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UGCNET-Dec2012-II: 46

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Back propagation is a learning technique that adjusts weights in the neutral network by propagating weight changes.

Forward from source to sink
Backward from sink to source
Forward from source to hidden nodes
Backward from sink to hidden nodes

ugcnetdec2012ii
machine-learning
data-mining
back-propagation

asked Jul 11, 2016 in Machine Learning by jothee
recategorized Oct 11, 2018 by Pooja Khatri | 2.6k views

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1 Answer

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Ans B

answered Jul 11, 2016 by sh!va

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Why it can't be option D as back propogation is multilayer feedbackward so it has hidden layers too. .

commented Jun 10, 2018 by SONALI MEHNDIRATTA

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UGCNET-Sep2013-III: 28
In a single perceptron, the updation rule of weight vector is given by $w(n+1) = w(n) + \eta [d(n)-y(n)]$ $w(n+1) = w(n) - \eta [d(n)-y(n)]$ $w(n+1) = w(n) + \eta [d(n)-y(n)]*x(n)$ $w(n+1) = w(n) - \eta [d(n)-y(n)]*x(n)$

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