Selected MCQ

Current Question
In a neural network, why is it important to normalize input features?
  • A. To make the network more complex
  • B. To improve convergence speed and training stability
  • C. To prevent overfitting entirely
  • D. Normalization is not needed
Correct Answer: B
Explanation:
Feature normalization ensures they have similar scales, which helps gradient descent converge faster.
Related Question 1
What is the main function of dropout layers in a neural network?
  • A. Speed up training by skipping layers
  • B. Prevent co-adaptation of neurons to reduce overfitting
  • C. Ensure output is always positive
  • D. Increase network depth
Correct Answer: B
Explanation:
Dropout randomly turns off neurons during training, reducing overfitting.
Related Question 2
In neural networks, what is 'backpropagation' used for?
  • A. Adjusting weights using gradient descent
  • B. Propagating inputs through layers
  • C. Generating output predictions
  • D. Normalizing data
Correct Answer: A
Explanation:
Backpropagation computes the gradients of the loss with respect to the network weights, which are then used to update them via optimization methods.
Related Question 3
What type of network topology features a single central point with all devices connected to it?
  • A. Bus topology
  • B. Star topology
  • C. Ring topology
  • D. Mesh topology
Correct Answer: B
Explanation:
A star topology connects all nodes to a central hub or switch.
Related Question 4
What is the primary advantage of using a GPU over a CPU for training deep neural networks?
  • A. Faster single-thread performance
  • B. Large memory capacity
  • C. Parallel processing of many operations
  • D. Built-in neural network instructions
Correct Answer: C
Explanation:
GPUs can perform many operations in parallel, greatly speeding up matrix computations in neural network training.
Related Question 5
What role does the "loss function" serve in training a neural network?
  • A. Generate predictions
  • B. Aggregate features
  • C. Calculate error to guide learning
  • D. Normalize data
Correct Answer: C
Explanation:
The loss function quantifies the difference between predictions and true values, guiding weight adjustments.