diff --git a/beginner_source/blitz/neural_networks_tutorial.py b/beginner_source/blitz/neural_networks_tutorial.py
index 4549089a5c4..68eab4ac88d 100644
--- a/beginner_source/blitz/neural_networks_tutorial.py
+++ b/beginner_source/blitz/neural_networks_tutorial.py
@@ -176,8 +176,9 @@ 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 have ``requires_grad=True``
-# will have their ``.grad`` Tensor accumulated with the gradient.
+# w.r.t. the neural net parameters, 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: