From 9c984a44ec7ae8a9898534b2ca5bedcd62539141 Mon Sep 17 00:00:00 2001
From: Martin Schroschk <martin.schroschk@tu-dresden.de>
Date: Thu, 7 Nov 2024 10:39:29 +0100
Subject: [PATCH] Fix link to alpha centauri section

---
 doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
index bcebd84b4..a36384bf3 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
@@ -63,7 +63,7 @@ marie@compute$ R -e 'install.packages("ggplot2")'
 The deep learning frameworks perform extremely fast when run on accelerators such as GPU.
 Therefore, using nodes with built-in GPUs, e.g., clusters
 [`Capella`](../jobs_and_resources/hardware_overview.md#capella),
-[`Alpha`](../jobs_and_resources/hardware_overview.md#alpha_centauri) and
+[`Alpha`](../jobs_and_resources/hardware_overview.md#alpha-centauri) and
 [`Power9`](../jobs_and_resources/hardware_overview.md) is beneficial for the examples here.
 
 ### R Interface to TensorFlow
-- 
GitLab