Update

units/en/live1/live1.mdx

@@ -1,4 +1,4 @@
-# Live 1: Deep RL Course. Intro, Q&A, and playing with Huggy 🐶
+# Live 1: How the course work, Q&A, and playing with Huggy 🐶
 
 In this first live stream, we explained how the course work (scope, units, challenges, and more) and answered your questions.
 
@@ -6,5 +6,4 @@ And finally, we saw some LunarLander agents you've trained and play with your Hu
 
 <Youtube id="JeJIswxyrsM" />
 
-
 To know when the next live is scheduled **check the discord server**. We will also send **you an email**. If you can't participate, don't worry, we record the live sessions.

units/en/unit3/deep-q-network.mdx

@@ -30,7 +30,7 @@ No, because one frame is not enough to have a sense of motion! But what if I add
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/temporal-limitation-2.jpg" alt="Temporal Limitation"/>
 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 frames are stacked together, **you can exploit some spatial properties across those frames**.
+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 frames are stacked together, **you can exploit some temporal properties across those frames**.
 
 If you don't know what are convolutional layers, don't worry. You can check the [Lesson 4 of this free Deep Reinforcement Learning Course by Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188)