Update URL's to account for a target heading text change . . .

fbsolo-ms1 · fbsolo-ms1 · commit 5ce8e4e55b3c · 2024-04-26T13:11:00.000-07:00
diff --git a/articles/lab-services/class-type-deep-learning-natural-language-processing.md b/articles/lab-services/class-type-deep-learning-natural-language-processing.md
@@ -35,7 +35,7 @@ For instructions on how to create a lab, see [Tutorial: Set up a lab](tutorial-s
 | Lab settings | Value |
 | ------------ | ------------------ |
 | Virtual machine (VM) size | **Small GPU (Compute)**. This size is best suited for compute-intensive and network-intensive applications like Artificial Intelligence and Deep Learning. |
-| VM image | [Data Science Virtual Machine for Linux (Ubuntu)](https://azuremarketplace.microsoft.com/marketplace/apps?search=Data%20science%20Virtual%20machine&page=1&filters=microsoft%3Blinux). This image provides deep learning frameworks and tools for machine learning and data science. To view the full list of installed tools on this image, see [What's included on the DSVM?](../machine-learning/data-science-virtual-machine/overview.md#whats-included-on-the-dsvm). |
+| VM image | [Data Science Virtual Machine for Linux (Ubuntu)](https://azuremarketplace.microsoft.com/marketplace/apps?search=Data%20science%20Virtual%20machine&page=1&filters=microsoft%3Blinux). This image provides deep learning frameworks and tools for machine learning and data science. To view the full list of installed tools on this image, see [What does the DSVM include?](../machine-learning/data-science-virtual-machine/overview.md#what-does-the-dsvm-include). |
 | Enable remote desktop connection | Optionally, check **Enable remote desktop connection**. The Data Science image is already configured to use X2Go so that teachers and students can connect using a GUI remote desktop. X2Go *doesn't* require the **Enable remote desktop connection** setting to be enabled. |
 | Template Virtual Machine Settings | Optionally, choose **Use a virtual machine image without customization**. If you're using [lab plans](concept-lab-accounts-versus-lab-plans.md) and the DSVM has all the tools that your class requires, you can skip the template customization step. |
 
diff --git a/articles/lab-services/how-to-setup-lab-gpu-1.md b/articles/lab-services/how-to-setup-lab-gpu-1.md
@@ -52,7 +52,7 @@ As shown in the preceding image, this option is enabled by default, which ensure
 - When you select a *visualization* GPU size, your lab VMs are powered by the [NVIDIA Tesla M60](https://images.nvidia.com/content/tesla/pdf/188417-Tesla-M60-DS-A4-fnl-Web.pdf) GPU and [GRID technology](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/solutions/resources/documents1/NVIDIA_GRID_vPC_Solution_Overview.pdf).  In this case, recent GRID drivers are installed, which enables the use of graphics-intensive applications.
 
 > [!IMPORTANT]
-> The **Install GPU drivers** option only installs the drivers when they aren't present on your lab's image.  For example, the GPU drivers are already installed on the Azure marketplace's [Data Science image](../machine-learning/data-science-virtual-machine/overview.md#whats-included-on-the-dsvm).  If you create a lab using the Data Science image and choose to **Install GPU drivers**, the drivers won't be updated to a more recent version.  To update the drivers, you will need to manually install them as explained in the next section.  
+> The **Install GPU drivers** option only installs the drivers when they aren't present on your lab's image.  For example, the GPU drivers are already installed on the Azure marketplace's [Data Science image](../machine-learning/data-science-virtual-machine/overview.md#what-does-the-dsvm-include).  If you create a lab using the Data Science image and choose to **Install GPU drivers**, the drivers won't be updated to a more recent version.  To update the drivers, you will need to manually install them as explained in the next section.  
 
 ### Install the drivers manually
 
diff --git a/articles/lab-services/how-to-setup-lab-gpu.md b/articles/lab-services/how-to-setup-lab-gpu.md
@@ -60,7 +60,7 @@ When you select **Install GPU drivers**, it ensures that recently released drive
 - When you select the Medium GPU *(Visualization)* size, your lab VMs are powered by the [NVIDIA Tesla M60](https://images.nvidia.com/content/tesla/pdf/188417-Tesla-M60-DS-A4-fnl-Web.pdf) GPU and [GRID technology](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/solutions/resources/documents1/NVIDIA_GRID_vPC_Solution_Overview.pdf).  In this case, recent GRID drivers are installed, which enables the use of graphics-intensive applications.
 
 > [!IMPORTANT]
-> The **Install GPU drivers** option only installs the drivers when they aren't present on your lab's image.  For example, NVIDIA GPU drivers are already installed on the Azure marketplace's [Data Science Virtual Machine image](../machine-learning/data-science-virtual-machine/overview.md#whats-included-on-the-dsvm).  If you create a Small GPU (Compute) lab using the Data Science image and choose to **Install GPU drivers**, the drivers won't be updated to a more recent version. To update the drivers, you will need to manually install the drivers.
+> The **Install GPU drivers** option only installs the drivers when they aren't present on your lab's image.  For example, NVIDIA GPU drivers are already installed on the Azure marketplace's [Data Science Virtual Machine image](../machine-learning/data-science-virtual-machine/overview.md#what-does-the-dsvm-include).  If you create a Small GPU (Compute) lab using the Data Science image and choose to **Install GPU drivers**, the drivers won't be updated to a more recent version. To update the drivers, you will need to manually install the drivers.
 
 ### Install GPU drivers manually
 
diff --git a/articles/lab-services/how-to-use-shared-image-gallery.md b/articles/lab-services/how-to-use-shared-image-gallery.md
@@ -13,7 +13,7 @@ An image contains the operating system, software applications, files, and settin
 
 You can use two types of images to set up a new lab:
 
-- Azure Marketplace images are prebuilt by Microsoft for use within Azure. These images have either Windows or Linux installed and may also include software applications. For example, the [Data Science Virtual Machine image](../machine-learning/data-science-virtual-machine/overview.md#whats-included-on-the-dsvm) includes deep learning frameworks and tools.
+- Azure Marketplace images are prebuilt by Microsoft for use within Azure. These images have either Windows or Linux installed and may also include software applications. For example, the [Data Science Virtual Machine image](../machine-learning/data-science-virtual-machine/overview.md#what-does-the-dsvm-include) includes deep learning frameworks and tools.
 - Custom images are created by your institution’s IT department and\or other educators. You can create both Windows and Linux custom images. You have the flexibility to install Microsoft and third-party applications based on your unique needs. You also can add files, change application settings, and more.
 
 > [!IMPORTANT]
diff --git a/articles/machine-learning/data-science-virtual-machine/overview.md b/articles/machine-learning/data-science-virtual-machine/overview.md
@@ -78,7 +78,7 @@ The DSVM can evaluate or learn new data science [tools](./tools-included.md). Tr
 
 ### Deep learning with GPUs
 
-In the DSVM, your training models can use deep learning algorithms on graphics processing unit (GPU)-based hardware. If you take advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you lever GPU-based hardware in the cloud, according to your needs. You can switch to a GPU-based VM when you train large models, or when you need high-speed computations while you keep the same OS disk. You can choose any of the N series GPU-enabled virtual machine SKUs with DSVM. Azure free accounts don't support GPU-enabled virtual machine SKUs. 
+In the DSVM, your training models can use deep learning algorithms on graphics processing unit (GPU)-based hardware. If you take advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you lever GPU-based hardware in the cloud, according to your needs. You can switch to a GPU-based VM when you train large models, or when you need high-speed computations while you keep the same OS disk. You can choose any of the N series GPU-enabled virtual machine SKUs with DSVM. Azure free accounts don't support GPU-enabled virtual machine SKUs.
 
 A Windows-edition DSVM comes preinstalled with GPU drivers, frameworks, and GPU versions of deep learning frameworks. On the Linux editions, deep learning on GPUs is enabled on the Ubuntu DSVMs.
 
