python numpy library Implementing Perceptron algorithm problems

  QUESTIONS/TASKS (20 marks) Explain the Perceptron algorithm for the binary classification case, providing its pseudo code. (20 marks) Implement a binary perceptron. (20 marks) Use the binary perceptron to train classifiers to discriminate between class 1 and class 2, class 2 and class 3, and class 1 and class 3. (20 marks) Extend the binary perceptron that you implemented in part 3 above to perform multi-class classification using the 1-vs-rest approach. Report the train and test classification accuracies after training for 20 iterations. (20 marks) Add an ⌠2 regularisation term to your multi-class classifier implemented in part 4. Set the regularisation coefficient to 0.01, 0.1, 1.0, 10.0, 100.0 and compare the train and test classification accuracies. Report the train and test classification accuracies for each of the three classifiers after training for 20 iterations. Which pair of classes is most difficult to separate?