diff --git a/beginner_source/basics/optimization_tutorial.py b/beginner_source/basics/optimization_tutorial.py
index 72bdf2e2eb6..be17289fd19 100644
--- a/beginner_source/basics/optimization_tutorial.py
+++ b/beginner_source/basics/optimization_tutorial.py
@@ -13,7 +13,7 @@
 ===========================
 
 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 (called an *epoch*) the model makes a guess about the output, calculates
+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
 detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown <https://www.youtube.com/watch?v=tIeHLnjs5U8>`__.