Update documentation for clarity and consistency in titles, image descriptions, and instructional notes

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/edge/_index.md

@@ -1,5 +1,5 @@
 ---
-title: Learn how to run AI on edge devices using Arduino Nano RP2040
+title: Learn how to run AI on Edge devices using Arduino Nano RP2040
 
 minutes_to_complete: 90
 

content/learning-paths/embedded-and-microcontrollers/edge/connect-and-set-up-arduino.md

@@ -32,9 +32,9 @@ To visualize the output of the voice command model, you will use a simple LED ci
 - **Anode (long leg) of the LED** → Connect to **GPIO pin D2** via the 220Ω resistor
 - **Cathode (short leg)** → Connect to **GND**
 
-![example image alt-text#center](images/led_connection.png "Figure 14. Circuit Connection")
+![example image alt-text#center](images/led_connection.png "Circuit Connection")
 
-![example image alt-text#center](images/led_connection_schematic.png "Figure 15. Circuit Schematic Connection")
+![example image alt-text#center](images/led_connection_schematic.png "Circuit Schematic Connection")
 
 ### Step 2: Set Up the Arduino IDEs
 
@@ -48,7 +48,7 @@ This guide will walk you through:
 - Adding the board support package for the Nano RP2040
 
 {{% notice Note %}}
-**Note:** Follow every instruction in the guide **except** `How do I set up the Raspberry Pi Pico W?`, as it is not needed for this project.
+Follow every instruction in the guide *except* `How do I set up the Raspberry Pi Pico W?`, as it is not needed for this project.
 {{% /notice %}}
 
 ### Step 3: Select Your Board and Port in the Arduino IDE

content/learning-paths/embedded-and-microcontrollers/edge/overview.md

@@ -7,14 +7,14 @@ layout: learningpathall
 ---
 
 {{< notice Note >}}
-This section introduces the key concepts that form the foundation of this Learning Path. Review it before starting the step-by-step tutorial.
+This section introduces the key concepts that form the foundation of this Learning Path. Review it before starting this Learning Path.
 {{< /notice >}}
 
 # Edge AI
 
 Edge AI refers to artificial intelligence models that run directly on edge devices, processing data locally rather than relying on cloud computing. These models are optimized for real-time decision-making on resource-constrained devices such as microcontrollers, embedded systems, and IoT sensors.
 
-**TinyML (tiny machine learning)** is a subset of Edge AI that focuses on deploying machine learning models on ultra-low-power microcontrollers. These devices typically have less than 1 MB of flash memory and a few hundred kilobytes of RAM, and they are designed to run for extended periods on minimal power, which is often for years on a single coin-cell battery.
+TinyML (Tiny Machine Learning) is a subset of Edge AI that focuses on deploying machine learning models on ultra-low-power microcontrollers. These devices typically have less than 1 MB of flash memory and a few hundred kilobytes of RAM, and they are designed to run for extended periods on minimal power, which is often for years on a single coin-cell battery.
 
 Despite these constraints, TinyML enables on-device inference, allowing edge devices to make intelligent decisions in real time without sending data to the cloud. This makes smart functionality possible in low-cost, battery-powered devices used in applications such as environmental monitoring, wearables, smart homes, and industrial sensors.
 
@@ -24,28 +24,35 @@ Key features of Edge AI and TinyML include:
 
 - **Low power consumption**: designed to run on batteries or harvested energy for months or years  
 - **Small model size**: models are optimized (for example, quantized or pruned) to fit into a few kilobytes or megabytes  
-- **Limited compute and memory**: typically operates with under 1 MB of RAM and very limited storage  
-- **Real-time inference**: enables immediate local decision-making (for example, wake-word detection)  
+- **Limited compute and memory**: typically operate with under 1 MB of RAM and very limited storage  
+- **Real-time inference**: immediate local decision-making (for example, wake-word detection)  
 - **Low latency**: no reliance on cloud; inference is performed on-device  
 - **Applications**: often used in audio classification, gesture detection, and anomaly detection  
-- **Example devices**: Arduino Nano 33 BLE Sense, STM32 MCUs, Raspberry Pi Pico, Arduino Nano RP2040 Connect, and more
 
-## Running AI Models on Resource-Constrained Devices
+Example devices include Arduino Nano 33 BLE Sense, STM32 MCUs, Raspberry Pi Pico, and Arduino Nano RP2040 Connect.
 
-Running AI on edge devices presents challenges. These devices often lack high-performance CPUs or GPUs, making compute power and memory usage key concerns. Additionally, because many edge devices run on batteries, energy efficiency is critical.
+## Run AI models on resource-constrained devices
 
-To address these constraints, models are optimized through techniques such as quantization, pruning, and knowledge distillation. These reduce model size and resource requirements while maintaining acceptable accuracy.
+Running AI on edge devices presents challenges. These devices often lack high-performance CPUs or GPUs, making compute power and memory usage key concerns. Since many edge devices run on batteries, energy efficiency is also critical.
 
-## Edge AI implementation workflow
+To overcome these constraints, models are optimized using techniques such as *quantization*, *pruning*, and *knowledge distillation*. These methods reduce model size and resource requirements while maintaining acceptable accuracy.
 
-Implementing Edge AI typically follows this workflow:
+## Edge AI workflow
 
-- Data collection using sensors such as microphones, motion detectors, or cameras
-- Model training on high-performance systems such as cloud servers or local workstations
-- Model optimization to reduce size and computation overhead for deployment
-- Deployment to microcontrollers or Arm-based processors
-- On-device inference, where the model makes real-time decisions based on local data
+[1] Data collection  
+→ Sensors capture data (such as audio, motion, and vision)  
 
+[2] Model training  
+→ Use cloud or local compute for training  
+
+[3] Model optimization  
+→ Apply quantization, pruning, or distillation  
+
+[4] Deployment  
+→ Flash model onto Arm-based edge device  
+
+[5] On-device inference  
+→ Device makes real-time predictions locally
 
 ## Applications of Edge AI
 
@@ -60,7 +67,7 @@ Edge AI is used in a wide range of real-world applications:
 
 ## The BLERP framework
 
-To remember the benefits of Edge AI, the **BLERP** mnemonic highlights five critical aspects:
+To recall the benefits of Edge AI, the **BLERP** mnemonic highlights five critical aspects:
 
 | Area       | Description                                                                                                                                                           |
 |------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|

content/learning-paths/embedded-and-microcontrollers/edge/program-and-deployment.md

@@ -327,6 +327,5 @@ Your Serial Monitor should look like the image below.
 
 ![example image alt-text#center](images/serial_monitor.png "Figure 16. Circuit Connection")
 
-{{% notice Congratulations %}}
-You’ve successfully programmed your first TinyML microcontroller! You've also built a functional, smart system to control an LED with your voice.
-{{% /notice %}}
+Congratulations, you’ve successfully programmed your first TinyML microcontroller! You've also built a functional, smart system to control an LED with your voice.
+

content/learning-paths/embedded-and-microcontrollers/edge/software-edge-impulse.md

@@ -6,7 +6,7 @@ weight: 3
 layout: learningpathall
 ---
 
-# Using Edge Impulse to Train TinyML models
+## Using Edge Impulse
 
 Now that you've learned the foundational concepts of Edge AI and TinyML, it's time to move from theory to practice.
 
@@ -22,17 +22,19 @@ Edge Impulse is a development platform that simplifies building, training, and d
 
 | Feature               | Description                                                                                         |
 |-----------------------|-----------------------------------------------------------------------------------------------------|
-| **Data collection**   | Collects data from sensors, such as accelerometers, microphones, and cameras in real time.              |
-| **Preprocessing**     | Provides tools for signal processing and feature extraction tailored for embedded systems.          |
-| **Model training**    | Supports built-in ML algorithms and integrates with frameworks like TensorFlow.                     |
-| **Edge optimization** | Automatically optimizes models to run efficiently on low-power edge hardware.                      |
-| **Deployment**        | Enables seamless deployment to microcontrollers, RTOS-based boards, and Linux devices.             |
-| **Web-based platform**| Fully browser-based interface for managing projects and workflows without needing local setup.     |
+| **Data collection**   | collects data from sensors, such as accelerometers, microphones, and cameras in real time              |
+| **Preprocessing**     | provides tools for signal processing and feature extraction tailored for embedded systems          |
+| **Model training**    | supports built-in ML algorithms and integrates with frameworks like TensorFlow                     |
+| **Edge optimization** | automatically optimizes models to run efficiently on low-power edge hardware                      |
+| **Deployment**        | enables seamless deployment to microcontrollers, RTOS-based boards, and Linux devices             |
+| **Web-based platform**| fully browser-based interface for managing projects and workflows without needing local setup     |
 
 ---
 
 ## Why it's important in Edge AI and TinyML
 
+Edge Impulse offers the following benefits:
+
 - Bridges the gap between machine learning and embedded development
 - Accelerates prototyping and deployment of AI features directly on hardware
 - Supports TinyML applications that run on devices with very limited memory and compute power
@@ -46,7 +48,7 @@ In the following sections, you'll walk through each key page on the Edge Impulse
 
 
 
-![Screenshot of the Edge Impulse home page showing the main navigation and project dashboard alt-text#center](images/1.png "Figure 1. Home page of Edge Impulse")
+![Screenshot of the Edge Impulse home page showing the main navigation and project dashboard alt-text#center](images/1.png "Home page of Edge Impulse website")
 
 
 ### Create a new project
@@ -57,7 +59,7 @@ For example, if you're building a keyword spotting model, you might name it `Wak
 
 You'll also need to select the appropriate **project type** and **project settings**, as shown in the snapshot below.
 
-![Screenshot showing the new project creation page in Edge Impulse, with fields for project name, type, and target device alt-text#center](images/3.png "Figure 2. New project setup")
+![Screenshot showing the new project creation page in Edge Impulse, with fields for project name, type, and target device alt-text#center](images/3.png "New project setup")
 
 ### Configure the target device
 
@@ -67,7 +69,7 @@ You can find the full specifications for the Arduino Nano RP2040 Connect on [Ard
 
 Follow the settings shown in the screenshot to complete the configuration.
 
-![Screenshot showing the Edge Impulse device configuration page with Arduino Nano RP2040 Connect selected alt-text#center](images/4.png "Figure 3. Configure Arduino Nano RP2040")
+![Screenshot showing the Edge Impulse device configuration page with Arduino Nano RP2040 Connect selected alt-text#center](images/4.png "Configure Arduino Nano RP2040")
 
 
 ### Add the dataset
@@ -82,14 +84,14 @@ git clone https://github.com/e-dudzi/Learning-Path.git
 
 The repository contains a `Dataset.zip` file with the dataset used in this project. Extract the contents to your local machine. For convenience, the dataset is already split into **training** and **testing** sets.
 
-![Screenshot showing the Edge Impulse interface with the Add existing data panel open, used to upload pre-recorded datasets alt-text#center](images/6.png "Figure 4. Add existing data")
+![Screenshot showing the Edge Impulse interface with the Add existing data panel open, used to upload pre-recorded datasets alt-text#center](images/6.png "Adding existing data")
 
 
 {{% notice Note %}}
 Do not check the green highlighted area during upload. The dataset already includes metadata. Enabling that option may result in much slower upload times and is unnecessary for this project.
 {{% /notice %}}
 
-![Screenshot showing the Data acquisition tab in Edge Impulse with uploaded samples organized by label alt-text#center](images/7.png "Figure 5. Dataset overview")
+![Screenshot showing the Data acquisition tab in Edge Impulse with uploaded samples organized by label alt-text#center](images/7.png "Dataset overview")
 
 ### Dataset uploaded successfully
 
@@ -104,7 +106,7 @@ This dataset is made up of four labels:
 - unknown
 {{% /notice %}}
 
-![Screenshot showing the Impulse design interface in Edge Impulse with input, processing, and learning blocks configured alt-text#center](images/8.png "Figure 6. Dataset overview")
+![Screenshot showing the Impulse design interface in Edge Impulse with input, processing, and learning blocks configured alt-text#center](images/8.png "Dataset overview")
 
 ### Create the impulse
 
@@ -115,7 +117,7 @@ Click **Create impulse** in the menu and configure it as shown in the screenshot
 After configuring the impulse, make sure to **save your changes**.
 
 
-![example image alt-text#center](images/9.png "Figure 7. Create Impulse")
+![example image alt-text#center](images/9.png "Create Impulse")
 
 ### Configure the MFCC block
 
@@ -127,7 +129,7 @@ Set the parameters exactly as shown in the screenshot. These settings determine
 
 These defaults are chosen for this Learning Path, but you can experiment with different values once you're more familiar with Edge Impulse.
 
-![Screenshot showing the MFCC configuration page in Edge Impulse with time and frequency parameters set for feature extraction alt-text#center](images/10.png "Figure 8. MFCC block configuration")
+![Screenshot showing the MFCC configuration page in Edge Impulse with time and frequency parameters set for feature extraction alt-text#center](images/10.png "MFCC block configuration")
 
 {{< notice Note >}}
 The green-highlighted section on the MFCC configuration page provides an estimate of how the model will perform on the target device. This includes memory usage (RAM and flash) and latency, helping ensure the model fits within hardware constraints.
@@ -139,7 +141,7 @@ After saving the MFCC parameters, the next step is to generate features from you
 
 When complete, you'll see a **2D feature plot** that shows how the data is distributed across the four labels: `on`, `off`, `noise`, and `unknown`. This helps visually confirm whether the classes are distinct and learnable.
 
-![Screenshot showing the feature explorer in Edge Impulse with a 2D visualization of four labeled audio classes alt-text#center](images/12.png "Figure 9. Feature explorer")
+![Screenshot showing the feature explorer in Edge Impulse with a 2D visualization of four labeled audio classes alt-text#center](images/12.png "Feature explorer")
 
 ### Set up the classifier
 
@@ -151,13 +153,13 @@ For this Learning Path, use a learning rate of `0.002` even though the screensho
 
 Once all parameters are set, click **Save and train** to begin training your model.
 
-![Screenshot showing the classifier configuration in Edge Impulse with neural network settings alt-text#center](images/13.png "Figure 10. Classifier settings")
+![Screenshot showing the classifier configuration in Edge Impulse with neural network settings alt-text#center](images/13.png "Classifier settings")
 
 ### Review model performance
 
 After training is complete, Edge Impulse will show the model’s performance metrics: **accuracy**, **loss**, and a **confusion matrix**.
 
-![Screenshot showing the model performance results in Edge Impulse including accuracy, loss, and confusion matrix alt-text#center](images/14.png "Figure 11. Model performance")
+![Screenshot showing the model performance results in Edge Impulse including accuracy, loss, and confusion matrix alt-text#center](images/14.png "Model performance")
 
 - **Accuracy**: how often the model correctly predicts labels  
 - **Loss**: how far off predictions are — lower values indicate better performance  
@@ -173,7 +175,7 @@ Review these results to see how well the model learned. If needed, adjust settin
 | Peak RAM usage   | 12.5 KB |
 | Flash usage      | 49.7 KB |
 
-![Screenshot showing performance metrics for the trained model on the target device alt-text#center](images/15.png "Figure 12. Model performance")
+![Screenshot showing performance metrics for the trained model on the target device alt-text#center](images/15.png "Model performance")
 
 You can also use the pre-trained model found in `ei-edge-ai-tutorials-arduino-1.0.1.zip` from the example repository.
 
@@ -187,7 +189,7 @@ To run the trained model on your Arduino Nano RP2040 Connect, export it as an Ar
 
 The model will be downloaded as a `.zip` file you can import into the Arduino IDE.
 
-![Screenshot showing the deployment options in Edge Impulse with Arduino library export selected alt-text#center](images/16.png "Figure 13. Model deployment")
+![Screenshot showing the deployment options in Edge Impulse with Arduino library export selected alt-text#center](images/16.png "Model deployment")
 
 ## Next steps