Update Course 1 - Part 6 - Lesson 2 - Notebook.ipynb

Course 1 - Part 6 - Lesson 2 - Notebook.ipynb

@@ -114,7 +114,7 @@
       "source": [
         "Your accuracy is probably about 89% on training and 87% on validation...not bad...But how do you make that even better? One way is to use something called Convolutions. I'm not going to details on Convolutions here, but the ultimate concept is that they narrow down the content of the image to focus on specific, distinct, details. \n",
         "\n",
-        "If you've ever done image processing using a filter (like this: https://en.wikipedia.org/wiki/Kernel_(image_processing)) then convolutions will look very familiar.\n",
+        "If you've ever done image processing using a filter (like this: [https://en.wikipedia.org/wiki/Kernel_(image_processing)](https://en.wikipedia.org/wiki/Kernel_(image_processing%29)) then convolutions will look very familiar.\n",
         "\n",
         "In short, you take an array (usually 3x3 or 5x5) and pass it over the image. By changing the underlying pixels based on the formula within that matrix, you can do things like edge detection. So, for example, if you look at the above link, you'll see a 3x3 that is defined for edge detection where the middle cell is 8, and all of its neighbors are -1. In this case, for each pixel, you would multiply its value by 8, then subtract the value of each neighbor. Do this for every pixel, and you'll end up with a new image that has the edges enhanced.\n",
         "\n",