diff --git a/beginner_source/blitz/neural_networks_tutorial.py b/beginner_source/blitz/neural_networks_tutorial.py
index 168572c4b14..a805cdeb3af 100644
--- a/beginner_source/blitz/neural_networks_tutorial.py
+++ b/beginner_source/blitz/neural_networks_tutorial.py
@@ -176,7 +176,7 @@ def num_flat_features(self, x):
 #           -> loss
 #
 # So, when we call ``loss.backward()``, the whole graph is differentiated
-# w.r.t. the loss, and all Tensors in the graph that has ``requires_grad=True``
+# w.r.t. the loss, and all Tensors in the graph that have ``requires_grad=True``
 # will have their ``.grad`` Tensor accumulated with the gradient.
 #
 # For illustration, let us follow a few steps backward: