a simple hand workout of the solution will be appreciatedthanks

E4.9 We want to train a perceptron network using the following training set: starting from the initial conditions W(0) = 10 11 , b(0) = | I i. Sketch the initial decision boundary, and show the weight vector and the three training input vectors, pi, p 2, p3 . Indicate the class of each input vector, and show which ones are correctly classified by the initial decision boundary ii. Present the input p, to the network, and perform one iteration of iii. Sketch the new decision boundary and weight vector, and again in- iv. Present the input p2 to the network, and perform one more itera v. Sketch the new decision boundary and weight vector, and again in- vi. Ifyou continued to use the perceptron learning rule, and presented the perceptron learning rule dicate which of the three input vectors are correctly classified. tion of the perceptron learning rule. dicate which of the three input vectors are correctly classified. all of the patterns many times, would the network eventually learn to correctly classify the patterns? Explain your answer. (This part does not require any calculations.) E4.9 We want to train a perceptron network using the following training set: starting from the initial conditions W(0) = 10 11 , b(0) = | I i. Sketch the initial decision boundary, and show the weight vector and the three training input vectors, pi, p 2, p3 . Indicate the class of each input vector, and show which ones are correctly classified by the initial decision boundary ii. Present the input p, to the network, and perform one iteration of iii. Sketch the new decision boundary and weight vector, and again in- iv. Present the input p2 to the network, and perform one more itera v. Sketch the new decision boundary and weight vector, and again in- vi. Ifyou continued to use the perceptron learning rule, and presented the perceptron learning rule dicate which of the three input vectors are correctly classified. tion of the perceptron learning rule. dicate which of the three input vectors are correctly classified. all of the patterns many times, would the network eventually learn to correctly classify the patterns? Explain your answer. (This part does not require any calculations.)