Editorial improvements.

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md

@@ -13,7 +13,7 @@ This Learning Path is about TinyML. It is a starting point for learning how inno
 
 This section provides an overview of the domain with real-life use cases and available devices.
 
-TinyML represents a significant shift in Machine Learning deployment. Unlike traditional Machine Learning, which typically depends on cloud-based servers or high-performance hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and less processing capabilities. 
+TinyML represents a significant shift in Machine Learning deployment. Unlike traditional Machine Learning, which typically depends on cloud-based servers or high-performance hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and fewer processing capabilities. 
 
 TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems.
 
@@ -34,7 +34,7 @@ Here are some of the key benefits of TinyML on Arm:
 
 - **Scalability**: With billions of Arm devices in the market, TinyML is well-suited for scaling across industries, enabling widespread adoption of AI at the edge.
 
-TinyML is being deployed across multiple industries, enhancing everyday experiences and enabling groundbreaking solutions. The table below contains a few examples of TinyML applications.
+TinyML is being deployed across multiple industries, enhancing everyday experiences and enabling groundbreaking solutions. The table below shows some examples of TinyML applications.
 
 | Area                  |  Device, Arm IP             | Description                                                                                                |
 | ------                | -------                     | ------------                                                                                               |
@@ -46,7 +46,7 @@ TinyML is being deployed across multiple industries, enhancing everyday experien
 
 ### Examples of Arm-based devices
 
-There are many Arm-based devices that you can use for TinyML projects. Some of these are listed below, but the list is not exhaustive.
+There are many Arm-based devices that you can use for TinyML projects. Some of these are detailed below, but the list is not exhaustive.
 
 #### Raspberry Pi 4 and 5
 

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md

@@ -7,18 +7,18 @@ cascade:
 
 minutes_to_complete: 40
 
-who_is_this_for: This is an introductory topic for developers and data scientists who are new to Tiny Machine Learning (TinyML) and interested in exploring its potential using PyTorch and ExecuTorch.
+who_is_this_for: This is an introductory topic for developers and data scientists new to Tiny Machine Learning (TinyML) who want to explore its potential using PyTorch and ExecuTorch.
 
 learning_objectives:
-    - Describe what makes TinyML different from other AI domains.
+    - Describe what differentiates TinyML from other AI domains.
     - Describe the benefits of deploying AI models on Arm-based edge devices.
-    - Select Arm-based devices for TinyML.
-    - Install and configure a TinyML development environment using ExecuTorch and the Corstone-320 FVP.
+    - Identify suitable Arm-based devices for TinyML applications.
+    - Set up and configure a TinyML development environment using ExecuTorch and Corstone-320 FVP.
 
 prerequisites:
     - Basic knowledge of Machine Learning concepts.
     - A Linux host machine or VM running Ubuntu 22.04 or higher.
-    - A [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/) or an Arm license to run the Corstone-300 Fixed Virtual Platform (FVP).
+    - A [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/) or an Arm license to run the Corstone-320 Fixed Virtual Platform (FVP).
 
 
 author_primary: Dominica Abena O. Amanfo

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md

@@ -10,7 +10,7 @@ layout: "learningpathall"
 
 ## Define a small neural network using Python
 
-With the development environment ready, you can create a simple PyTorch model to test the setup.
+With your development environment set up, you can create a simple PyTorch model to test the setup.
 
 This example defines a small feedforward neural network for a classification task. The model consists of two linear layers with ReLU activation in between.
 

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

@@ -74,4 +74,4 @@ Your next steps depend on your hardware.
 
 If you have the Grove Vision AI Module, proceed to [Set up the Grove Vision AI Module V2 Learning Path](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/).
 
-If you do not have the Grove Vision AI Module, you can use the Corstone-300 FVP instead. See the Learning Path [Set up the Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/).
+If you do not have the Grove Vision AI Module, you can use the Corstone-320 FVP instead. See the Learning Path [Set up the Corstone-320 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/).

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

@@ -17,7 +17,7 @@ cd $HOME/executorch/examples/arm
 ./setup.sh --i-agree-to-the-contained-eula
 ```
 
-After the script has finished running, it prints a command to run to finalize the installation. This will add the FVP executables to your path.
+After the script has finished running, it prints a command to run to finalize the installation. This step adds the FVP executables to your system path.
 
 ```bash
 source $HOME/executorch/examples/arm/ethos-u-scratch/setup_path.sh