Machine Learning, Linear Regression, Gradient Descent, Python, Stochastic Gradient.
dict() b1_dict = dict() b0_dict = dict() # save number of pass it went over the dataframe number_of_pass_dict = dict() Now for each stepsize, conduct SGD, save coefficients (b0, b1) and gradient vector size. Note first 200 generated coefficients are discarded here, and use the average of the rest