Editorial

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/gh-runners/compare-performance.md

@@ -14,7 +14,7 @@ In this section, you will change the PyTorch backend being used to test the trai
 
 In the previous section, you used the PyTorch 2.3.0 Docker Image compiled with OpenBLAS from DockerHub to run your testing workflow. PyTorch can be run with other backends. You will now modify the testing workflow to use PyTorch 2.3.0 Docker Image compiled with OneDNN and the Arm Compute Library. 
 
-The [Arm Compute Library](https://github.com/ARM-software/ComputeLibrary) is a collection of low-level machine learning functions optimized for Arm's Cortex-A and Neoverse processors and Mali GPUs. Arm-hosted GitHub runners use Arm Neoverse CPUs, which make it possible to optimize your neural networks to take advantage of processor features. ACL implements kernels (also known as operators or layers), using specific instructions that run faster on AArch64.
+The [Arm Compute Library](https://github.com/ARM-software/ComputeLibrary) is a collection of low-level machine learning functions optimized for Arm's Cortex-A and Neoverse processors and Mali GPUs. Arm-hosted GitHub runners use Arm Neoverse CPUs, which make it possible to optimize your neural networks to take advantage of processor features. ACL implements kernels, which are also known as operators or layers, using specific instructions that run faster on AArch64.
 
 ACL is integrated into PyTorch through [oneDNN](https://github.com/oneapi-src/oneDNN), an open-source deep neural network library.
 
@@ -43,11 +43,11 @@ jobs:
 
 ### Run the test workflow
 
-Trigger the **Test Model** job again by clicking the `Run workflow` button on the `Actions` tab.
+Trigger the **Test Model** job again by clicking the **Run workflow** button on the **Actions** tab.
 
 The test workflow starts running. 
 
-Navigate to the workflow run on the `Actions` tab, click into the job, and expand the **Run testing script** step. 
+Navigate to the workflow run on the **Actions** tab, click into the job, and expand the **Run testing script** step. 
 
 You see a change in the performance results with OneDNN and ACL kernels being used. 
 

content/learning-paths/servers-and-cloud-computing/gh-runners/workflows.md

@@ -135,23 +135,23 @@ The `test-model.yml` file needs to be edited to be able to use the saved model f
 
 Complete the steps below to modify the testing workflow file:
 
-1. Navigate to the `Actions` tab on your GitHub repository. 
+1. Navigate to the **Actions** tab on your GitHub repository. 
 
-2. Click on `Train Model` on the left side of the page.
+2. Click on **Train Model** on the left side of the page.
 
-3. Click on the completed `Train Model` workflow. 
+3. Click on the completed **Train Model** workflow. 
 
 4. Copy the 11-digit ID number from the end of the URL in your browser address bar.
 
 ![#run-id](/images/run-id.png)
 
-5. Navigate back to the `Code` tab and open the file `.github/workflows/test-model.yml`. 
+5. Navigate back to the **Code** tab and open the file `.github/workflows/test-model.yml`. 
 
 6. Click the Edit button, represented by a pencil on the top right of the file contents.
 
 7. Update the `run-id` parameter with the 11 digit ID number you copied. 
 
-8. Save the file by clicking the `Commit changes` button.
+8. Save the file by clicking the **Commit changes** button.
 
 
 #### Run the workflow file
@@ -160,7 +160,7 @@ You are now ready to run the **Test Model** workflow.
 
 1. Navigate to the `Actions` tab and select the **Test Workflow** on the left side.
 
-2. Click the `Run workflow` button to run the workflow on the main branch.
+2. Click the **Run workflow** button to run the workflow on the main branch.
 
 ![#run-workflow](images/run-workflow.png)
 
@@ -170,7 +170,7 @@ Click on the workflow to view the output from each step.
 
 ![Actions_test](/images/actions_test.png)
 
-Click on the "Run testing script" step to see the accuracy of the model and a table of the results from the PyTorch profiler. 
+Click on the **Run testing script** step to see the accuracy of the model and a table of the results from the PyTorch profiler. 
 
 The output from is similar to: