Answer / clara

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An auto-associative network is: 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

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What is the advantage of pooling layer in convolutional neural networks?

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How are layers counted?

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 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 the mentioned d) None of the mentioned

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What are the population, sample, training set, design set, validation set, and test set?

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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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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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 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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What are cases and variables?

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How neural networks became a universal function approximators?

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The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

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What learning rate should be used for backprop?

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