Machine Learning, Linear Regression, Gradient Descent, Python, Stochastic Gradient.
55000, 50) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') grid_b0, grid_b1 = np.meshgrid(grid_b0, grid_b1) zs = np.array([rss(x,y, df) for x,y in zip(np.ravel(grid_b0), np.ravel(grid_b1))]) Z = zs.reshape(grid_b0.shape) ax.plot_surface(grid_b0, grid_b1, Z, rstride=1, cstride=1,