How are nns related to statistical methods?

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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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How many kinds of kohonen networks exist?

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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 are neural networks? What are the types of neural networks?

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How are artificial neural networks different from normal computers?

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

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What are the applications of a Recurrent Neural Network (RNN)?

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What is the role of activation functions in a Neural Network?

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Explain Generative Adversarial Network.

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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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 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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