In gradient descent algo, small learning rate _____.1 pointovershoot, become stable and divergeovershoot, become unstable and divergeovershoot, become stable and convergeNone of the above
Question
In gradient descent algo, small learning rate _____.1 pointovershoot, become stable and divergeovershoot, become unstable and divergeovershoot, become stable and convergeNone of the above
Solution
In gradient descent algorithm, a small learning rate will cause the model to converge slowly. This is because the learning rate determines the step size at each iteration while moving toward a minimum of a loss function. If the learning rate is too small, the model will need many updates to reach the minimum. Therefore, the correct answer is "None of the above".
Similar Questions
What role does the learning rate play in the Steepest Descent method? a. It represents the error function b. It determines the size of the incremental steps in updating parameters c. It is the output of the input layer d. It represents the difference between true output and observed output
In Gradient Descent, refers to the magnitude of updates to the parameters, and refers to the direction of updates.
Gradient Descent algorithms converge to a local minimum, and if the function is convex, they converge to a __________ minimum.
_______occurs when the gradients become very small and tend towards zero.a.Gated Recurrent Unit Networks.b.Long Short Term Memory Networksc.Vanishing Gradientsd.Exploding Gradients
Explain the role of the following factors in reaching global minima with a gradient descent algorithm for linear regression.a. Epochsb. Learning ratec. Parametersd. Bias and Variance
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.