How are weights initialized in a network?

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How are artificial neural networks different from normal computers?

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

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

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What are artificial neural networks?

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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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What are combination, activation, error, and objective functions?

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

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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 is simple artificial neuron?

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What are cases and variables?

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what are some advantages and disadvantages of neural network?

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