Gradient Descent MCQ

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• What is the generalized goal of gradient descent?
  ক

  Minimize J(t1)

  খ

  Minimize J(t0, t1, t1, …, tn)

  গ

  Minimize J(t0, t1)

  ঘ

  Maximize J(t1)
• h(x) = t0 + t1x. Alpha value (learning rate) is 0.1. Initial theta values are 0.3, 0.73. X = and Y = . What is the value of t0 after 1st iteration?
  ক

  and Y = . What is the value of t0 after 1st iteration? ] 0.73

  খ

  1, 2, 3

  গ

  1, 2, 3

  ঘ

  1, 2, 3
• h(x) = t0 + t1x. Alpha value (learning rate) is 0.1. Initial theta values are 0, 0. X = and Y = . What is the value of cost function after 1st iteration?
  ক

  and Y = . What is the value of cost function after 1st iteration? ] 0.3

  খ

  1, 2, 3

  গ

  1, 2, 3

  ঘ

  1, 2, 3
• Who invented gradient descent?
  ক

  Ross Quinlan

  খ

  Leslie Valiant

  গ

  Thomas Bayes

  ঘ

  Augustin-Louis Cauchy
• What is the output of gradient descent after each iteration?
  ক

  Updated t0, t1

  খ

  J(t0, t1)

  গ

  J(t1, t0)

  ঘ

  A better learning rate
• Cost function has a squared term, but gradient descent does not. Why?
  ক

  Integration of cost function

  খ

  The square root of the cost function

  গ

  Differentiation of cost function

  ঘ

  They are not related
• The cost function contains a squared term and is divided by 2*m where m is the number of training examples. What is in the denominator of gradient descent function?
  ক

  2*m

  খ

  m

  গ

  m/2

  ঘ

  m2
• What is the correct way to update t0 and t1?
  ক

  Calculate t0 and t1 and then update t0 and t1

  খ

  Update t0 and t1 and then calculate t0 and t1

  গ

  Calculate t0, update t0 and then calculate t1, update t1

  ঘ

  Calculate t1, update t1 and then calculate t0, update t0
• What happens when the learning rate is high?
  ক

  It always reaches the minima quickly

  খ

  It overshoots the maxima

  গ

  Most of the times, it overshoots the minima

  ঘ

  Nothing happens
• Gradient descent always gives minimal cost function.
  ক

  True

  খ

  False
• What is the goal of gradient descent?
  ক

  Reduce complexity

  খ

  Reduce overfitting

  গ

  Maximize cost function

  ঘ

  Minimize cost function