diff --git a/doc.zih.tu-dresden.de/docs/software/distributed_training.md b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
index cfb8c6a38f2b3115aa690eb4615e02697f37fa17..f1879521b52714079b5d5cf044d1c2dfc710ce8c 100644
--- a/doc.zih.tu-dresden.de/docs/software/distributed_training.md
+++ b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
@@ -141,8 +141,9 @@ wait
 !!! note
     This section is under construction
 
-Pytorch provides mutliple ways to acheieve data parallelism to train the deep learning models effieciently. These models are part of the `torch.distributed` sub-package that ships 
-with the main deep learning package.
+PyTorch provides multiple ways to achieve data parallelism to train the deep learning models
+efficiently. These models are part of the `torch.distributed` sub-package that ships with the main
+deep learning package.
 
 Easiest method to quickly prototype if the model is trainable in a multi-GPU setting is to wrap the exisiting model with the `torch.nn.DataParallel` class as shown below,