grammar

Author: nibaccam

articles/machine-learning/concept-distributed-training.md

@@ -17,7 +17,7 @@ ms.date: 03/24/2020
 
 Distributed training refers to the ability to share data loads and training tasks across multiple GPUs to accelerate model training. The typical use case for distributed training is for training [deep learning](concept-deep-learning-vs-machine-learning.md) models. 
 
-Deep neural networks are often computed intensive as they require large learning workloads in order to processing millions of examples and parameters across its multiple layers. This deep learning lends itself well to distributed training, since running tasks in parallel instead of serially saves time and compute resources.
+Deep neural networks are often compute intensive as they require large learning workloads in order to processing millions of examples and parameters across its multiple layers. This deep learning lends itself well to distributed training, since running tasks in parallel instead of serially saves time and compute resources.
 
 ## Distributed training in Azure Machine Learning