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

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
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D.  Simple forerunner of modern neural networks, without hidden layers
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The network that involves backward links from output to the input and hidden layers is called _________ a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

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

Last Answer : c) Recurrent neural network

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

Description : 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

Last Answer : c) Recurrent neural network

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.

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

What is perceptron? a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

Description : What is perceptron? a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

Last Answer : a) a single layer feed-forward neural network with pre-processing

A perceptron is a ______________ a) Feed-forward neural network b) Backpropagation algorithm c) Backtracking algorithm d) Feed Forward-backward algorithm

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A perceptron is a ——————————–. a) Feed-forward neural network b) Back-propagation algorithm c) Back-tracking algorithm d) Feed Forward-backward algorithm e) Optimal algorithm with Dynamic programming

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Last Answer : a) Feed-forward neural network

A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

Description : A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

Last Answer : a) a single layer feed-forward neural network with pre-processing

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

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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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 ... ) 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

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Last Answer : b) Nonlinear Functions

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

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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

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

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Last Answer : d) All of the mentioned

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

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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.

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

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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

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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

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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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Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results. a) True – this works always, and these multiple perceptrons learn to classify even complex problems. b) False – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do c) True – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded d) False – just having a single perceptron is enough

Description : Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results. ... have to be explicitly hand-coded d) False - just having a single perceptron is enough

Last Answer : c) True – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded

What are the categories of neural network structures?

Description : What are the categories of neural network structures? 

Last Answer : i. Acyclic (or) Feed – forward networks  ii. Cyclic (or) Recurrent Networks

_________ is an artificial neural network with multiple hidden layers between the input and output layers? (A) Deep neural network (B) Shallow neural network (C) Both(A) & (B) (D) None of the Above

Description : _________ is an artificial neural network with multiple hidden layers between the input and output layers? (A) Deep neural network (B) Shallow neural network (C) Both(A) & (B) (D) None of the Above

Last Answer : (A) Deep neural network

What is the name of the function in the following statement “A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0”? a) Step function b) Heaviside function c) Logistic function d) Perceptron function

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Last Answer : b) Heaviside function

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Last Answer : a) True

What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn d) None of the mentioned

Description : What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the ... the network to allow weights to be adjusted so that the network can learn d) None of the mentioned

Last Answer : c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn

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Description : The name for the function in question 16 is a) Step function b) Heaviside function c) Logistic function d) Perceptron function

Last Answer : b) Heaviside function

What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. d) None of the mentioned

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Last Answer : c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

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Description : what was the forerunner of the modern camera and what was it mostly used for?

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Last Answer : A. Narrow AI 

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Last Answer : A. Narrow AI 

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Last Answer : a) True