diff --git a/doc/src/week39/codes/adagradplain.py b/doc/src/week39/codes/adagradplain.py
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+# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(theta):
+    return (1.0/n)*np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x# +4*x*x
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS)
+theta = np.random.randn(2,1)
+iterations = 1000
+# Value for learning rate
+eta = 0.01
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-8
+Giter = 0.0
+for iter in range(iterations):
+    gradients = training_gradient(theta)
+    Giter += gradients*gradients
+    update = gradients*eta/(delta+np.sqrt(Giter))
+    theta -= update
+print("theta from own AdaGrad")
+print(theta)
+