please use Python.

answer all parts! thank you

X X2 Problem 2) Gradient descent learning: Consider the following set of data points: input desired label 1 1 1 1 0 1 0 1 0 - 1 - 1 0 - 1 0 0 - 1 1 0 As the above table shows, the data points are categorized (labeled) in two groups specified by the labels "I" and "0" a) Use the gradient descent learning algorithm to train a single neuron on the data samples given in the table above. Repeat the gradient descent algorithm for only 3 iterations. Assume the learning rate is 0.1 with initial weight vector of (111). a) Draw the average square error all the three iterations. Is the error decreasing? Explain your observation. b) Draw the schematics of the trained classifier after the third iteration. c) Provide the equation of the trained model at every iteration. Hint: the question of the three classifier lines. d) Plot the given data points with two different markers for each group. e) Plot the classifier line of the 3rd iteration to the plot in (d). Label each classifier. 1) Calculate the accuracy, sensitivity, and specificity of the 3rd classifier. X X2 Problem 2) Gradient descent learning: Consider the following set of data points: input desired label 1 1 1 1 0 1 0 1 0 - 1 - 1 0 - 1 0 0 - 1 1 0 As the above table shows, the data points are categorized (labeled) in two groups specified by the labels "I" and "0" a) Use the gradient descent learning algorithm to train a single neuron on the data samples given in the table above. Repeat the gradient descent algorithm for only 3 iterations. Assume the learning rate is 0.1 with initial weight vector of (111). a) Draw the average square error all the three iterations. Is the error decreasing? Explain your observation. b) Draw the schematics of the trained classifier after the third iteration. c) Provide the equation of the trained model at every iteration. Hint: the question of the three classifier lines. d) Plot the given data points with two different markers for each group. e) Plot the classifier line of the 3rd iteration to the plot in (d). Label each classifier. 1) Calculate the accuracy, sensitivity, and specificity of the 3rd classifier