Explore topic-wise MCQs in Neural Networks.

This section includes 12 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Networks knowledge and support exam preparation. Choose a topic below to get started.

1.

For noisy input vectors, Hebb methodology of learning can be employed?

A. yes
B. no
Answer» C.
Discussion
2.

Number of output cases depends on what factor?

A. number of inputs
B. number of distinct classes
C. total number of classes
D. none of the mentioned
Answer» C. total number of classes
Discussion
3.

In determination of weights by learning, for noisy input vectors what kind of learning should be employed?

A. hebb learning law
B. widrow learning law
C. hoff learning law
D. no learning law
Answer» E.
Discussion
4.

NUMBER_OF_OUTPUT_CASES_DEPENDS_ON_WHAT_FACTOR??$

A. number of inputs
B. number of distinct classes
C. total number of classes
D. none of the mentioned
Answer» C. total number of classes
Discussion
5.

For_noisy_input_vectors,_Hebb_methodology_of_learning_can_be_employed?$

A. yes
B. no
Answer» C.
Discussion
6.

By using only linear processing units in output layer, can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?

A. yes
B. no
Answer» C.
Discussion
7.

Can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?

A. yes
B. no
Answer» B. no
Discussion
8.

what are affine transformations?

A. addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern.
B. addition of bias term (+1) which results in arbitrary rotation, scaling, translation of input pattern.
C. addition of bias term (-1) or (+1) which results in arbitrary rotation, scaling, translation of input pattern.
D. none of the mentioned
Answer» B. addition of bias term (+1) which results in arbitrary rotation, scaling, translation of input pattern.
Discussion
9.

What is the features that cannot be accomplished earlier without affine transformations?

A. arbitrary rotation
B. scaling
C. translation
D. all of the mentioned
Answer» D. all of the mentioned
Discussion
10.

What are the features that can be accomplished using affine transformations?

A. arbitrary rotation
B. scaling
C. translation
D. all of the mentioned
Answer» E.
Discussion
11.

In determination of weights by learning, for linear input vectors what kind of learning should be employed?

A. hebb learning law
B. widrow learning law
C. hoff learning law
D. no learning law
Answer» C. hoff learning law
Discussion
12.

In determination of weights by learning, for orthogonal input vectors what kind of learning should be employed?

A. hebb learning law
B. widrow learning law
C. hoff learning law
D. no learning law
Answer» B. widrow learning law
Discussion