Random Initialization MCQ

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

• Initialization is particularly important in neural networks.
  ক

  True

  খ

  False
• A too-large weight initialized on the neural network can lead to vanishing gradient whereas a too-small weight initialized on the neural network can lead to exploding gradient.
  ক

  True

  খ

  False
• What will be the result if we use the identical set of weights whenever we train the network?
  ক

  The learning algorithm would work fine

  খ

  The learning algorithm would make changes to the network weights

  গ

  The learning algorithm would fail to make changes to the network weights, and the model will be stuck

  ঘ

  The learning algorithm would make changes to the network weights, and the model will be stuck
• Among the below distributions pair which one can be used to attain random initialization?
  ক

  Uniform distribution and Normal distribution

  খ

  Standard distribution and Normal distribution

  গ

  Uniform distribution and Zero distribution

  ঘ

  Zero distribution and Normal distribution
• Which of the following initialization method is used often in order to recommend the weights during weight initialization?
  ক

  Zero initialization method

  খ

  Random initialization method

  গ

  Xavier initialization method

  ঘ

  He-et-al initialization method
• Which of the following statement is not true about random initialization?
  ক

  Weights are randomly distributed with a mean of zero

  খ

  Weights are randomly distributed with a standard deviation of one

  গ

  To break symmetry the weights are initialized randomly

  ঘ

  Following random initialization, each neuron can then proceed to learn the same function of its input
• Arrange the following steps in sequence used in the course of training a neural network after initializing weight and biases. I. Backward propagation II. Forward propagation III. Compute the loss function IV. Repeat the steps for n iterations till we have minimized the loss function, without overfitting the train data
  ক

  I → II → III → IV

  খ

  II → III → I → IV

  গ

  III → II → I → IV

  ঘ

  II → I → III → IV