Corrected heading size

Author: madeline-underwood

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

@@ -1,5 +1,5 @@
 ---
-title: "Edge AI on Arm: PyTorch and ExecuTorch Rock Paper Scissors"
+title: "Edge AI on Arm: PyTorch and ExecuTorch rock-paper-scissors"
 
 minutes_to_complete: 60
 

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/env-setup-1.md

@@ -6,11 +6,9 @@ weight: 2
 layout: learningpathall
 ---
 
-## Set up your environment for Tiny Rock, Paper, Scissors on Arm (PyTorch + ExecuTorch)
+## Set up your environment for Tiny rock-paper-scissors on Arm 
 
-
-
-This Learning Path is a direct follow-up to [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm). While the previous Learning Path introduced the core concepts and toolchain, this one puts that knowledge into practice with a small, real-world example. You move from a simple [Feedforward Neural Network](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/4-build-model) to a practical computer vision task: a tiny Rock-Paper-Scissors game that runs efficiently on Arm-based edge devices.
+This Learning Path is a direct follow-up to [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm). While the previous Learning Path introduced the core concepts and toolchain, this one puts that knowledge into practice with a small, real-world example. You move from a simple [Feedforward Neural Network](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/4-build-model) to a practical computer vision task: a tiny rock-paper-scissors game that runs efficiently on Arm-based edge devices.
 
 You will train a lightweight CNN to classify images of the letters R, P, and S as "rock," "paper," or "scissors." The script uses a synthetic data renderer to create a large dataset of these images with various transformations and noise, eliminating the need for a massive real-world dataset.
 

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/fine-tune-2.md

@@ -1,5 +1,5 @@
 ---
-title: Train and Test the Rock-Paper-Scissors Model
+title: Train and Test the rock-paper-scissors Model
 weight: 3
 
 ### FIXED, DO NOT MODIFY

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/fvp-3.md

@@ -6,9 +6,9 @@ weight: 4
 layout: learningpathall
 ---
 
-# Compile and run the rock-paper-scissors model on Corstone-320 FVP
+## Compile and run the rock-paper-scissors model on Corstone-320 FVP
 
-This section shows how to compile your trained Rock, Paper, Scissors model and run it on the Corstone-320 Fixed Virtual Platform (FVP), a simulated Arm-based edge device. This completes the end-to-end workflow for deploying a TinyML model for on-device inference.
+This section shows how to compile your trained rock-paper-scissors model and run it on the Corstone-320 Fixed Virtual Platform (FVP), a simulated Arm-based edge device. This completes the end-to-end workflow for deploying a TinyML model for on-device inference.
 
 ## Compile and build the executable
 
@@ -75,7 +75,7 @@ Build the executable:
 cmake --build "$ET_HOME/examples/arm/executor_runner/cmake-out" -j --target arm_executor_runner
 ```
 
-### Run the model on the FVP
+## Run the model on the FVP
 With the `arm_executor_runner` executable ready, you can now run it on the Corstone-320 FVP to see the model on a simulated Arm device.
 
 ```bash