diff --git a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md
index 6d2ef5092f79b..c3922a654ec90 100644
--- a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md
+++ b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md
@@ -14,8 +14,8 @@ Learn how to install the GPU drivers to use your GPU with Model Builder.
 
 ## Hardware requirements
 
-- At least one CUDA compatible GPU. For a list of compatible GPUs, see [NVIDIA's guide](https://developer.nvidia.com/cuda-gpus).
-- At least 6GB of dedicated GPU memory.
+- At least one CUDA-compatible GPU. For a list of compatible GPUs, see [NVIDIA's guide](https://developer.nvidia.com/cuda-gpus).
+- At least 6 GB of dedicated GPU memory.
 
 ## Prerequisites
 
@@ -25,9 +25,9 @@ Learn how to install the GPU drivers to use your GPU with Model Builder.
 ### Image classification only
 
 - NVIDIA developer account. If you don't have one, [create a free account](https://developer.nvidia.com/developer-program).
-- Install dependencies
+- Install dependencies:
   - Install [CUDA v10.1](https://developer.nvidia.com/cuda-10.1-download-archive-update2). Make sure you install CUDA v10.1, not any other newer version.
-  - Install [cuDNN v7.6.4 for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-download). You cannot have multiple versions of cuDNN installed. After downloading cuDNN v7.6.4 zip file and unpacking it, copy `<CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll` to `<YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin`.
+  - Install [cuDNN for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-download). (You can't have multiple versions of cuDNN installed.)
 
 ## Troubleshooting
 
@@ -37,7 +37,7 @@ Deep learning scenarios tend to run faster on GPUs.
 
 Some scenarios like image classification support training on Azure GPU VMs.
 
-However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. Note however that training times are significantly longer.  
+However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. However, training times are significantly longer.  
 
 **How do I know what GPU I have?**
 
@@ -46,25 +46,25 @@ However, if local GPUs or Azure are not an option for you, these scenarios also
 1. Right-click on the Windows start menu icon and select **Settings**.
 1. Select **Settings** > **System**
 1. Select **Display** and scroll down to **Related settings**.
-1. Select **Advanced display**. Your GPU’s make and model should be shown under **Display information**.
+1. Select **Advanced display**. Your GPU's make and model are shown under **Display information**.
 
 ***Check GPU from Task Manager***
 
 1. Right-click on the Windows start menu icon and select **Task Manager**.
 1. Select **Performance**.
 1. In the last pane of the tab, choose **GPU**. If this option is available, it will likely be at the bottom of the list.
-1. In the top right corner of the GPU selection, information about your computer’s GPU will be visible.
+1. In the top right corner of the GPU selection, information about your computer's GPU is shown.
 
 **I don't see my GPU in Settings or Task Manager but I know I have an NVIDIA GPU.**
 
-1. Open Device Manager
-1. Look at Display adapters
-1. Install appropriate [driver](https://www.nvidia.com/drivers) for your GPU.
+1. Open Device Manager.
+1. Look at Display adapters.
+1. Install the appropriate [driver](https://www.nvidia.com/drivers) for your GPU.
 
 **How do I see what version of CUDA I have?**
 
-1. Open a PowerShell or command line window
-1. Type in `nvcc --version`
+1. Open a PowerShell or command line window.
+1. Run the command `nvcc --version`.
 
 **cuda is not available, please confirm you have a cuda-supported gpu**