Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results. a) True – this works always, and these multiple perceptrons learn to classify even complex problems. b) False – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do c) True – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded d) False – just having a single perceptron is enough

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Explain Generative Adversarial Network.

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The name for the function in question 16 is a) Step function b) Heaviside function c) Logistic function d) Perceptron function

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Why use artificial neural networks? What are its advantages?

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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

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Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

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How are weights initialized in a network?

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What are combination, activation, error, and objective functions?

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 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 the mentioned b) (ii) is true c) (i) and (ii) are true d) None of the mentioned

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 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

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How are nns related to statistical methods?

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What is a neural network and what are some advantages and disadvantages of such a network?

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