Answer / clara

a

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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 Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned

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What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?

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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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How human brain works?

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Explain Generative Adversarial Network.

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What are artificial neural networks?

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I need a MATLAB source code to recognize different regular geometric shapes such as: squares,rectangles,triangles,circles and ellipses in different sizes using neural network. All of the images containing these shapes should be in binary format with the size of 300*400 pixels. would you please give me a MATLAB code to detect these geometric shapes?

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Are neural networks helpful in medicine?

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Which is the similar operation performed by the drop-out in neural network?

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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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