1. Why do we need biological neural networks?
a) to solve tasks like machine vision & natural language processing
b) to apply heuristic search methods to find solutions of problem
c) to make smart human interactive & user friendly system
d) all of the mentioned
Explanation:These are the basic aims that a neural network achieve.
2. What is the trend in software nowadays?
a) to bring computer more & more closer to user
b) to solve complex problems
c) to be task specific
d) to be versatile
Explanation: Software should be more interactive to the user, so that it can understand its problem in a better fashion.
3. What’s the main point of difference between human & machine intelligence?
a) human perceive everything as a pattern while machine perceive it merely as data
b) human have emotions
c) human have more IQ & intellect
d) human have sense organs
Explanation: Humans have emotions & thus form different patterns on that basis, while a machine(say computer) is dumb & everything is just a data for him.
4. What is auto-association task in neural networks?
a) find relation between 2 consecutive inputs
b) related to storage & recall task
c) predicting the future inputs
d) none of the mentioned
Explanation:This is the basic definition of auto-association in neural networks.
5. Does pattern classification belongs to category of non-supervised learning?
a) yes
b) no
Explanation: Pattern classification belongs to category of supervised learning.
6. In pattern mapping problem in neural nets, is there any kind of generalization involved between input & output?
a) yes
b) no
Explanation: The desired output is mapped closest to the ideal output & hence there is generalisation involved
7. What is unsupervised learning?
a) features of group explicitly stated
b) number of groups may be known
c) neither feature & nor number of groups is known
d) none of the mentioned
Explanation: Basic definition of unsupervised learning.
8. Does pattern classification & grouping involve same kind of learning?
a) yes
b) no
Explanation: Pattern classification involves supervised learning while grouping is an unsupervised one.
9. Does for feature mapping there’s need of supervised learning?
a) yes
b) no
Explanation:Feature mapping can be unsupervised, so it’s not a sufficient condition.
10. Example of a unsupervised feature map?
a) text recognition
b) voice recognition
c) image recognition
d) none of the mentioned
Explanation: Since same vowel may occur in different context & its features vary over overlapping regions of different vowel