Machine Learning, Deep Learning, Stochastic Gradient, Data Science, Editors Pick.
points x = np.array([1, 2, 3]) # quadratic function, a parabola y = x**2 for run in range(0, runs): print("Iter " + str(run) + ": Y=" + str(y)) # compute first derivative x = np.gradient(y, 1) # update the function output y = x ** 2 gradient_loop() In this case, the function gradient_loop takes a