Q-2.

Consider a data set in which the two points {(1, 1),(1, 1)} belong to one class, and the other two points {(1, 1),(1, 1)} belong to the other class. Start with perceptron parameter values at (0, 0), and work out a few stochastic gradient-descent updates with = 1. While performing the stochastic gradient-descent updates, cycle through the training points in any order.

(a) Does the algorithm converge in the sense that the change in objective function becomes extremely small over time?

(b) Explain why the situation in (a) occurs

use this book: neural networks and deep learning by Charu c Aggarwal, go to chapter 1 and look for exercise 4.

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