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Objective Type Questions & Answers


Neural Networks and Deep Learning-Unit-1 Objective Type Questions



1. What is purpose of Axon?

A . receptors

B . transmitter

C . transmission

D . none of the mentioned

Answer



2. When the cell is said to be fired?

A . if potential of body reaches a steady threshold values

B . if there is impulse reaction

C . during upbeat of heart

D . none of the mentioned

Answer



3. The amount of output of one unit received by another unit depends on what?

A . output unit

B . input unit

C . activation value

D . weight

Answer



4. The process of adjusting the weight is known as?

A . activation

B . synchronisation

C . learning

D . none of the mentioned

Answer



5. What is adaline in neural networks?

A . adaptive linear element

B . automatic linear element

C . adaptive line element

D . none of the mentioned

Answer



6. In adaline model what is the relation between output & activation value(x

A . linear

B . nonlinear

C . can be either linear or non-linear

D . none of the mentioned

Answer



7. what is the another name of weight update rule in adaline model based on its functionality?

A . LMS error learning law

B . gradient descent algorithm

C . both LMS error & gradient descent learning law

D . none of the mentioned

Answer



8. State whether Hebb’s law is supervised learning or of unsupervised type?

A . supervised

B . unsupervised

C . either supervised or unsupervised

D . can be both supervised & unsupervised

Answer



9. Learning is a?

A . slow process

B . fast process

C . can be slow or fast in general

D . can`t say

Answer



10. What is supervised learning?

A . weight adjustment based on deviation of desired output from actual output

B . weight adjustment based on desired output only

C . weight adjustment based on actual output only

D . none of the mentioned

Answer



11. What is unsupervised learning?

A . weight adjustment based on deviation of desired output from actual output

B . weight adjustment based on desired output only

C . weight adjustment based on local information available to weights

D . none of the mentioned

Answer



12. 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

Answer



13. 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

Answer



14. What are general limitations of back propagation rule?

A . local minima problem

B . slow convergence

C . scaling

D . all of the mentioned

Answer



15. 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

Answer



16. 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

Answer



17. What is the objective of associative memories?

A . to store patters

B . to recall patterns

C . to store association between patterns

D . to store association between patterns for later recall of one of patterns given the other

Answer



18. What is objective of linear autoassociative feedforward networks?

A . to associate a given pattern with itself

B . to associate a given pattern with others

C . to associate output with input

D . none of the mentioned

Answer



19. What is the objective of a pattern storage task in a network?

A . to store a given set of patterns

B . to recall a give set of patterns

C . both to store and recall

D . none of the mentioned

Answer



20. If the weight matrix stores the given patterns, then the network becomes?

A . autoassoiative memory

B . heteroassociative memory

C . multidirectional assocative memory

D . temporal associative memory

Answer



21. If the weight matrix stores association between a pair of patterns, then network becomes?

A . autoassoiative memory

B . heteroassociative memory

C . multidirectional assocative memory

D . temporal associative memory

Answer



22. What is the objective of BAM?

A . to store pattern pairs

B . to recall pattern pairs

C . to store a set of pattern pairs and they can be recalled by giving either of pattern as input

D . none of the mentioned

Answer



Fill in the Blanks


23. The junctions that allow signal transmission between the axons terminals and dendrites are called ______________

Answer


24. In Neural Networks learning processes, Learning with a teacher is also referred to as _________ learning

Answer


25. ___________ Learning is a feedback-based Network technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions.

Answer


26. The __________ is used to control the amount of weight adjustment at each step of training.

Answer


27. The weight updating in case of perceptron learning, if y≠t is _______________

Answer


28. Madaline stands for ________________

Answer


29. In Adaline learning rule is found to minimize the _________ error between the activation and the target value.

Answer


30. The training of the Back Propagation Network is done in ___________ stages

Answer


31. CAM stands for ________________

Answer


32. In the _________ associative memory network, the training input vector and training output vector are the same.

Answer


33. The BAM is a __________ associative pattern-marching network that encodes binary or bipolar patterns using Hebbian learning rule

Answer




Relevant Materials :

Neural Networks and Deep Learning-Unit-1 Objective Type Questions - [ NNDL ]

Neural Networks and Deep Learning-Unit-2 Objective Type Questions - [ NNDL ]

Neural Networks and Deep Learning-Unit-3 Objective Type Questions - [ NNDL ]

Neural Networks and Deep Learning-Unit-4 Objective Type Questions - [ NNDL ]


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