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

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
by

1 Answer

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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Related questions

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

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

Description : 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

Last Answer : a) Feed-forward 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

Description : 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

Last Answer : b) Heaviside function

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) True b) False c) Sometimes – it can also output intermediate values as well d) Can’t say

Description : 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) True b) False c) Sometimes – it can also output intermediate values as well d) Can’t say

Last Answer : a) True

A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 5 and 20 respectively. The output will be: a) 238 b) 76 c) 119 d) 123

Description : A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 5 and 20 respectively. The output will be: a) 238 b) 76 c) 119 d) 123

Last Answer : a) 238

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

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

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.

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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Last Answer : a) Feed-forward 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 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

Description : 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 ... mathematical functions that are continue d) Because they are the only mathematical functions you can draw

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

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 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

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

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

A perceptron has input weights W1 = -3.9 and W2 = 1.1 with threshold value T = 0.3. What output does it give for the input x1 = 1.3 and x2 = 2.2? (A) -2.65 (B) -2.30 (B) 0 (D) 1

Description : A perceptron has input weights W1 = -3.9 and W2 = 1.1 with threshold value T = 0.3. What output does it give for the input x1 = 1.3 and x2 = 2.2? (A) -2.65 (B) -2.30 (B) 0 (D) 1

Last Answer : Answer: C

In artificial Neural Network interconnected processing elements are called A. nodes or neurons B. weights C. axons D. Soma

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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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Each connection link in ANN is associated with ________ which has information about the input signal. A. neurons B. weights C. bias D. activation function

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An artificial neurons receives n inputs x1, x2,...,xn with weights w1,w2,...,wn attached to the input links. The weighted sum ............... is computed to be passed on to a non-linear filter ϕ called activation function to release the output. (A) Σ wi (B) Σ xi (C) Σ wi + Σ xi (D) Σ wi . Σ xi

Description : An artificial neurons receives n inputs x1, x2,...,xn with weights w1,w2,...,wn attached to the input links. The weighted sum ............... is computed to be passed on to a non-linear filter ϕ called activation function to release the output. (A) Σ wi (B) Σ xi (C) Σ wi + Σ xi (D) Σ wi . Σ xi

Last Answer : (D) Σ wi . Σ xi

Internal state of neuron is called __________, is the function of the inputs the neurons receives A. Weight B. activation or activity level of neuron C. Bias D. None of these

Description : Internal state of neuron is called __________, is the function of the inputs the neurons receives A. Weight B. activation or activity level of neuron C. Bias D. None of these

Last Answer : B. activation or activity level of neuron

. In an Unsupervised learning ____________ a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given

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Last Answer : b) Specific output values are not given

In an Unsupervised learning a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given e) Neither inputs nor outputs are given

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Last Answer : b) Specific output values are not given

What is meant by persistence actions? a) Allow a literal to remain false b) Allow a literal to remain true c) Allow a literal to remain false & true d) None of the mentioned

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Which object recognition process is an error-prone process? a) Bottom-up segmentation b) Top-down segmentation c) Both Bottom-up & Top-down segmentation d) None of the mentioned

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Perceptron learning, Delta learning and LMS learning are learning methods which falls under the category of (A) Error correction learning – learning with a teacher (B) Reinforcement learning – learning with a critic (C) Hebbian learning (D) Competitive learning – learning without a teacher

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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

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____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned

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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

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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

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______ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned

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Which were built in such a way that humans had to supply the inputs and interpret the outputs? a) Agents b) AI system c) Sensor d) Actuators

Description : Which were built in such a way that humans had to supply the inputs and interpret the outputs? a) Agents b) AI system c) Sensor d) Actuators

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Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned

Description : Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned

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Description : How the compactness of the bayesian network can be described? a) Locally structured b) Fully structured c) Partial structure d) All of the mentioned

Last Answer : a) Locally structured

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Description : What does the bayesian network provides? a) Complete description of the domain b) Partial description of the domain c) Complete description of the problem d) None of the mentioned

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____________ planning allows the agent to take advice from the domain designer in the form of decomposition rules. a) GraphPlan b) Hierarchical task network (HTN) c) SatPlan d) None of the mentioned

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Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned

Description : Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned

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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

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What does the Bayesian network provides? a) Complete description of the domain b) Partial description of the domain c) Complete description of the problem d) None of the mentioned

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Last Answer : a) Complete description of the domain

The Network is overloaded with enormous data sent by many computers within the network. The inability of the network to deliver the data is termed as __________ . (1) Access control (2) Congestion (3) Error propagation (4) Deadlock

Description : The Network is overloaded with enormous data sent by many computers within the network. The inability of the network to deliver the data is termed as __________ . (1) Access control (2) Congestion (3) Error propagation (4) Deadlock

Last Answer : Congestion

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

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Last Answer : a) True

To overcome the need to backtrack in constraint satisfaction problem can be eliminated by ____________ a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals

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Define constraint propagation.

Description : Define constraint propagation.

Last Answer : It is the general term for propagating (i.e.) spreading the implications of constraints on the variable on to other variable. 

What is meant by predicate indexing? a) All the one kind of facts in one bucket and another kind in other bucket b) Acts like index for facts c) All of the mentioned d) None of the mentioned

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Last Answer : a) All the one kind of facts in one bucket and another kind in other bucket

A heuristic is a way of trying ___________ a) To discover something or an idea embedded in a program b) To search and measure how far a node in a search tree seems to be from a goal c) To compare two nodes in a search tree to see if one is better than another d) All of the mentioned

Description : A heuristic is a way of trying ___________ a) To discover something or an idea embedded in a program b) To search and measure how far a node in a search tree seems to be from a goal c) To compare two nodes in a search tree to see if one is better than another d) All of the mentioned

Last Answer : d) All of the mentioned

In a token ring network the transmission speed is 10^7 bps and the propagation speed is 200 metres/micro second. The 1- bit delay in this network is equivalent to: a. 500 metres of cable. b. 200 metres of cable. c. 20 metres of cable. d. 50 metres of cable

Description : In a token ring network the transmission speed is 10^7 bps and the propagation speed is 200 metres/micro second. The 1- bit delay in this network is equivalent to: a. 500 metres of cable. b. 200 metres of cable. c. 20 metres of cable. d. 50 metres of cable

Last Answer : c. 20 metres of cable.

When a top-level function is entered, the LISP processor does? a) It reads the function entered b) It prints the result returned by the function c) Large memory and high-speed processor d) All of the mentioned

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Last Answer : b) It prints the result returned by the function