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 can you do with an nn and what not?

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Explain Generative Adversarial Network.

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What is artificial intelligence neural networks?

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

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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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What are conjugate gradients, levenberg-marquardt, etc.?

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What is the difference between a Feedforward Neural Network and Recurrent Neural 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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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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How does ill-conditioning affect nn training?

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How are weights initialized in a network?

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