Merge pull request #2274 from annietllnd/main

Fix skillevels across content

Author: pareenaverma

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md

@@ -7,41 +7,41 @@ cascade:
 
 minutes_to_complete: 90
 
-who_is_this_for: This topic is for machine learning engineers, embedded AI developers, and researchers interested in deploying TinyML models for NLP on Arm-based edge devices using PyTorch and ExecuTorch. 
+who_is_this_for: This topic is for machine learning engineers, embedded AI developers, and researchers interested in deploying TinyML models for NLP on Arm-based edge devices using PyTorch and ExecuTorch.
 
-learning_objectives: 
+learning_objectives:
     - Train a custom CNN-based sentiment classification model implemented in PyTorch.
     - Optimize and convert the model using ExecuTorch for Arm-based edge devices.
     - Deploy and run inference on the Corstone-320 FVP.
 
 prerequisites:
-   - Basic knowledge of machine learning concepts. 
-   - It is advised to complete The Learning Path, [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm) before starting this learning path. 
+   - Basic knowledge of machine learning concepts.
+   - It is advised to complete The Learning Path, [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm) before starting this learning path.
    - Familiarity with Python and PyTorch.
    - A Linux host machine or VM running Ubuntu 22.04 or higher.
-   - An Arm license to run the examples on the Corstone-320 Fixed Virtual Platform (FVP), for hands-on deployment.  
+   - An Arm license to run the examples on the Corstone-320 Fixed Virtual Platform (FVP), for hands-on deployment.
 
 
 author: Dominica Abena O. Amanfo
 
 ### Tags
-skilllevels: Intermediate 
+skilllevels: Introductory
 subjects: ML
 armips:
     - Cortex-A
 tools_software_languages:
-    - tinyML 
-    - CNN 
+    - tinyML
+    - CNN
     - PyTorch
     - ExecuTorch
-    
+
 operatingsystems:
     - Linux
 
 
 further_reading:
     - resource:
-        title: Run Llama 3 on a Raspberry Pi 5 using ExecuTorch 
+        title: Run Llama 3 on a Raspberry Pi 5 using ExecuTorch
         link: /learning-paths/embedded-and-microcontrollers/rpi-llama3
         type: website
     - resource:

content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md

@@ -5,7 +5,7 @@ minutes_to_complete: 10
 
 who_is_this_for: This is a topic for users of µVision who want to migrate to the new project format (csolution) required by CMSIS-Toolbox.
 
-learning_objectives: 
+learning_objectives:
     - Import, convert, and build uvprojx-based projects in Keil Studio.
     - Convert uvprojx-based projects in µVision.
     - Convert and build uvprojx-based projects on the command line.
@@ -19,7 +19,7 @@ prerequisites:
 author: Christopher Seidl
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Advanced
 subjects: Performance and Architecture
 armips:
     - Cortex-M
@@ -43,7 +43,7 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/internet-of-things-blog/posts/keil-mdk-version-6
         type: blog
     - resource:
-        title: keil.arm.com 
+        title: keil.arm.com
         link: https://keil.arm.com
         type: website
 

content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md

@@ -18,7 +18,7 @@ prerequisites:
 author: Dawid Borycki
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Introductory
 subjects: Migration to Arm
 armips:
     - Cortex-A

content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md

@@ -5,7 +5,7 @@ minutes_to_complete: 25
 
 who_is_this_for: Software developers of Android applications and mobile games who are interested in learning how to enable Arm Fixed Rate Compression (AFRC) to improve performance.
 
-learning_objectives: 
+learning_objectives:
     - Query for fixed-rate compression support.
     - Specify what compression to use.
     - Verify that compression is applied.
@@ -18,7 +18,7 @@ prerequisites:
 author: Jose-Emilio Munoz-Lopez
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Advanced
 subjects: Graphics
 armips:
     - Mali

content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md

@@ -5,7 +5,7 @@ minutes_to_complete: 40
 
 who_is_this_for: Unity developers wanting to analyze the performance of their apps on Android devices
 
-learning_objectives: 
+learning_objectives:
     - Deploy to Android
     - Profile code running on an Android device
     - Analyze performance data
@@ -19,7 +19,7 @@ prerequisites:
 author: Arm
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Introductory
 subjects: Performance and Architecture
 armips:
     - armv8

content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md

@@ -5,7 +5,7 @@ minutes_to_complete: 120
 
 who_is_this_for: This Learning Path is for Vulkan developers who are familiar with rendering and are interested in deploying ray tracing in their applications.
 
-learning_objectives: 
+learning_objectives:
     - Describe how the Vulkan ray tracing API works.
     - Describe how to use ray tracing to implement realistic shadows, reflections, and refractions.
     - Implement basic ray tracing effects in a Vulkan renderer.
@@ -18,7 +18,7 @@ prerequisites:
 author: Iago Calvo Lista
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Advanced
 subjects: Graphics
 armips:
     - Mali

content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md

@@ -5,7 +5,7 @@ minutes_to_complete: 90
 
 who_is_this_for: Developers who want to optimize their Unity apps on Android
 
-learning_objectives: 
+learning_objectives:
     - Use Arm Neon intrinsics in your Unity C# scripts
     - Optimize your code
     - Collect and compare performance data using the Unity Profiler and Analyzer tools
@@ -19,7 +19,7 @@ prerequisites:
 author: Arm
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Advanced
 subjects: Gaming
 armips:
     - armv8