Update inference.md

Author: dawidborycki

content/learning-paths/laptops-and-desktops/win_python_onnx/inference.md

@@ -11,41 +11,47 @@ layout: "learningpathall"
 You will now use the implemented code to run inference.
 
 ## Packages
-Start by activating the virtual environment and installing Python packages:
+Start by activating the virtual environment and installing the necessary Python packages. Activate the virtual environment:
+
 ```console
 venv-x64\Scripts\activate.bat
 ```
 
-Then, type:
+Then install required packages:
 ```console
 py -V:3.13 -m pip install onnxruntime numpy matplotlib wget torchvision torch
 ```
 
 ## Running Inference
-To run the inference type the following
+To perform inference, run the following command:
+
 ```console
 py -V:3.13 .\main.py  
 ```
 
-The code will display the sample inference result like shown below:
-
-If you close the image, you will see the computation time:
+The code will display a sample inference result similar to the image below:
+![fig1](figures/01.png)
+Upon closing the displayed image, the script will output the computation time:
 ```output
 PS C:\Users\db\onnx> py -V:3.13 .\main.py  
 Computation time: 95.854 ms
 PS C:\Users\db\onnx> py -V:3.13 .\main.py
 Computation time: 111.230 ms
 ```
-![fig1](figures/01.png)
 
-To compare these with Windows Arm 64, repeat the following for the Arm-64 Python architecture:
+
+To compare results with Windows Arm 64, repeat the steps below using the Arm-64 Python architecture. Activate the Arm64 virtual environment and install packages:
 ```console
 venv-arm64\Scripts\activate.bat
 py -V:3.13-arm64 -m pip install onnxruntime numpy matplotlib wget torchvision torch
+```
+
+Run inference using Arm64:
+```console
 py -V:3.13-arm64 main.py
 ```
 
-Note the above will work, when the onnx runtime will become available for Windows on Arm 64.
+Note: The above Arm64 commands will function properly once ONNX Runtime becomes available for Windows Arm 64.
 
 ## Summary
-
+In this learning path, you’ve learned how to use ONNX Runtime to perform inference on the MNIST dataset. You prepared your environment, implemented the necessary Python code, and measured the performance of your inference tasks.