diff --git a/notebooks/Block 4/Solutions to Exercises Block 4 - Convolutional Neural Networks.ipynb b/notebooks/Block 4/Solutions to Exercises Block 4 - Convolutional Neural Networks.ipynb
index 9b5b4866594fc80dacbef6b0b9fd24c5fee8fb4e..391341979eaddf8f81bc5fa4e21f626fb5dda639 100644
--- a/notebooks/Block 4/Solutions to Exercises Block 4 - Convolutional Neural Networks.ipynb	
+++ b/notebooks/Block 4/Solutions to Exercises Block 4 - Convolutional Neural Networks.ipynb	
@@ -749,7 +749,7 @@
     "\n",
     "In the cell below, create a convolutional neural network that consists of 3 convolution blocks. Each convolutional block contains a `Conv2D` layer followed by a max pool layer. The first convolutional block should have 16 filters, the second one should have 32 filters, and the third one should have 64 filters. All convolutional filters should be 3 x 3. All max pool layers should have a `pool_size` of `(2, 2)`.\n",
     "\n",
-    "After the 3 convolutional blocks you should have a flatten layer followed by a fully connected layer with 512 units. The CNN should output class probabilities based on 5 classes which is done by the **softmax** activation function. All other layers should use a **relu** activation function. You should also add Dropout layers with a probability of 20%, where appropriate."
+    "After the 3 convolutional blocks you should have a flatten layer followed by a fully connected layer with 512 units. The CNN should output class probabilities based on 5 classes which is done by the `softmax` activation function. All other layers should use a `relu` activation function. You should also add Dropout layers with a probability of 20%, where appropriate."
    ]
   },
   {