is zero.

Author

sleepless_geek

Language

EN