diff --git a/chapter_convolutional-neural-networks/conv-layer.md b/chapter_convolutional-neural-networks/conv-layer.md
index a9f64544e2..715417026f 100644
--- a/chapter_convolutional-neural-networks/conv-layer.md
+++ b/chapter_convolutional-neural-networks/conv-layer.md
@@ -282,7 +282,7 @@ Next, we construct a kernel `K` with a height of 1 and a width of 2.
 When we perform the cross-correlation operation with the input,
 if the horizontally adjacent elements are the same,
 the output is 0. Otherwise, the output is nonzero.
-Note that this kernel is a special case of a finite difference operator. At location $(i,j)$ it computes $x_{i,j} - x_{(i+1),j}$, i.e., it computes the difference between the values of horizontally adjacent pixels. This is a discrete approximation of the first derivative in the horizontal direction. After all, for a function $f(i,j)$ its derivative $-\partial_i f(i,j) = \lim_{\epsilon \to 0} \frac{f(i,j) - f(i+\epsilon,j)}{\epsilon}$. Let's see how this works in practice.
+Note that this kernel is a special case of a finite difference operator. At location $(i,j)$ it computes $x_{i,j} - x_{i,j+1}$, i.e., it computes the difference between the values of horizontally adjacent pixels. This is a discrete approximation of the first derivative in the horizontal direction. After all, for a function $f(i,j)$ its derivative $-\partial_j f(i,j) = \lim_{\epsilon \to 0} \frac{f(i,j) - f(i,j+\epsilon)}{\epsilon}$. Let's see how this works in practice.
 
 ```{.python .input}
 %%tab all