This set of Neural Networks Inteview Questions and Answers for freshers focuses on “Learning Basics – 2”.
1. 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
View Answer
Explanation: Supervised learning is based on weight adjustment based on deviation of desired output from actual output.
2. Supervised learning may be used for?
a) temporal learning
b) structural learning
c) both temporal & structural learning
d) none of the mentioned
View Answer
Explanation: Supervised learning may be used for both temporal & structural learning.
3. 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
View Answer
Explanation: Structural learning deals with learning the overall structure of network in a macroscopic view.
4. 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
View Answer
Explanation: Temporal learning is concerned with capturing weight relationships.
5. 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
View Answer
Explanation: Unsupervised learning is purely based on adjustment based on local information available to weights.
6. Learning methods can only be online?
a) yes
b) no
View Answer
Explanation: Learning can be offline too.
7. Online learning allows network to incrementally adjust weights continuously?
a) yes
b) no
View Answer
Explanation: Follows from basic definition of online learning.
8. What is nature of input in activation dynamics?
a) static
b) dynamic
c) both static & dynamic
d) none of the mentioned
View Answer
Explanation: Input is fixed throughout the dynamics.
9. Adjustments in activation is slower than that of synaptic weights?
a) yes
b) no
View Answer
Explanation: Adjustments in activation is faster than that of synaptic weights.
10. 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
View Answer
Explanation: Refer to weight equation of synaptic dynamic model.
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