diff --git a/Chapters/5-Optimization/INDEX.md b/Chapters/5-Optimization/INDEX.md
index 90d9542..b9e9928 100644
--- a/Chapters/5-Optimization/INDEX.md
+++ b/Chapters/5-Optimization/INDEX.md
@@ -280,15 +280,15 @@ small value, usually in the order of $10^{-8}$
 > This example is tough to understand if we where to apply it to a matrix $W$
 > instead of a vector. To make it easier to understand in matricial notation:
 >
-> $$
+$$
    \begin{aligned}
         \nabla L^{(k + 1)} &= \frac{d \, Loss^{(k)}}{d \, W^{(k)}} \\
         G^{(k + 1)} &= G^{(k)} +(\nabla L^{(k+1)}) ^2 \\
         W^{(k+1)} &= W^{(k)} - \eta \frac{\nabla L^{(k + 1)}}
                 {\sqrt{G^{(k+1)} + \epsilon}}
     \end{aligned}
-> $$
->
+$$
+> 
 > In other words, compute the gradient and scale it for the sum of its squares
 > until that point