AI Neural Networks MCQ
Neural Networks MCQs : This section focuses on "Neural Networks" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.
1. Who was the inventor of the first neurocomputer?
A. Dr. John Hecht-Nielsen
B. Dr. Robert Hecht-Nielsen
C. Dr. Alex Hecht-Nielsen
D. Dr. Steve Hecht-Nielsen
View Answer
Ans : B
Explanation: The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen.
2. What is full form of ANNs?
A. Artificial Neural Node
B. AI Neural Networks
C. Artificial Neural Networks
D. Artificial Neural numbers
View Answer
Ans : C
Explanation: Artificial Neural Networks is the full form of ANNs.
3. How many types of Artificial Neural Networks?
A. 2
B. 3
C. 4
D. 5
View Answer
Ans : A
Explanation: There are two Artificial Neural Network topologies : FeedForward and Feedback.
4. In FeedForward ANN, information flow is _________.
A. unidirectional
B. bidirectional
C. multidirectional
D. All of the above
View Answer
Ans : A
Explanation: FeedForward ANN the information flow is unidirectional.
5. In which ANN, loops are allowed?
A. FeedForward ANN
B. FeedBack ANN
C. Both A and B
D. None of the Above
View Answer
Ans : B
Explanation: FeedBack ANN loops are allowed. They are used in content addressable memories.
6. Which of the following is not an Machine Learning strategies in ANNs?
A. Unsupervised Learning
B. Reinforcement Learning
C. Supreme Learning
D. Supervised Learning
View Answer
Ans : C
Explanation: Supreme Learning is not an Machine Learning strategies in ANNs.
7. What is the full form of BN in Neural Networks?
A. Bayesian Networks
B. Belief Networks
C. Bayes Nets
D. All of the above
View Answer
Ans : D
Explanation: The full form BN is Bayesian networks and Bayesian networks are also called Belief Networks or Bayes Nets.
8. Which of the following is an Applications of Neural Networks?
A. Automotive
B. Aerospace
C. Electronics
D. All of the above
View Answer
Ans : D
Explanation: All above are appliction of Neural Networks.
9. What is the name of node which take binary values TRUE (T) and FALSE (F)?
A. Dual Node
B. Binary Node
C. Two-way Node
D. Ordered Node
View Answer
Ans : B
Explanation: Boolean nodes : They represent propositions, taking binary values TRUE (T) and FALSE (F).
10. What is perceptron?
A. a single layer feed-forward neural network with pre-processing
B. an auto-associative neural network
C. a double layer auto-associative neural network
D. a neural network that contains feedback
View Answer
Ans : A
Explanation: The perceptron is a single layer feed-forward neural network.
11. What is an auto-associative network?
A. a neural network that contains no loops
B. a neural network that contains feedback
C. a neural network that has only one loop
D. a single layer feed-forward neural network with pre-processing
View Answer
Ans : B
Explanation: An auto-associative network is equivalent to a neural network that contains feedback. The number of feedback paths(loops) does not have to be one.
12. A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 3, 2 and 1 respectively. What will be the output?
A. 30
B. 40
C. 50
D. 60
View Answer
Ans : B
Explanation: The output is found by multiplying the weights with their respective inputs, summing the results and multiplying with the transfer function. Therefore: Output = 2 * (1*4 + 2*3 + 3*2 + 4*1) = 40.
13. What is Neuro software?
A. A software used to analyze neurons
B. It is powerful and easy neural network
C. Designed to aid experts in real world
D. It is software used by Neurosurgeon
View Answer
Ans : B
Explanation: Neuro software is powerful and easy neural network.
14. What is back propagation?
A. It is another name given to the curvy function in the perceptron
B. It is the transmission of error back through the network to adjust the inputs
C. It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
D. None of the Above
View Answer
Ans : C
Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
15. Neural Networks are complex ______________ with many parameters.
A. Linear Functions
B. Nonlinear Functions
C. Discrete Functions
D. Exponential Functions
View Answer
Ans : A
Explanation: Neural networks are complex linear functions with many parameters.
16. The network that involves backward links from output to the input and hidden layers is called _________
A. Self organizing map
B. Perceptrons
C. Recurrent neural network
D. Multi layered perceptron
View Answer
Ans : C
Explanation: RNN (Recurrent neural network) topology involves backward links from output to the input and hidden layers.
17. Which of the following is not the promise of artificial neural network?
A. It can explain result
B. It can survive the failure of some nodes
C. It has inherent parallelism
D. It can handle noise
View Answer
Ans : A
Explanation: The artificial Neural Network (ANN) cannot explain result.
18. The BN variables are composed of how many dimensions?
A. 2
B. 3
C. 4
D. 5
View Answer
Ans : B
Explanation: The BN variables are composed of two dimensions : Range of prepositions and Probability assigned to each of the prepositions.
19. The output at each node is called_____.
A. node value
B. Weight
C. neurons
D. axons
View Answer
Ans : A
Explanation: The output at each node is called its activation or node value.
20. The first artificial neural network was invented in _____.
A. 1957
B. 1958
C. 1959
D. 1960
View Answer
Ans : B
Explanation: The first artificial neural network was invented in 1958.
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