⟩ Explain Artificial Neural Networks (ANNs)?
A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
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Explain Neural Networks?
Suppose we need a MATLAB source code to recognize different regular geometric shapessuch 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.Give us a MATLAB code to detect these geometric shapes?
What is Artificial Intelligence Neural Networks?
A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0. a) True b) False c) Sometimes - it can also output intermediate values as well d) Can't say
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
Which of the following is not the promise of artificial neural network? a) It can explain result b) It can survive the failure of some nodes c) It has inherent parallelism d) It can handle noise
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
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
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
