GATE DA 2026 Set 1 — Question 29

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NAT+1 / -0MediumGradient DescentNeural NetworksMachine Learning

Machine Learning → Neural Networks → Gradient Descent

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Question

Consider that for a supervised learning task, the objective function being minimized is fw(x)=wxf_w(x) = wx, where xRx \in \mathbb{R} is the input and wRw \in \mathbb{R} is the parameter. Stochastic Gradient Descent with learning rate of 0.10 is used for parameter updates.
Suppose that at the end of iteration ii, the value of ww becomes 10.00.
Let x=10.00x = 10.00 be the input for iteration (i+1)(i + 1).
The value of ww at the end of iteration (i+1)(i + 1) is __________ . (Rounded off to two decimal places)
Your answer

Enter a number. Decimals, negative values and scientific notation are accepted.

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