diff --git a/chapters/en/unit2/cnns/introduction.mdx b/chapters/en/unit2/cnns/introduction.mdx
index 66217794d..f443d48ab 100644
--- a/chapters/en/unit2/cnns/introduction.mdx
+++ b/chapters/en/unit2/cnns/introduction.mdx
@@ -8,7 +8,7 @@ In this unit, we will learn about Convolutional Neural Networks, an important st
 
 ## Convolution: Basic Ideas
 
-Convolution is an operation used to extract features from data. The data can be 1D, 2D or 3D. We'll explain the operation with a solid example. All you need to know now is that the operation simply takes a matrix made of numbers, moves it through the data, and takes the sum of products between the data and that matrix. This matrix is called kernel or filter. You might say, "What does it have to do with the feature extraction, and how am I supposed to apply it?
+Convolution is an operation used to extract features from data. The data can be 1D, 2D or 3D. We'll explain the operation with a solid example. All you need to know now is that the operation simply takes a matrix made of numbers, moves it through the data, and takes the sum of products between the data and that matrix. This matrix is called kernel or filter. You might say, "What does it have to do with the feature extraction, and how am I supposed to apply it?"
 Don’t panic! We’re getting to it.
 
 To illustrate the intuition, let's take a look at this example. We have this 1D data, and we visualize it. Visualization will help understand the effects of convolution operation.