Which of the following statements about Gradient Descent are true? (Select all that apply)Group of answer choicesLearning rate is a crucial hyperparameter in its performance.It requires calculation of gradients for the entire dataset.It can be very slow when the dataset is very large.It is guaranteed to find the global minimum for non-convex functions.
Question
Which of the following statements about Gradient Descent are true? (Select all that apply)Group of answer choicesLearning rate is a crucial hyperparameter in its performance.It requires calculation of gradients for the entire dataset.It can be very slow when the dataset is very large.It is guaranteed to find the global minimum for non-convex functions.
Solution
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"Learning rate is a crucial hyperparameter in its performance." - True. The learning rate determines how big the steps the algorithm takes downhill during optimization. If it's too small, the algorithm will be slow. If it's too large, the algorithm might overshoot the minimum and fail to converge.
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"It requires calculation of gradients for the entire dataset." - True and False. This is true for Batch Gradient Descent, which uses
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