Editorial

Author: madeline-underwood

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

@@ -28,17 +28,25 @@ GitHub Actions runs workflow files to automate processes. Workflows run when spe
 
 [YAML](https://yaml.org/) defines a workflow. 
 
-Workflows specify how a job is triggered, the running environment, and the commands to run. 
+Workflows specify:
 
-The machine running workflows is called a _runner_.
+* How a job is triggered.
+* The running environment.
+* The commands to run. 
+
+The machine running the workflows is called a _runner_.
 
 ## Arm-hosted GitHub runners
 
 Hosted GitHub runners are provided by GitHub, so you do not need to set up and manage cloud infrastructure. Arm-hosted GitHub runners use the Arm architecture so you can build and test software without the necessity for cross-compiling or instruction emulation.
 
-Arm-hosted GitHub runners enable you to optimize your workflows, reduce cost, and improve energy consumption. 
+Arm-hosted GitHub runners enable you to:
+
+* Optimize your workflows.
+* Reduce cost.
+* Improve energy consumption. 
 
-Additionally, the Arm-hosted runners are preloaded with essential tools, making it easier for to develop and test your applications.
+Additionally, the Arm-hosted runners are preloaded with essential tools, which makes it easier for to develop and test your applications.
 
 Arm-hosted runners are available for Linux and Windows. This Learning Path uses Linux.
 

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

@@ -49,7 +49,7 @@ The transformations used when loading data are part of the preprocessing step, w
 
 In accordance with common machine learning practices, data is separated into training and testing data to avoid overfitting the neural network.
 
-Use this code to load the dataset:
+Here is the code to load the dataset:
 
 ```python
 transform = transforms.Compose([
@@ -68,7 +68,7 @@ The next step is to define a class for the model, listing the layers used.
 
 The model defines the forward pass function used at training time to update the weights. Additionally, the loss function and optimizer for the model are defined.
 
-Use this code that defines the model:
+Here is the code that defines the model:
 
 ```python
 class TrafficSignNet(nn.Module):
@@ -100,7 +100,7 @@ A training loop performs the actual training.
 
 The number of epochs is arbitrarily set to 10 for this example. When the training is finished, the model weights are saved to a `.pth` file.
 
-Use this code for the training loop:
+Here is the code for the training loop:
 
 ```python
 num_epochs = 10
@@ -143,7 +143,7 @@ Testing is done by loading the model that was saved after training and preparing
 
 As in training, transformations are used to load the test data from the GTSRB dataset.
 
-Use this code to load the model and the test data:
+Here is the code to load the model and the test data:
 
 ```python
 model_path = args.model if args.model else './models/traffic_sign_net.pth'