diff --git a/doc.zih.tu-dresden.de/docs/software/ngc_containers.md b/doc.zih.tu-dresden.de/docs/software/ngc_containers.md
index 72397dfe6092cad5ce1f79ca1280847f980fee90..e22ca5748322466470e410bb1d7d822fe68e5bc9 100644
--- a/doc.zih.tu-dresden.de/docs/software/ngc_containers.md
+++ b/doc.zih.tu-dresden.de/docs/software/ngc_containers.md
@@ -131,7 +131,7 @@ The majority of the NGC containers allow you to use multiple GPUs from one node
 
 However, PyTorch and TensorFlow containers support multi-GPU usage.
 
-The example of using PyTorch container for the training ResNet50 model on the classification task on ImageNet dataset is presented below:
+An example of using the PyTorch container for training of the ResNet50 model on the classification task on ImageNet dataset is presented below:
 
 ```console
 marie@login$ srun -p alpha --nodes 1 --ntasks-per-node 8 --ntasks 8 --gres=gpu:8 --time=08:00:00 --pty --mem=500000 bash