production publish

.wordlist.txt

@@ -3312,7 +3312,6 @@ WGSL
 WPR
 WebGL
 WebGPU
-WebGPU’s
 Xperf
 andc
 andnot
@@ -3352,7 +3351,6 @@ LastWriteTime
 LiteRT
 OV
 Seeed
-WebGPU’s
 WiseEye
 Yolov
 blp
@@ -3384,7 +3382,7 @@ Dsouza
 FGCT
 GCT
 GCs
-GC’s
+GC's
 HNso
 HeapRegionSize
 HugePages
@@ -3400,7 +3398,6 @@ Preema
 Roesch
 Sourcefire
 TPACKET
-WebGPU’s
 Whitepaper
 YGCT
 axion
@@ -3431,7 +3428,6 @@ EOF
 EVCLI
 EVidence
 Evcli
-GC’s
 GenerateMatrix
 ImageCapture
 InputStream
@@ -3456,7 +3452,8 @@ TestOpenCV
 TrustedFirmware
 Veraison
 WeatherForecast
-WebGPU’s
+weatherForecast
+WebGPU's
 Wiredtiger
 androidml
 ar
@@ -3510,4 +3507,5 @@ unutilized
 vLLM
 veraison
 verifier
-vllm
+vllm
+observables

content/install-guides/skopeo.md

@@ -1,6 +1,5 @@
 ---
 title: Skopeo
-draft: true
 author_primary: Jason Andrews
 minutes_to_complete: 10
 official_docs: https://github.com/containers/skopeo
@@ -24,7 +23,7 @@ Skopeo is a command-line utility that performs various operations on container i
 
 This article explains how to install Skopeo for Ubuntu on Arm.
 
-Skopeo is available for Windows, macOS, and Linux and supports the Arm architecture. Refer to [Installing Skopeo](https://github.com/containers/skopeo/blob/main/install.md) for information about other operating systems and architectures.
+Skopeo is available for Windows, macOS, and Linux and supports the Arm architecture. See [Installing Skopeo](https://github.com/containers/skopeo/blob/main/install.md) for further information about other operating systems and architectures.
 
 ## What should I consider before installing Skopeo on Arm?
 
@@ -56,13 +55,13 @@ Confirm the installation by checking the version:
 skopeo --version
 ```
 
-To see the help message:
+To see the help message use this command:
 
 ```bash
 skopeo --help
 ```
 
-The output is:
+The output that you will see should be:
 
 ```output
 Various operations with container images and container image registries
@@ -72,44 +71,44 @@ Usage:
   skopeo [command]
 
 Available Commands:
-  copy                                          Copy an IMAGE-NAME from one location to another
-  delete                                        Delete image IMAGE-NAME
-  generate-sigstore-key                         Generate a sigstore public/private key pair
-  help                                          Help about any command
-  inspect                                       Inspect image IMAGE-NAME
-  list-tags                                     List tags in the transport/repository specified by the SOURCE-IMAGE
-  login                                         Login to a container registry
-  logout                                        Logout of a container registry
-  manifest-digest                               Compute a manifest digest of a file
-  standalone-sign                               Create a signature using local files
-  standalone-verify                             Verify a signature using local files
-  sync                                          Synchronize one or more images from one location to another
+  copy                                          Copy an IMAGE-NAME from one location to another.
+  delete                                        Delete image IMAGE-NAME.
+  generate-sigstore-key                         Generate a sigstore public/private key pair.
+  help                                          Help about any command.
+  inspect                                       Inspect image IMAGE-NAME.
+  list-tags                                     List tags in the transport/repository specified by the SOURCE-IMAGE.
+  login                                         Log in to a container registry.
+  logout                                        Log out of a container registry.
+  manifest-digest                               Compute a manifest digest of a file.
+  standalone-sign                               Create a signature using local files.
+  standalone-verify                             Verify a signature using local files.
+  sync                                          Synchronize one or more images from one location to another.
 
 Flags:
-      --command-timeout duration   timeout for the command execution
-      --debug                      enable debug output
-  -h, --help                       help for skopeo
-      --insecure-policy            run the tool without any policy check
-      --override-arch ARCH         use ARCH instead of the architecture of the machine for choosing images
-      --override-os OS             use OS instead of the running OS for choosing images
-      --override-variant VARIANT   use VARIANT instead of the running architecture variant for choosing images
-      --policy string              Path to a trust policy file
-      --registries.d DIR           use registry configuration files in DIR (e.g. for container signature storage)
-      --tmpdir string              directory used to store temporary files
-  -v, --version                    Version for Skopeo
+      --command-timeout duration   Timeout for the command execution.
+      --debug                      Enable debug output.
+  -h, --help                       Help for skopeo.
+      --insecure-policy            Run the tool without any policy check.
+      --override-arch ARCH         Use ARCH instead of the architecture of the machine for choosing images.
+      --override-os OS             Use OS instead of the running OS for choosing images.
+      --override-variant VARIANT   Use VARIANT instead of the running architecture variant for choosing images.
+      --policy string              Path to a trust policy file.
+      --registries.d DIR           Use registry configuration files in DIR (for example, for container signature storage).
+      --tmpdir string              Directory used to store temporary files.
+  -v, --version                    Version for Skopeo.
 
 Use "skopeo [command] --help" for more information about a command.
 ```
 
 ## How do I get started with Skopeo?
 
-Some commands to get you started with Skopeo are demonstrated below.
+You can use the commands listed below to get you started with Skopeo.
 
 ### How can I check if a container image supports Arm?
 
 To find out if an image is multi-architecture, including Arm, you can inspect the image's manifest.
 
-For example, to check if the dev container available for creating Arm Learning Paths supports the Arm architecture run:
+For example, to check if the dev container available for creating Arm Learning Paths supports the Arm architecture, run:
 
 ```bash
 skopeo inspect --raw docker://docker.io/armswdev/learn-dev-container:latest | jq '.manifests[] | select(.platform.architecture == "arm64")'
@@ -166,7 +165,7 @@ The output confirms that both `arm64` and `amd64` are supported architectures as
 
 ## What are some other uses for Skopeo?
 
-Copy an image from a registry to a local directory. This command is similar to `docker pull` and will copy the image from the remote registry to your local directory.
+Copy an image from a registry to a local directory. This command is similar to `docker pull` and copies the image from the remote registry to your local directory.
 
 ```bash
 skopeo copy docker://docker.io/armswdev/uname:latest dir:./uname

content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md

@@ -1,23 +1,23 @@
 ---
-title: Create and train a PyTorch model for digit classification
+title: Create and train a PyTorch model for digit classification using the MNIST dataset
 
 minutes_to_complete: 160
 
-who_is_this_for: This is an advanced topic for software developers interested in learning how to use PyTorch to create and train a feedforward neural network for digit classification. You will also learn how to use the trained model in an Android application. Finally, you will apply model optimizations.
+who_is_this_for: This is an advanced topic for software developers interested in learning how to use PyTorch to create and train a feedforward neural network for digit classification, and also software developers interested in learning how to use and apply optimizations to the trained model in an Android application.
 
 learning_objectives:
     - Prepare a PyTorch development environment.
     - Download and prepare the MNIST dataset.
-    - Create a neural network architecture using PyTorch.
-    - Train a neural network using PyTorch.
-    - Create an Android app and loading the pre-trained model.
+    - Create and train a neural network architecture using PyTorch.
+    - Create an Android app and load the pre-trained model.
     - Prepare an input dataset.
     - Measure the inference time.
     - Optimize a neural network architecture using quantization and fusing.
-    - Use an optimized model in the Android application.
+    - Deploy an optimized model in an Android application.
 
 prerequisites:
-    - A computer that can run Python3, Visual Studio Code, and Android Studio. The OS can be Windows, Linux, or macOS.
+    - A machine that can run Python3, Visual Studio Code, and Android Studio. 
+    - For the OS, you can use Windows, Linux, or macOS.
 
 
 author_primary: Dawid Borycki

content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_next-steps.md

@@ -4,7 +4,7 @@
 # ================================================================================
 
 next_step_guidance: >
-   Proceed to Use Keras Core with TensorFlow, PyTorch, and JAX backends to continue exploring Machine Learning.
+   To continue exploring Machine Learning, you can now learn about using Keras Core with TensorFlow, PyTorch, and JAX backends.
 
 # 1-3 sentence recommendation outlining how the reader can generally keep learning about these topics, and a specific explanation of why the next step is being recommended.
 

content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_review.md

@@ -15,31 +15,31 @@ review:
         question: >        
             Does the input layer of the model flatten the 28x28 pixel image into a 1D array of 784 elements?
         answers:
-            - "Yes"
-            - "No"
+            - "Yes."
+            - "No."
         correct_answer: 1
         explanation: >
             Yes, the model uses nn.Flatten() to reshape the 28x28 pixel image into a 1D array of 784 elements for processing by the fully connected layers.
     - questions:
         question: >
-            Will the model make random predictions if it’s run before training?
+            Will the model make random predictions if it is run before training?
         answers:
-            - "Yes"
-            - "No"
+            - "Yes."
+            - "No."
         correct_answer: 1
         explanation: >
-            Yes, however in such the case the model will produce random outputs, as the network has not been trained to recognize any patterns from the data. 
+            Yes, however in this scenario the model will produce random outputs, as the network has not been trained to recognize any patterns from the data. 
     - questions:
         question: >
-            Which loss function was used to train the PyTorch model on the MNIST dataset?
+            Which loss function did you use to train the PyTorch model on the MNIST dataset in this Learning Path?
         answers:
-            - Mean Squared Error Loss
-            - Cross Entropy Loss
-            - Hinge Loss
+            - Mean Squared Error Loss.
+            - Cross-Entropy Loss.
+            - Hinge Loss.
             - Binary Cross-Entropy Loss
         correct_answer: 2
         explanation: >
-            Cross Entropy Loss was used to train the model because it is suitable for multi-class classification tasks like digit classification. It measures the difference between the predicted probabilities and the true class labels, helping the model learn to make accurate predictions. 
+            Cross-Entropy Loss was used to train the model as it is suitable for multi-class classification such as digit classification. It measures the difference between the predicted probabilities and the true class labels, helping the model to learn to make accurate predictions. 
 
 # ================================================================================
 #       FIXED, DO NOT MODIFY

content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/app.md

@@ -7,29 +7,31 @@ weight: 10
 layout: "learningpathall"
 ---
 
-You are now ready to run the Android application. You can use an emulator or a physical device. 
-
-The screenshots below show an emulator. 
+You are now ready to run the Android application. The screenshots below show an emulator, but you can also use a physical device. 
 
 To run the app in Android Studio using an emulator, follow these steps:
 
 1. Configure the Emulator:
-* Go to Tools > Device Manager (or click the Device Manager icon on the toolbar).
-* Click Create Device to set up a new virtual device (if you haven’t done so already).
-* Choose a device model, such as Pixel 4, and click Next.
-* Select a system image, such as Android 11, API level 30, and click Next.
-* Review the settings and click Finish to create the emulator.
+
+* Go to **Tools** > **Device Manager**, or click the Device Manager icon on the toolbar.
+* Click **Create Device** to set up a new virtual device, if you haven’t done so already.
+* Choose a device model, such as the Pixel 4, and click **Next**.
+* Select a system image, such as Android 11, API level 30, and click **Next**.
+* Review the settings, and click **Finish** to create the emulator.
 
 2. Run the App:
-* Make sure the emulator is selected in the device dropdown menu in the toolbar (next to the “Run” button).
-* Click the Run button (a green triangle). Android Studio will build the app, install it on the emulator, and launch it.
 
-3. View the App on the Emulator: Once the app is installed, it will automatically open on the emulator screen, allowing you to interact with it as if it were running on a real device.
+* Make sure the emulator is selected in the device drop-down menu in the toolbar, next to the **Run** button.
+* Click the **Run** button, which is a green triangle. Android Studio builds the app, installs it on the emulator, and then launches it.
+
+3. View the App on the Emulator:
+
+* Once the app is installed, it automatically opens on the emulator screen, allowing you to interact with it as if it were running on a real device.
 
-Once the application is started, click the Load Image button. It will load a randomly selected image. Then, click Run Inference to recognize the digit. The application will display the predicted label and the inference time as shown below:
+Once the application starts, click the **Load Image** button. It loads a randomly-selected image. Then, click **Run Inference** to recognize the digit. The application displays the predicted label and the inference time as shown below:
 
-![img](Figures/05.png)
+![img alt-text#center](Figures/05.png "Figure 7. Digit Recognition 1")
 
-![img](Figures/06.png)
+![img alt-text#center](Figures/06.png "Figure 8. Digit Recognition 2")
 
-In the next step you will learn how to further optimize the model.
+In the next step of this Learning Path, you will learn how to further optimize the model.