Answer / clara

b

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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Who is concerned with nns?

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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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What is Pooling in CNN and how does it work?

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What is the difference between a Feedforward Neural Network and Recurrent Neural Network?

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How human brain works?

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Explain in detail Neural Networks?

4 Answers   TCS,

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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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How does an LSTM network work?

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How does ill-conditioning affect nn training?

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How artificial neural networks can be applied in future?

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List some commercial practical applications of artificial neural networks?

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