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 b9a665b133e6ece35f4b26dad37516f20e15e6d7..afead82a8abfb5ca3826a0110940ea5574a8c1dd 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 +63,8 @@ marie@compute$ R -e 'install.packages("ggplot2")'
 ## Deep Learning with R
 
 The deep learning frameworks perform extremely fast when run on accelerators such as GPU.
-Therefore, using nodes with built-in GPUs, e.g., partitions [ml](../jobs_and_resources/power9.md)
+Therefore, using nodes with built-in GPUs, e.g., partitions
+[ml](../jobs_and_resources/hardware_overview.md)
 and [alpha](../jobs_and_resources/alpha_centauri.md), is beneficial for the examples here.
 
 ### R Interface to TensorFlow
diff --git a/doc.zih.tu-dresden.de/docs/software/machine_learning.md b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
index f2e5f24aa9f4f8e5f8fb516310b842584d30a614..1f40e6199e88f6aa4fd68037a0f4b32113001913 100644
--- a/doc.zih.tu-dresden.de/docs/software/machine_learning.md
+++ b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
@@ -13,7 +13,7 @@ The main feature of the nodes is the ability to work with the
 [NVIDIA Tesla V100](https://www.nvidia.com/en-gb/data-center/tesla-v100/) GPU with **NV-Link**
 support that allows a total bandwidth with up to 300 GB/s. Each node on the
 partition ML has 6x Tesla V-100 GPUs. You can find a detailed specification of the partition in our
-[Power9 documentation](../jobs_and_resources/power9.md).
+[Power9 documentation](../jobs_and_resources/hardware_overview.md).
 
 !!! note