Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

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Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

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How to avoid overflow in the logistic function?

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Are neural networks helpful in medicine?

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 A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

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What learning rate should be used for backprop?

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List some commercial practical applications of artificial neural networks?

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How are artificial neural networks different from normal computers?

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How artificial neural networks can be applied in future?

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What are cases and variables?

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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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How many kinds of nns exist?

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