diff --git a/tutorials/CIFAR_Captum_Robustness.ipynb b/tutorials/CIFAR_Captum_Robustness.ipynb
index 02df44450..155046572 100644
--- a/tutorials/CIFAR_Captum_Robustness.ipynb
+++ b/tutorials/CIFAR_Captum_Robustness.ipynb
@@ -248,7 +248,7 @@
     }
    ],
    "source": [
-    "image_show(image, pred+ \" \" + str(score.item()))\n",
+    "image_show(image, pred + \" \" + str(score.item()))\n",
     "image_show(unnormalize(perturbed_image_fgsm), new_pred_fgsm + \" \" + str(score_fgsm.item()))\n"
    ]
   },
@@ -307,7 +307,7 @@
     }
    ],
    "source": [
-    "image_show(image, pred+ \" \" + str(score.item()))\n",
+    "image_show(image, pred + \" \" + str(score.item()))\n",
     "image_show(unnormalize(perturbed_image_pgd.detach()), new_pred_pgd + \" \" + str(score_pgd.item()))\n"
    ]
   },
@@ -315,7 +315,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As seen above, the perturbed input is classified as a ship, confirming the targetted attack was successful. "
+    "As seen above, the perturbed input is classified as a ship, confirming the targeted attack was successful. "
    ]
   },
   {
@@ -338,7 +338,7 @@
    "source": [
     "In addition to adversarial attacks, we have developed an AttackComparator, which allows quantifying model performance against any set of perturbations or attacks, including custom transformations.\n",
     "\n",
-    "In this section, we will use the AttackComparator to measure how this model performs against the FGSM / PGD attacks described above as well as torchvision transforms. Note that the attack comparator can be used with any perturbation or attack functions."
+    "In this section, we will use the AttackComparator to measure how this model performs against the FGSM / PGD attacks described above as well as torchvision transforms. Note that the AttackComparator can be used with any perturbation or attack functions."
    ]
   },
   {
@@ -442,7 +442,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The comparator also allows us to aggregate results over a series of batches. We start by resetting the stored metrics from this example, and evaluate a series of batches from the test dataset. Once complete, we can look at the summary returned by the Attack Comparator."
+    "The comparator also allows us to aggregate results over a series of batches. We start by resetting the stored metrics from this example, and evaluate a series of batches from the test dataset. Once complete, we can look at the summary returned by the AttackComparator."
    ]
   },
   {
@@ -585,7 +585,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that a kernel size of 5 was the minimum necessary to misclassify this image. Let's look at the perturbed image and corresponding prediction, and how this compares with the original."
+    "We see that a kernel size of 7 was the minimum necessary to misclassify this image. Let's look at the perturbed image and corresponding prediction, and how this compares with the original."
    ]
   },
   {
@@ -624,7 +624,7 @@
     "image_show(alt_im, new_pred_blur + \" \" + str(score_blur.item()))\n",
     "\n",
     "# Original\n",
-    "image_show(image, pred+ \" \" + str(score.item()))\n"
+    "image_show(image, pred + \" \" + str(score.item()))\n"
    ]
   },
   {