Welcome to the Learning Concepts of Neural Networks MCQs Page

Dive deep into the fascinating world of Learning Concepts of Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Learning Concepts of Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Learning Concepts of Neural Networks, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Neural Networks.

Check out the MCQs below to embark on an enriching journey through Learning Concepts of Neural Networks. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Neural Networks.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Learning Concepts of Neural Networks. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Learning Concepts of Neural Networks MCQs | Page 5 of 5

Explore more Topics under Neural Networks

01

Basics of Artificial Neural Networks

02

Activation and Synaptic Dynamics

03

Feedforward Neural Networks

04

Feedback Neural Networks

05

Competitive Learning Neural Networks

06

Applications of Artificial Neural Networks

Q41.

Correlation learning law is special case of?

Hebb learning law

Perceptron learning law

Delta learning law

LMS learning law

Discuss

Answer: (a).Hebb learning law

Q42.

Correlation learning law is what type of learning?

supervised

unsupervised

either supervised or unsupervised

both supervised or unsupervised

Discuss

Answer: (a).supervised

Q43.

Correlation learning law can be represented by equation?

∆wij= µ(si) aj

∆wij= µ(bi – si) aj

∆wij= µ(bi – si) aj Á(xi),where Á(xi) is derivative of xi

∆wij= µ bi aj

Discuss

Answer: (d).∆wij= µ bi aj

Q44.

The other name for instar learning law?

looser take it all

winner take it all

winner give it all

looser give it all

Discuss

Answer: (b).winner take it all

Q45.

The instar learning law can be represented by equation?

∆wij= µ(si) aj

∆wij= µ(bi – si) aj

∆wij= µ(bi – si) aj Á(xi),where Á(xi) is derivative of xi

∆wk= µ (a-wk), unit k with maximum output is identified

Discuss

Answer: (d).∆wk= µ (a-wk), unit k with maximum output is identified

Q46.

Is instar a case of supervised learning?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q47.

Is outstar a case of supervised learning?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q48.

Which of the following learning laws belongs to same category of learning?

hebbian, perceptron

perceptron, delta

hebbian, widrow-hoff

instar, outstar

Discuss

Answer: (b).perceptron, delta

Q49.

In hebbian learning intial weights are set?

random

near to zero

near to target value

Discuss

Answer: (b).near to zero

Page 5 of 5

Welcome to the Learning Concepts of Neural Networks MCQs Page

Learning Concepts of Neural Networks MCQs | Page 5 of 5

Explore more Topics under Neural Networks

Basics of Artificial Neural Networks

Activation and Synaptic Dynamics

Feedforward Neural Networks

Feedback Neural Networks

Competitive Learning Neural Networks

Applications of Artificial Neural Networks

Suggested Topics

Object Oriented Programming Using C++

Python

Artificial Intelligence

PHP

Computer Architecture

Reverse Engineering

Hadoop

MongoDB

DBMS