diff --git a/book/src/AI-Models/Autoencoder/Autoencoder.md b/book/src/AI-Models/Autoencoder/Autoencoder.md
index 0f1b52625b4c96f0b27c2d2ad7531058403777b4..ae6968fb13bf22489786574c4dd17db71cff8bca 100644
--- a/book/src/AI-Models/Autoencoder/Autoencoder.md
+++ b/book/src/AI-Models/Autoencoder/Autoencoder.md
@@ -30,7 +30,7 @@ decoder. This is used to restrict the information flow to only the important par
 usecase. In the case of a denoising autoencoder for example, the bottleneck should filter out the 
 noise.
 
-Smaller bottlenecks lower the risk of [overfitting](https://en.wikipedia.org/wiki/Overfitting) 
+Smaller bottlenecks lower the risk of [overfitting](../../Glossary.md#overfitting) 
 since it can't contain enough information relative to the input size to effectively learn 
 specific inputs.
 However the smaller the bottleneck is the larger is the risk of losing important data.