Neural Networks Learning Basics MCQs : This section focuses on the "Neural Networks Learning Basics". These Multiple Choice Questions (MCQs) should be practiced to improve the Neural Networks Learning Basics skills required for various interviews (campus interview, walk-in interview, company interview), placement, entrance exam and other competitive examinations.
Question 1
Activation models are?
A. dynamic
B. static
C. deterministic
D. none of the mentioned
Question 2
Adjustments in activation is slower than that of synaptic weights?
A. yes
B. no
Question 3
How is pattern information distributed?
A. it is distributed all across the weights
B. it is distributed in localised weights
C. it is distributed in certain proctive weights only
D. none of the mentioned
Question 4
If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by?
A. xb(t)=deterministic model
B. xb(t)=deterministic model + noise component
C. xb(t)=deterministic model*noise component
D. none of the mentioned’
Question 5
Learning is a?
A. slow process
B. fast process
C. can be slow or fast in general
D. can’t say
Question 6
Learning methods can only be online?
A. yes
B. no
Question 7
Memory decay affects what kind of memory?
A. short tem memory in general
B. older memory in general
C. can be short term or older
D. none of the mentioned
Question 8
Online learning allows network to incrementally adjust weights continuously?
A. yes
B. no
Question 9
Supervised learning may be used for?
A. temporal learning
B. structural learning
C. both temporal & structural learning
D. none of the mentioned
Question 10
What are the requirements of learning laws?
A. convergence of weights
B. learning time should be as small as possible
C. learning should use only local weights
D. all of the mentioned
Question 11
What are the requirements of learning laws?
A. learning should be able to capture more & more patterns
B. learning should be able to grasp complex nonliear mappings
C. convergence of weights
D. all of the mentioned
Question 12
what does the term wij(0) represents in synaptic dynamic model?
A. a prioi knowledge
B. just a constant
C. no strong significance
D. future adjustments
Question 13
What is asynchronous update in a network?
A. update to all units is done at the same time
B. change in state of any one unit drive the whole network
C. change in state of any number of units drive the whole network
D. none of the mentioned
Question 14
What is equilibrium in neural systems?
A. deviation in present state, when small perturbations occur
B. settlement of network, when small perturbations occur
C. change in state, when small perturbations occur
D. none of the mentioned
Question 15
What is nature of input in activation dynamics?
A. static
B. dynamic
C. both static & dynamic
D. none of the mentioned
Question 16
What is structural learning?
A. concerned with capturing input-output relationship in patterns
B. concerned with capturing weight relationships
C. both weight & input-output relationships
D. none of the mentioned
Question 17
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
Question 18
What is temporal learning?
A. concerned with capturing input-output relationship in patterns
B. concerned with capturing weight relationships
C. both weight & input-output relationships
D. none of the mentioned
Question 19
What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)?
A. xb(t)=0
B. xb(t)=1
C. xb(t)=n(t), where n is noise component
D. xb(t)=n(t)+1
Question 20
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