Below you can see the first 3 data points of the data set, all labeled as 'setosa'. Let's set the numerical value for 'setosa' to 1. (i.e. y = 1). [ ]X[0:3] array([[5.1, 1.4), [4.9, 1.4], [4.7, 1.3]]) Suppose the initial weights of the logistic neuron are w0 = 0.1,w1 = -0.2, w2 = 0.1. Q1-1. Write the weights after processing data points 0,1,2, with learning rate n = 0.1 and show your calculations. This is similar to the previous assignment, only done now for the logistic neuron. You can also use LogisticRegressionGD to check your calculations. Q1-2. Given our data X, let Xd-2 and Xd-3 be the quadratic and cubic features. Using code for polynomial regression from the Regression Code Notebook, generate X d-2 and Xd=3. Q1-3. Using LogisticRegressionGD, fit X, Xd-2 and Xd-3. Here you should set n 0.0001 and niter Z 10000. For each of these three cases, report the loss function value for the model computed by LogisticRegressionGD. (What happens if n is chosen too large or too small?) Here, for a fixed choice of n and niter, it is expected that the loss value decreases as d increases. [ ] # your calculations and code go here