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

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
by

1 Answer

Answer :

c) Recurrent neural network
by

Related questions

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

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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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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_________ 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

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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

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