diff --git a/08_pytorch_paper_replicating.ipynb b/08_pytorch_paper_replicating.ipynb
index 728258e0..519d60aa 100644
--- a/08_pytorch_paper_replicating.ipynb
+++ b/08_pytorch_paper_replicating.ipynb
@@ -715,7 +715,7 @@
     ">\n",
     "> You could definitely do that by reproducing all of the math equations from the paper with custom PyTorch layers and that would certainly be an educative exercise, however, using pre-existing PyTorch layers is usually favoured as pre-existing layers have often been extensively tested and performance checked to make sure they run correctly and fast. \n",
     "\n",
-    "> **Note:** We're going to be focused on writing PyTorch code to create these layers, for the background on what each of these layers does, I'd suggest reading the ViT Paper in full or reading the linked resources for each layer.\n",
+    "> **Note:** We're going to be focused on writing PyTorch code to create these layers. For the background on what each of these layers does, I'd suggest reading the ViT Paper in full or reading the linked resources for each layer.\n",
     "\n",
     "Let's take Figure 1 and adapt it to our FoodVision Mini problem of classifying images of food into pizza, steak or sushi.\n",
     "\n",