In ____ the goal is for the software to use what it has learned in one area to solve problems in other areas. A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

In ____ the goal is for the software to use what it has learned in one area to solve problems in other areas. A. Machine Learning B. Deep Learning C. Neural Networks D. None of these
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B. Deep Learning 
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The goal is for the software to use what is it is was learned in one area to solve problem in other area. (a) Machine learning (b) Deep learning (c) Neural network (d) All of above

Description : The goal is for the software to use what is it is was learned in one area to solve problem in other area. (a) Machine learning (b) Deep learning (c) Neural network (d) All of above

Last Answer : (b) Deep learning

Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structured and function of the brain called _____. A. Machine learning B. Artificial neural networks C. Deep learning D. Robotics

Description : Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structured and function of the brain called _____. A. Machine learning B. Artificial neural networks C. Deep learning D. Robotics

Last Answer : B. Artificial neural networks 

______________is a branch of science that deals with programing the systems in such a way that they automatically learn and improve with experience A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Description : ______________is a branch of science that deals with programing the systems in such a way that they automatically learn and improve with experience A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Last Answer : A. Machine Learning 

Computer programs that mimic the way the human brain processes information is called as A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Description : Computer programs that mimic the way the human brain processes information is called as A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Last Answer : The correct answer is: C. Neural Networks Neural Networks are computer programs or models that are designed to mimic the way the human brain processes information. They are a subset of ... advanced form of neural networks that uses multiple layers of artificial neurons to perform complex tasks.

Computer programs that mimic the way the human brain processes information is called as:- a) Machine learning b) Deep learning c)Neural networks d) None of the above

Description : Computer programs that mimic the way the human brain processes information is called as:- a) Machine learning b) Deep learning c)Neural networks d) None of the above

Last Answer : c)Neural networks

Which of the following is the model used for learning? a) Decision trees b) Neural networks c) Propositional and FOL rules d) All of the mentioned

Description : Which of the following is the model used for learning? a) Decision trees b) Neural networks c) Propositional and FOL rules d) All of the mentioned

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PROLOG, LISP, NLP are the language of ____ A. Artificial Intelligence B. Machine Learning C. Internet of Things D. Deep Learning

Description : PROLOG, LISP, NLP are the language of ____ A. Artificial Intelligence B. Machine Learning C. Internet of Things D. Deep Learning

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Why are linearly separable problems of interest of neural network researchers? a) Because they are the only class of problem that network can solve successfully b) Because they are the only class of problem that Perceptron can solve successfully c) Because they are the only mathematical functions that are continue d) Because they are the only mathematical functions you can draw

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Last Answer : b) Because they are the only class of problem that Perceptron can solve successfully

Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Description : Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex ... solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Last Answer : d) Because it is the simplest linearly inseparable problem that exists.

This set of Artificial Intelligence MCQs focuses on “Neural Networks – 2”. 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Description : This set of Artificial Intelligence MCQs focuses on Neural Networks - 2 . 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in ... by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Last Answer : d) Because it is the simplest linearly inseparable problem that exists.

A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. a) Decision tree b) Graphs c) Trees d) Neural Networks

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Last Answer : a) Decision tree

Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

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Last Answer : a) Linear Functions

Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

Last Answer : d) All of the mentioned

Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a picture are connected or not a) (ii) and (iii) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Last Answer : a) (ii) and (iii) are true

What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ‘computational’ rates a) (i) and (ii) are true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Last Answer : d) All of the mentioned

Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. a) All of the mentioned are true b) (ii) and (iii) are true c) (i), (ii) and (iii) are true d) None of the mentioned

Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned

Last Answer : a) All of the mentioned are true

Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : b) Nonlinear Functions

Core of soft Computing is A. Fuzzy Computing, Neural Computing, Genetic Algorithms B. Fuzzy Networks and Artificial Intelligence C. Artificial Intelligence and Neural Science D. Neural Science and Genetic Science

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Last Answer : D. Neural Science and Genetic Science

Perceptron is A. General class of approaches to a problem. B. Performing several computations simultaneously C. Structures in a database those are statistically relevant D. Simple forerunner of modern neural networks, without hidden layers

Description : Perceptron is A. General class of approaches to a problem. B. Performing several computations simultaneously C. Structures in a database those are statistically relevant D. Simple forerunner of modern neural networks, without hidden layers

Last Answer : D. Simple forerunner of modern neural networks, without hidden layers

Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Description : Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Last Answer : b) Nonlinear Functions

Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

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Last Answer : a) Linear Functions

Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

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Last Answer : d) All of the mentioned

Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a picture are connected or not a) (ii) and (iii) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Last Answer : a) (ii) and (iii) are true

What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ‘computational’ rates a) (i) and (ii) are true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Last Answer : d) All of the mentioned

Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in operation to biological ones. a) All of the mentioned b) (ii) is true c) (i) and (ii) are true d) None of the mentioned

Description : Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in ... b) (ii) is true c) (i) and (ii) are true d) None of the mentioned

Last Answer : c) (i) and (ii) are true

Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. a) All of the mentioned are true b) (ii) and (iii) are true c) (i), (ii) and (iii) are true d) None of the mentioned

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Last Answer : a) All of the mentioned are true

What is multilayer feed forward neural networks?

Description : What is multilayer feed forward neural networks?

Last Answer : It consists of many hidden units. Each hidden unit act as a perceptron that represents a soft threshold functions in the input space. Output unit act as a soft threshold linear combination of several such functions.

What are the two functions in Neural network’s Activation functions?

Description : What are the two functions in Neural network’s Activation functions? 

Last Answer : i. Threshold function  ii. Sigmoid function

Define Neural Networks.

Description : Define Neural Networks.

Last Answer : It consists of nodes or units connected by directed links. A link propagates the activation. Each link has a numeric weight which determines the strength and sign of the connection.

Membership function can be thought of as a technique to solve empirical problems on the basis of A. knowledge B. examples C. learning D. experience

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Last Answer : D. experience

The given illustrative diagram is related to which topic? a) Machine Learning b) Neural Network Processing c) Reasoning d) Deep learning

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Last Answer : a) Machine Learning

he given illustrative diagram is related to which topic? a) Machine Learning b) Neural Network Processing c) Reasoning d) Deep learning

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Last Answer : Machine Learning

Is branch of science that deals with programming the system in such that they automatically learn with experience (a) Machine learning (b) Deep earning (c) Neural learning (d) All of above

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Last Answer : (a) Machine learning

the consist of computer program that mimic the way the human brain process information. (a) Machine learning (b) Deep learning (c) Neural learning (d) All of this

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Last Answer : (c) Neural learning

K-means, self-organizing maps, hierarchical clustering are the example of _____. A. Supervised learning B. Unsupervised learning C. Machine learning D. Deep learning

Description : K-means, self-organizing maps, hierarchical clustering are the example of _____. A. Supervised learning B. Unsupervised learning C. Machine learning D. Deep learning

Last Answer : B. Unsupervised learning 

Classifying email as a spam, labeling webpages based on their content, voice recognition are the example of _____. A. Supervised learning B. Unsupervised learning C. Machine learning D. Deep learning

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Last Answer : A. Supervised learning 

The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

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Last Answer : c) Recurrent neural network

IBM Watson supercomputer comes under ____ AI. A. Narrow AI B. General AI C. Neural AI D. None of above

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Weak AI is also known as ____ A. Narrow AI B. General AI C. Neural AI D. None of above

Description : Weak AI is also known as ____ A. Narrow AI B. General AI C. Neural AI D. None of above

Last Answer : A. Narrow AI 

Which of the following neural networks uses supervised learning? (A) Multilayer perceptron (B) Self organizing feature map (C) Hopfield network (1) (A) only (2) (B) only (3) (A) and (B) only (4) (A) and (C) only

Description : Which of the following neural networks uses supervised learning? (A) Multilayer perceptron (B) Self organizing feature map (C) Hopfield network (1) (A) only (2) (B) only (3) (A) and (B) only (4) (A) and (C) only

Last Answer : (1) (A) only

What is Neuro software? a) A software used to analyze neurons b) It is powerful and easy neural network

Description : What is Neuro software? a) A software used to analyze neurons b) It is powerful and easy neural network

Last Answer : b) It is powerful and easy neural network

Neuro software is: a) A software used to analyze neurons b) It is powerful and easy neural network c) Designed to aid experts in real world d) It is software used by Neuro surgeon

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Last Answer : b) It is powerful and easy neural network

Which algorithm will work backward from the goal to solve a problem? a) Forward chaining b) Backward chaining c) Hill-climb algorithm d) None of the mentioned

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Last Answer : c) All of the mentioned

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Shaping teaching techniques to fit the learning patterns of individual students is the goal of __________ a) decision support b) automatic programming c) intelligent computer-assisted instruction d) expert systems

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Last Answer : c) intelligent computer-assisted instruction

Which of the following is the component of learning system? a) Goal b) Model c) Learning rules d) All of the mentioned

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Last Answer : d) All of the mentioned

The expert system developed at MIT to solve mathematical problems is known as ___________ a) RAND b) ISIS c) MACSYMA d) MOLGEN

Description : The expert system developed at MIT to solve mathematical problems is known as ___________ a) RAND b) ISIS c) MACSYMA d) MOLGEN

Last Answer : c) MACSYMA

An AI system developed by Daniel Bobrow to read and solve algebra word problems. a) SHRDLU b) SIMD c) BACON d) STUDENT

Description : An AI system developed by Daniel Bobrow to read and solve algebra word problems. a) SHRDLU b) SIMD c) BACON d) STUDENT

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ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems. A. True B. False C. D.

Description : ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems. A. True B. False C. D.

Last Answer : A. True