Selected MCQ

Current Question
What is the vanishing gradient problem?
  • A. Loss function failing to converge
  • B. Gradients are becoming very small in deep networks
  • C. Model accuracy exceeding training accuracy
  • D. Optimization getting stuck in local minima
Correct Answer: B
Explanation:
The vanishing gradient problem occurs when gradients shrink, hampering learning in deep networks.
Related Question 1
Which activation function is commonly used in hidden layers of deep neural networks due to its ability to mitigate vanishing gradient problems?
  • A. Sigmoid
  • B. Tanh
  • C. ReLU
  • D. Softmax
Correct Answer: C
Explanation:
ReLU (Rectified Linear Unit) is popular for hidden layers because it accelerates convergence by avoiding vanishing gradients.
Related Question 2
Which of the following is a common loss function for regression problems?
  • A. Cross-entropy loss
  • B. Mean squared error
  • C. Hinge loss
  • D. Binary entropy
Correct Answer: B
Explanation:
Mean squared error is typically used for regression to measure the average squared difference.