diff --git a/beginner_source/basics/optimization_tutorial.py b/beginner_source/basics/optimization_tutorial.py
index 82bfaa8f07..3dfd60ecc6 100644
--- a/beginner_source/basics/optimization_tutorial.py
+++ b/beginner_source/basics/optimization_tutorial.py
@@ -15,7 +15,7 @@
 Now that we have a model and data it's time to train, validate and test our model by optimizing its parameters on
 our data. Training a model is an iterative process; in each iteration the model makes a guess about the output, calculates
 the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in
-the `previous section  <autograd_tutorial.html>`_), and **optimizes** these parameters using gradient descent. For a more
+the `previous section  <autogradqs_tutorial.html>`_), and **optimizes** these parameters using gradient descent. For a more
 detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown <https://www.youtube.com/watch?v=tIeHLnjs5U8>`__.
 
 Prerequisite Code