diff --git a/units/en/unit3/deep-q-network.mdx b/units/en/unit3/deep-q-network.mdx
index dc8cb13..50cd4f2 100644
--- a/units/en/unit3/deep-q-network.mdx
+++ b/units/en/unit3/deep-q-network.mdx
@@ -32,7 +32,7 @@ That’s why, to capture temporal information, we stack four frames together.
 
 Then the stacked frames are processed by three convolutional layers. These layers **allow us to capture and exploit spatial relationships in images**. But also, because the frames are stacked together, **we can exploit some temporal properties across those frames**.
 
-If you don't know what convolutional layers are, don't worry. You can check out [Lesson 4 of this free Deep Reinforcement Learning Course by Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188)
+If you don't know what convolutional layers are, don't worry. You can check out [Lesson 4 of this free Deep Learning Course by Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188)
 
 Finally, we have a couple of fully connected layers that output a Q-value for each possible action at that state.