Machine learning MCQ - Learning rate in gradient descent

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, What is gradient descent? Gradient descent is an optimization algorithm used to find the values of parameters of a function that minimizes a cost funtion

Machine Learning MCQ - What is learning rate in gradient descent

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1. Which of the following statements is true about the learning rate alpha in gradient descent?

a) If alpha is very small, gradient descent will be fast to converge. If alpha is too large, gradient descent will overshoot

b) If alpha is very small, gradient descent can be slow to converge. If alpha is too large, gradient descent will overshoot

c) If alpha is very small, gradient descent can be slow to converge. If alpha is too large, gradient descent can be slow too

d) If alpha is very small, gradient descent will be fast to converge. If alpha is too large, gradient descent will be slow

Answer: (b) If alpha is very small, gradient descent can be slow. If alpha is too large, gradient descent will overshoot   What is learning rate? Learning rate (alpha) is a hyper-parameter used to control the rate at which an algorithm updates the parameter estimates or learns the values of the parameters. It is used to scale the magnitude of parameter updates during gradient descent. Learning rate is a scalar, a value that tells the machine how fast or how slow to arrive at some conclusion.   Effect of small and large learning rates If the learning rate (alpha) is too small, learning will take so long to converge. On the other hand, if the learning rate is too large, during learning, it may miss to converge.   What is gradient descent? In machine learning, gradient descent is an optimization algorithm which is used to learn the model parameters. This algorithm works in iteration to find the local minimum of a cost function.

 

  

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What is gradient descent

Why do we use gradient descent algorithm?

What are the effects of small and large values of learning rate alpha?

What is learning rate and what is its significant in learning the parameters?

If alpha is small, it takes too long to converge. If alphas is large, it may not converge because of bouncing effects

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