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26 AI Neural Networks Questions And Answers

2⟩ Explain Neural Networks?

A neural network can be defined as a model of reasioning

based on the human brain.

The human brain incorporates nearly 10 billion neurons and

60 trillion connections,Synapses,between them.

By using multiple neurons simultaneously,the brain can

perform its functions much faster than the faster computers.

Although a single neuron has a very simple structure,an

army of such elements constitutes a tremendous processing

power.

The network which represents the connections among several

neurons is called a neural network.

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4⟩ What is Artificial Intelligence Neural Networks?

For the sake of trying to produce intelligent behavior however really all that's being done is work with artificial neural networks where each cell is a very simple processor and the goal is to try and make them work together to solve some problem. That's all that gets covered in this book. Many people are skeptical that artificial neural networks can produce human levels of performance because they are so much simpler than the biological neural networks.

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8⟩ Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in operation to biological ones. a) All of the mentioned b) (ii) is true c) (i) and (ii) are true d) None of the mentioned

c) (i) and (ii) are true

Explanation:

The training time depends on the size of the network; the number of neuron is greater and therefore the number of possible 'states' is increased. Neural networks can be simulated on a conventional computer but the main advantage of neural networks - parallel execution - is lost. Artificial neurons are not identical in operation to the biological ones.

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9⟩ 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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10⟩ 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

c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

Explanation:

Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

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11⟩ 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

d) All of the mentioned

Explanation:

Neural networks learn by example. They are more fault tolerant because they are always able to respond and small changes in input do not normally cause a change in output. Because of their parallel architecture, high computational rates are achieved.

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12⟩ 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.

d) Because it is the simplest linearly inseparable problem that exists.

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13⟩ 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

b) a neural network that contains feedback

Explanation:

An auto-associative network is equivalent to a neural network that contains feedback. The number of feedback paths(loops) does not have to be one.

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14⟩ 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

a) 238

Explanation:

The output is found by multiplying the weights with their respective inputs, summing the results and multiplying with the transfer function. Therefore:

Output = 2 * (1*4 + 2*10 + 3*5 + 4*20) = 238.

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15⟩ 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) All of the mentioned are true

Explanation:

Neural networks have higher computational rates than conventional computers because a lot of the operation is done in parallel. That is not the case when the neural network is simulated on a computer. The idea behind neural nets is based on the way the human brain works. Neural nets cannot be programmed, they cam only learn by examples.

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16⟩ 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

a) a single layer feed-forward neural network with pre-processing

Explanation:

The perceptron is a single layer feed-forward neural network. It is not an auto-associative network because it has no feedback and is not a multiple layer neural network because the pre-processing stage is not made of neurons.

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17⟩ 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

c) True - perceptrons can do this but are unable to learn to do it - they have to be explicitly hand-coded

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18⟩ 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

c) 000 or 010 or 110 or 100

Explanation:

The truth table before generalization is:

Inputs Output

000 $

001 $

010 $

011 $

100 $

101 $

110 0

111 1

where $ represents don't know cases and the output is random.

After generalization, the truth table becomes:

Inputs Output

000 0

001 1

010 0

011 1

100 0

101 1

110 0

111 1

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