Gradient Descent Algorithm MCQ

Test your knowledge with important Gradient Descent Algorithm MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more Gradient Descent Algorithm MCQs at Bissoy

• Which of the following is not related to a gradient descent?
  ক

  AdaBoost

  খ

  Adadelta

  গ

  Adagrad

  ঘ

  RMSprop
• Which of the following statements is false about choosing learning rate in gradient descent?
  ক

  Small learning rate leads to slow convergence

  খ

  Large learning rate cause the loss function to fluctuate around the minimum

  গ

  Large learning rate can cause to divergence

  ঘ

  Small learning rate cause the training to progress very fast
• The main difference between gradient descents variants are based on the amount of data.
  ক

  True

  খ

  False
• The gradient is set to zero to find the minimum or the maximum of a function.
  ক

  False

  খ

  True
• Simple gradient descent is a better batch optimization method than conjugate gradients and quasi-newton methods.
  ক

  False

  খ

  True
• In batch method gradient descent, each step requires the entire training set be processed in order to evaluate the error function.
  ক

  True

  খ

  False
• Which of the following statements is false about gradient descent?
  ক

  It updates the weight to comprise a small step in the direction of the negative gradient

  খ

  The learning rate parameter is η where η > 0

  গ

  In each iteration, the gradient is re-evaluated for the new weight vector

  ঘ

  In each iteration, the weight is updated in the direction of positive gradient
• We can use gradient descent as a best solution, when the parameters cannot be calculated analytically.
  ক

  False

  খ

  True
• Gradient descent is an optimization algorithm for finding the local minimum of a function.
  ক

  True

  খ

  False