diff --git a/beginner_source/nlp/pytorch_tutorial.py b/beginner_source/nlp/pytorch_tutorial.py
index 62249bf2dc8..6ec1b82709c 100644
--- a/beginner_source/nlp/pytorch_tutorial.py
+++ b/beginner_source/nlp/pytorch_tutorial.py
@@ -9,7 +9,7 @@
 All of deep learning is computations on tensors, which are
 generalizations of a matrix that can be indexed in more than 2
 dimensions. We will see exactly what this means in-depth later. First,
-lets look what we can do with tensors.
+let's look what we can do with tensors.
 """
 # Author: Robert Guthrie
 
@@ -162,7 +162,7 @@
 # other operation, etc.)
 #
 # If ``requires_grad=True``, the Tensor object keeps track of how it was
-# created. Lets see it in action.
+# created. Let's see it in action.
 #
 
 # Tensor factory methods have a ``requires_grad`` flag
@@ -187,7 +187,7 @@
 # But how does that help us compute a gradient?
 #
 
-# Lets sum up all the entries in z
+# Let's sum up all the entries in z
 s = z.sum()
 print(s)
 print(s.grad_fn)
@@ -222,7 +222,7 @@
 
 
 ######################################################################
-# Lets have Pytorch compute the gradient, and see that we were right:
+# Let's have Pytorch compute the gradient, and see that we were right:
 # (note if you run this block multiple times, the gradient will increment.
 # That is because Pytorch *accumulates* the gradient into the .grad
 # property, since for many models this is very convenient.)