Implement the linear perceptron using stochastic gradient descent (SGD) or gradient descent (GD). Download the dataset 'data3.
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Implement the linear perceptron using stochastic gradient descent (SGD) or gradient descent (GD). Download the

dataset "data3.mat". Use the whole data set as training, where each row consists of the feature vector x followed by the label y ∈ {−1, 1} (last column). Show with figures the resulting linear decision boundary on the 2d x data. Show the evolution of binary classification error and the perceptron error with time (or number of iterations) from random initialization until convergence on a successful run (some random inits may not converge or may require many iterations). For GD, discuss the convergence behavior as you vary the step size (η).

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