Machine Learning, Linear Regression, Gradient Descent, Python, Stochastic Gradient.
normalize y_normalizer = np.sqrt(sum(y**2)) y_norm = y/y_normalizer x_normalizer = np.sqrt(sum(x**2)) x_norm = x/x_normalizer I tried 5 different stepsize here from 0.5 to 0.001 stepsize = [0.5, 0.1, 0.05, 0.01, 0.001] # save gradient vector length for each stepsize gradient_vector_length_dict =