Problem statementSend feedbackYou run gradient descent for 20 iterations with α=0.3 and compute cost function after each iteration. You find that the value of cost function decreases slowly and is still decreasing after 20 iterations. Based on this, which of the following conclusions seems most plausible ?
Question
Problem statementSend feedbackYou run gradient descent for 20 iterations with α=0.3 and compute cost function after each iteration. You find that the value of cost function decreases slowly and is still decreasing after 20 iterations. Based on this, which of the following conclusions seems most plausible ?
Solution
Based on the information given, the most plausible conclusion is that the learning rate, α, might be too small. If the cost function is decreasing slowly, it suggests that the steps taken by the gradient descent algorithm are too small. This could be because the learning rate is too small.
In gradient descent, the learning rate determines the size of the steps we take to reach a (local) minimum. If the learning rate is too small, the algorithm will take tiny steps and will need many iterations to converge to the minimum. This seems to be the case here, as the cost function is still decreasing after 20 iterations.
Therefore, you might want to try increasing the learning rate α to speed up convergence. However, be careful not to set the learning rate too high, as this could cause the algorithm to overshoot the minimum and fail to converge.
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