[amp_mcq option1=”Because they are the only class of problem that network can solve successfully” option2=”Because they are the only class of problem that Perceptron can solve successfully” option3=”Because they are the only mathematical functions that are continue” option4=”Because they are the only mathematical functions you can draw” correct=”option2″]
The correct answer is: B. Because they are the only class of problem that Perceptron can solve successfully.
A Perceptron is a type of artificial neural network that can only learn to classify linearly separable data. This means that the data must be able to be separated into two classes by a straight line. If the data is not linearly separable, then a Perceptron will not be able to learn to classify it correctly.
Neural network researchers are interested in linearly separable problems because they are a relatively simple type of problem that can be used to test the performance of neural networks. If a neural network can learn to classify linearly separable data correctly, then it is more likely to be able to learn to classify more complex data correctly.
Options A, C, and D are incorrect because they are not true. Neural networks can solve problems that are not linearly separable, and they can also solve problems that are not mathematical functions.