Why Are Linearly Separable Problems Of Interest Of Neural Network Researchers? A) Because They Are The Only Class Of Problem That Network Can Solve Successfully B) Because They Are The Only Class Of Problem That Perceptron Can Solve Successfully C) Because They Are The Only Mathematical Functions That Are Continue D) Because They Are The Only Mathematical Functions You Can Draw

⟩ Why are linearly separable problems of interest of neural network researchers? a) Because they are the only class of problem that network can solve successfully b) Because they are the only class of problem that Perceptron can solve successfully c) Because they are the only mathematical functions that are continue d) Because they are the only mathematical functions you can draw

b) Because they are the only class of problem that Perceptron can solve successfully

Explanation:

Linearly separable problems of interest of neural network researchers because they are the only class of problem that Perceptron can solve successfully

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What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. d) None of the mentioned

What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high 'computational' rates a) (i) and (ii) are true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

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.

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

A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 5 and 20 respectively. The output will be a) 238 b) 76 c) 119 d) 123

Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. a) All of the mentioned are true b) (ii) and (iii) are true c) (i), (ii) and (iii) are true d) None of the mentioned

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

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

A 3-input neuron is trained to output a zero when the input is 110 and a one when the input is 111. After generalization, the output will be zero when and only when the input is a) 000 or 110 or 011 or 101 b) 010 or 100 or 110 or 101 c) 000 or 010 or 110 or 100 d) 100 or 111 or 101 or 001

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