Problem 1) [Paper-based] Perceptron leaming: Consider the following set of data points: As the above table shows, the data points are categorized (labeled) in two groups specified by the labels "1" and "0". a) Use the perceptron learning rule to train a single neuron perceptron on the data points given above. Show all the steps in the iteration. Initialize the weights to (w0=0.7,w1=0.2,w2=0.95), assume the leaming rate is 1 (i.e., =1 ), and use the hard-limiter activation function (If v0 output 1: otherwise output 0 ), i.e.: (v)={1,0,ifv0ifv