Machine Learning, Linear Regression, Gradient Descent, Python, Stochastic Gradient.
= np.sqrt(g_b0**2 + g_b1**2) < tol if satisfied: print('gradient vector size: ', np.sqrt(g_b0**2 + g_b1**2)) break graident_vector_length_dict[str(stepsize)] = gradient_vector_length # discard first 200 generated b0 and b1 b1_dict[str(stepsize)] = np.mean(b1_sgd[200:]) b0_dict[str(stepsize)] =