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MCQ-ANN Quiz

AI Neural Networks MCQ 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 Ans : B Explanation: The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen.
2. How many types of Artificial Neural Networks? A. 2 B. 3 C. 4 D. 5
3. In which ANN, loops are allowed? A. FeedForward ANN B. FeedBack ANN C. Both A and B D. None of the Above
4. What is the full form of BN in Neural Networks? A. Bayesian Networks B. Belief Networks C. Bayes Nets D. All of the above

11. Which of the following is not an Machine Learning strategies in ANNs? A. Unsupervised Learning B. Reinforcement Learning C. Supreme Learning D. Supervised Learning
12. Which of the following is an Applications of Neural Networks? A. Automotive B. Aerospace C. Electronics D. All of the above
13. 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
14. 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
15. 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
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 1: ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems.

a) True b) False 2: Artificial neural network used for a) Pattern Recognition b) Classification c) Clustering d) All of these Q1. A neural network model is said to be inspired from the human brain. The neural network consists of many neurons, each neuron takes an input, processes it and gives an output. Here’s a diagrammatic representation of a real neuron. Which of the following statement(s) correctly represents a real neuron? A. A neuron has a single input and a single output only B. A neuron has multiple inputs but a single output only C. A neuron has a single input but multiple outputs D.A neuron has multiple inputs and multiple outputs E. All of the above statements are valid Q2. Below is a mathematical representation of a neuron.

What would be the weights and bias? (Hint: For which values of w1, w2 and b does our neuron implement an AND function?) A. Bias = -1.5, w1 = 1, w2 = 1 B. Bias = 1.5, w1 = 2, w2 = 2 C. Bias = 1, w1 = 1.5, w2 = 1. D. None of these Q4. A network is created when we multiple neurons stack together. Let us take an example of a neural network simulating an XNOR function. You can see that the last neuron takes input from two neurons before it. The activation function for all the neurons is given by:

Suppose X1 is 0 and X2 is 1, what will be the output for the above neural network? A. 0 B. 1 Q5. In a neural network, knowing the weight and bias of each neuron is the most important step. If you can somehow get the correct value of weight and bias for each neuron, you can approximate any function. What would be the best way to approach this? A. Assign random values and pray to God they are correct B. Search every possible combination of weights and biases till you get the best value C. Iteratively check that after assigning a value how far you are from the best values, and slightly change the assigned values values to make them better D. None of these Q6. What are the steps for using a gradient descent algorithm?

**1. Calculate error between the actual value and the predicted value

2. Reiterate until you find the best weights of network
3. Pass an input through the network and get values from output layer
4. Initialize random weight and bias
5. Go to each neurons which contributes to the error and change its respective** values to reduce the error A. 1, 2, 3, 4, 5 B. 5, 4, 3, 2, 1 C. 3, 2, 1, 5, 4 D. 4, 3, 1, 5, 2

Given above is a description of a neural network. When does a neural network model become a deep learning model? A. When you add more hidden layers and increase depth of neural network B. When there is higher dimensionality of data C. When the problem is an image recognition problem D. None of these Q13. In training a neural network, you notice that the loss does not decrease in the few starting epochs. The reasons for this could be:

**1. The learning is rate is low

2. Regularization parameter is high
3. Stuck at local minima What according to you are the probable reasons?** A. 1 and 2 B. 2 and 3 C. 1 and 3 D. Any of these Q14. Which of the following is true about model capacity (where model capacity means

the ability of neural network to approximate complex functions)? A. As number of hidden layers increase, model capacity increases B. As dropout ratio increases, model capacity increases C. As learning rate increases, model capacity increases D. None of these Q15. If you increase the number of hidden layers in a Multi Layer Perceptron, the classification error of test data always decreases. True or False? A. True B. False Q16. You are building a neural network where it gets input from the previous layer as well as from itself. Which of the following architecture has feedback connections? A. Recurrent Neural network B. Convolutional Neural Network C. Restricted Boltzmann Machine D. None of these Q17. What is the sequence of the following tasks in a perceptron?

C. Fully Connected Neural Network D. Both A and B Q27. In a neural network, which of the following techniques is used to deal with overfitting? A. Dropout B. Regularization C. Batch Normalization D. All of these Q28. Y = ax^2 + bx + c (polynomial equation of degree 2) Can this equation be represented by a neural network of single hidden layer with linear threshold? A. Yes B. No Q30. Which of the following statement is the best description of early stopping? A. Train the network until a local minimum in the error function is reached B. Simulate the network on a test dataset after every epoch of training. Stop training when the generalization error starts to increase C. Add a momentum term to the weight update in the Generalized Delta Rule, so that training converges more quickly D. A faster version of backpropagation, such as the `Quickprop’ algorithm Q31. What if we use a learning rate that’s too large? A. Network will converge B. Network will not converge Q34. When pooling layer is added in a convolutional neural network, translation in- variance is preserved. True or False?

A. True B. False Q36. The graph represents gradient flow of a four-hidden layer neural network which is trained using sigmoid activation function per epoch of training. The neural network suffers with the vanishing gradient problem. Which of the following statements is true? A. Hidden layer 1 corresponds to D, Hidden layer 2 corresponds to C, Hidden layer 3 corresponds to B and Hidden layer 4 corresponds to A B. Hidden layer 1 corresponds to A, Hidden layer 2 corresponds to B, Hidden layer 3 corresponds to C and Hidden layer 4 corresponds to D Q37. For a classification task, instead of random weight initializations in a neural network, we set all the weights to zero. Which of the following statements is true? A. There will not be any problem and the neural network will train properly B. The neural network will train but all the neurons will end up recognizing the same thing C. The neural network will not train as there is no net gradient change D. None of these Q39. For an image recognition problem (recognizing a cat in a photo), which

Q44. Consider the scenario. The problem you are trying to solve has a small amount of data. Fortunately, you have a pre-trained neural network that was trained on a similar problem. Which of the following methodologies would you choose to make use of this pre-trained network? A. Re-train the model for the new dataset B. Assess on every layer how the model performs and only select a few of them C. Fine tune the last couple of layers only D. Freeze all the layers except the last, re-train the last layer 2.Which of the following is true (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. A. All of these B. (ii) and (iii) are true C. (i), (ii) and (iii) are true D. None of these 3.Which of the following is true for neural networks (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in operation to biological ones. A. All of these B. (ii) is true C. (i) and (ii) are true D. None of these 4.What are the advantages of neural networks over conventional computers (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ‘computational’ rates A. (i) and (ii) are true B. (i) and (iii) are true C. Only (i) D. All of these 5.Which of the following is true Single layer associative neural networks do not have the ability to (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a picture are connected or not

A. (ii) and (iii) are true B. (ii) is true C. All of these D. None of these 6.Which is true for neural networks A. It has set of nodes and connections B. Each node computes it’s weighted input C. Node could be in excited state or non-excited state D. All of these 7.Why is the XOR problem exceptionally interesting to neural network researchers A. Because it can be expressed in a way that allows you to use a neural network B. Because it is complex binary operation that cannot be solved using neural networks C. Because it can be solved by a single layer perceptron D. Because it is the simplest linearly inseparable problem that exists.

8. Neural Networks are complex with many parameters A. Linear Functions B. Nonlinear Functions C. Discrete Functions D. Exponential Functions 14.A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0. A. True B. False C. Sometimes – it can also output intermediate values as well D. Can’t say
9. The name for the function in question 16 is A. Step function B. Heaviside function C. Logistic function D. Perceptron function
10. The network that involves backward links from output to the input and hidden layers is called as. A. Self organizing maps B. Perceptrons