diff --git a/Chapters/5-Optimization/INDEX.md b/Chapters/5-Optimization/INDEX.md
index 97b8aa1..90d9542 100644
--- a/Chapters/5-Optimization/INDEX.md
+++ b/Chapters/5-Optimization/INDEX.md
@@ -281,12 +281,12 @@ small value, usually in the order of $10^{-8}$
 > instead of a vector. To make it easier to understand in matricial notation:
 >
 > $$
->    \begin{aligned}
->        \nabla L^{(k + 1)} &= \frac{d \, Loss^{(k)}}{d \, W^{(k)}} \\
->        G^{(k + 1)} &= G^{(k)} +(\nabla L^{(k+1)}) ^2 \\
->        W^{(k+1)} &= W^{(k)} - \eta \frac{\nabla L^{(k + 1)}}
+   \begin{aligned}
+        \nabla L^{(k + 1)} &= \frac{d \, Loss^{(k)}}{d \, W^{(k)}} \\
+        G^{(k + 1)} &= G^{(k)} +(\nabla L^{(k+1)}) ^2 \\
+        W^{(k+1)} &= W^{(k)} - \eta \frac{\nabla L^{(k + 1)}}
                 {\sqrt{G^{(k+1)} + \epsilon}}
->    \end{aligned}
+    \end{aligned}
 > $$
 >
 > In other words, compute the gradient and scale it for the sum of its squares