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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
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
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
4. The process of adjusting the weight is known as?
A . activation
B . synchronisation
C . learning
D . none of the mentioned
5. What is adaline in neural networks?
A . adaptive linear element
B . automatic linear element
C . adaptive line element
D . none of the mentioned
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
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
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
9. Learning is a?
A . slow process
B . fast process
C . can be slow or fast in general
D . can`t say
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
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
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
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
14. What are general limitations of back propagation rule?
A . local minima problem
B . slow convergence
C . scaling
D . all of the mentioned
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
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
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
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
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
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
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
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
Fill in the Blanks
23. The junctions that allow signal transmission between the axons terminals and dendrites are called ______________
24. In Neural Networks learning processes, Learning with a teacher is also referred to as _________ learning
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.
26. The __________ is used to control the amount of weight adjustment at each step of training.
27. The weight updating in case of perceptron learning, if y≠t is _______________
28. Madaline stands for ________________
29. In Adaline learning rule is found to minimize the _________ error between the activation and the target value.
30. The training of the Back Propagation Network is done in ___________ stages
31. CAM stands for ________________
32. In the _________ associative memory network, the training input vector and training output vector are the same.
33. The BAM is a __________ associative pattern-marching network that encodes binary or bipolar patterns using Hebbian learning rule
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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