Fixing typo.

chapter_02/nb_ch02_03.ipynb

@@ -36,7 +36,7 @@
       "source": [
         "# A simple CNN for the edge lover task\n",
         "\n",
-        "**Goal:** In this notebook you train a very simple CNN with only 1 kernel to discriminate images containing vertical from those containing horizontal stripes. To check what pattern is recognized by the learned kernel you will visualize the weights of the kernel as an image. You will see that the CNN leans a useful kernel (either a vertical or horiziontal bar).You can experiment with the code to check the influence of the kernel size, the activation function and the pooling method on the result.  \n",
+        "**Goal:** In this notebook you train a very simple CNN with only 1 kernel to discriminate images containing vertical from those containing horizontal stripes. To check what pattern is recognized by the learned kernel you will visualize the weights of the kernel as an image. You will see that the CNN learns a useful kernel (either a vertical or horiziontal bar).You can experiment with the code to check the influence of the kernel size, the activation function and the pooling method on the result.  \n",
         "\n",
         "**Usage:** The idea of the notebook is that you try to understand the provided code by running it, checking the output and playing with it by slightly changing the code and rerunning it.  \n",
         "\n",
@@ -311,7 +311,7 @@
       },
       "source": [
         "### Make a train and validation dataset of images with vertical and horizontal images\n",
-        "Now, let's make a train dataset *X_train* with 1000 images (500 images with vertical and 500 images with horizontal bars). We normalize the images values to be between 0 and 1 by dividing all values with 255. We create a secont dataste *X_val* with exactly the same properties to validate the training of the CNN."
+        "Now, let's make a train dataset *X_train* with 1000 images (500 images with vertical and 500 images with horizontal bars). We normalize the images values to be between 0 and 1 by dividing all values with 255. We create a secont dataset *X_val* with exactly the same properties to validate the training of the CNN."
       ]
     },
     {
@@ -771,4 +771,4 @@
       "outputs": []
     }
   ]
-}
+}