1. What is the objective of backpropagation algorithm?

a) to develop learning algorithm for multilayer feedforward neural network

b) to develop learning algorithm for single layer feedforward neural network

c) to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly

d) none of the mentioned

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2. The backpropagation law is also known as generalized delta rule, is it true?

a) yes

b) no

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3. What is true regarding backpropagation rule?

a) it is also called generalized delta rule

b) error in output is propagated backwards only to determine weight updates

c) there is no feedback of signal at nay stage

d) all of the mentioned

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4. There is feedback in final stage of backpropagation algorithm?

a) yes

b) no

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5. What is true regarding backpropagation rule?

a) it is a feedback neural network

b) actual output is determined by computing the outputs of units for each hidden layer

c) hidden layers output is not all important, they are only meant for supporting input and output layers

d) none of the mentioned

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6. What is meant by generalized in statement “backpropagation is a generalized delta rule” ?

a) because delta rule can be extended to hidden layer units

b) because delta is applied to only input and output layers, thus making it more simple and generalized

c) it has no significance

d) none of the mentioned

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7. What are general limitations of back propagation rule?

a) local minima problem

b) slow convergence

c) scaling

d) all of the mentioned

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8. What are the general tasks that are performed with backpropagation algorithm?

a) pattern mapping

b) function approximation

c) prediction

d) all of the mentioned

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9. Does backpropagaion learning is based on gradient descent along error surface?

a) yes

b) no

c) cannot be said

d) it depends on gradient descent but not error surface

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10. How can learning process be stopped in backpropagation rule?

a) there is convergence involved

b) no heuristic criteria exist

c) on basis of average gradient value

d) none of the mentioned

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