Updated structure

Author: dominica-of

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md

@@ -18,13 +18,7 @@ The host machine is where you will perform most of your development work, especi
 
 - The Ubuntu version should be `20.04 or higher`. 
 - If you do not have the board, the `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture.
-- Also, though Executorch supports Windows via WSL, it is limited in resource.
-
-
-### Corstone-300 FVP Setup for ExecuTorch
-
-To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html). Follow this tutorial till the **"Install the TOSA reference model"** Section. It should be the last thing you do from this tutorial.
-
+- Though Executorch supports Windows via WSL, it is limited in resource.
 
 
 ## Install Executorch
@@ -65,5 +59,5 @@ sudo apt install screen
 ```
 
 ## Next Steps
-1. If you don't have access to the physical board: Skip to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-6-FVP.md)
-2. If you have access to the board: Skip to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-6-Grove.md)
+1. If you don't have access to the physical board: Go to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/)
+2. If you have access to the board: Go to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/)

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md

@@ -14,4 +14,4 @@ To install and set up the Corstone-300 FVP on your machine, refer to [Building a
 
 
 ## Next Steps
-1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md) to test your environment setup.
+1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8/) to test your environment setup.

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md

@@ -8,9 +8,7 @@ weight: 8 # 1 is first, 2 is second, etc.
 layout: "learningpathall"
 ---
 ## Before you begin 
-Only follow this part of the tutorial if you have the board. 
-
-Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path).
+Only follow this part of the tutorial if you have the board. Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path).
 
 
 ### Compilers 
@@ -52,10 +50,8 @@ edge-impulse-daemon
 ```
 Follow the prompts to log in.
 
-5. Verify your board is connected
-
-If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse.
+5. If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse.
 
 
 ## Next Steps
-1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md) to test your environment setup.
+1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8/) to test your environment setup.

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md

@@ -1,18 +1,18 @@
 ---
 title: Troubleshooting and Best Practices
-weight: 7
+weight: 10
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 ## Troubleshooting
 - If you encounter permission issues, try running the commands with sudo.
 - Ensure your Grove - Vision AI Module V2 is properly connected and recognized by your computer.
-- If Edge Impulse CLI fails to detect your device, try unplugging and replugging the USB cable.
+- If Edge Impulse CLI fails to detect your device, try unplugging, hold the **Boot button** and replug the USB cable. Release the button once you replug.
 
 ## Best Practices
 - Always cross-compile your code on the host machine to ensure compatibility with the target Arm device.
 - Utilize model quantization techniques to optimize performance on constrained devices like the Grove - Vision AI Module V2.
 - Regularly update your development environment and tools to benefit from the latest improvements in TinyML and edge AI technologies
 
-You've now set up your environment for TinyML development on the Grove - Vision AI Module V2. In the next modules, we'll explore data collection, model training, and deployment using PyTorch v2.0 and Executorch.
+You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Netrowk.