Merge pull request #480 from harvard-edge/475-some-notes-in-llm-and-o…

…ther-generic-issues

475 Some notes in llm and other generic issues

contents/labs/arduino/nicla_vision/kws/kws.qmd

@@ -267,7 +267,7 @@ void setup()
 Create two functions, `turn_off_leds()` function , to turn off all RGB LEDs
 
 ``` cpp
-**
+/*
  * @brief      turn_off_leds function - turn-off all RGB LEDs
  */
 void turn_off_leds(){
@@ -280,7 +280,7 @@ void turn_off_leds(){
 Another `turn_on_led()` function is used to turn on the RGB LEDs according to the most probable result of the classifier.
 
 ``` cpp
-/**
+/*
  * @brief      turn_on_leds function used to turn on the RGB LEDs
  * @param[in]  pred_index     
  *             no:       [0] ==> Red ON

contents/labs/arduino/nicla_vision/nicla_vision.qmd

@@ -6,9 +6,9 @@ These labs provide a unique opportunity to gain practical experience with machin
 
 ## Pre-requisites
 
-- **Nicla Vision Board**: Ensure you have the Nicla Vision board.
-- **USB Cable**: For connecting the board to your computer.
-- **Network**: With internet access for downloading necessary software.
+- **Nicla Vision Board** : Ensure you have the Nicla Vision board.
+- **USB Cable** : For connecting the board to your computer.
+- **Network** : With internet access for downloading necessary software.
 
 ## Setup
 

contents/labs/labs.qmd

@@ -54,15 +54,15 @@ These labs are designed for:
 
 Each lab follows a structured approach:
 
-1. **Introduction**: Explore the application and its significance in real-world scenarios.
+1. **Introduction** : Explore the application and its significance in real-world scenarios.
 
-2. **Setup**: Step-by-step instructions to configure the hardware and software environment.
+2. **Setup** : Step-by-step instructions to configure the hardware and software environment.
 
-3. **Deployment**: Guidance on training and deploying the pre-trained ML models on supported devices.
+3. **Deployment** : Guidance on training and deploying the pre-trained ML models on supported devices.
 
-4. **Exercises**: Hands-on tasks to modify and experiment with model parameters.
+4. **Exercises** : Hands-on tasks to modify and experiment with model parameters.
 
-5. **Discussion**: Analysis of results, potential improvements, and practical insights.
+5. **Discussion** : Analysis of results, potential improvements, and practical insights.
 
 ## Troubleshooting and Support
 

contents/labs/raspi/image_classification/image_classification.qmd

@@ -777,19 +777,19 @@ This Python script creates a web-based interface for capturing and organizing im
 
 #### Key Features:
 
-1. **Web Interface**: Accessible from any device on the same network as the Raspberry Pi.
-2. **Live Camera Preview**: This shows a real-time feed from the camera.
-3. **Labeling System**: Allows users to input labels for different categories of images.
-4. **Organized Storage**: Automatically saves images in label-specific subdirectories.
-5. **Per-Label Counters**: Keeps track of how many images are captured for each label.
-6. **Summary Statistics**: Provides a summary of captured images when stopping the capture process.
+1. **Web Interface** : Accessible from any device on the same network as the Raspberry Pi.
+2. **Live Camera Preview** : This shows a real-time feed from the camera.
+3. **Labeling System** : Allows users to input labels for different categories of images.
+4. **Organized Storage** : Automatically saves images in label-specific subdirectories.
+5. **Per-Label Counters** : Keeps track of how many images are captured for each label.
+6. **Summary Statistics** : Provides a summary of captured images when stopping the capture process.
 
 #### Main Components:
 
-1. **Flask Web Application**: Handles routing and serves the web interface.
-2. **Picamera2 Integration**: Controls the Raspberry Pi camera.
-3. **Threaded Frame Capture**: Ensures smooth live preview.
-4. **File Management**: Organizes captured images into labeled directories.
+1. **Flask Web Application** : Handles routing and serves the web interface.
+2. **Picamera2 Integration** : Controls the Raspberry Pi camera.
+3. **Threaded Frame Capture** : Ensures smooth live preview.
+4. **File Management** : Organizes captured images into labeled directories.
 
 #### Key Functions:
 
@@ -1435,10 +1435,10 @@ The code creates a web application for real-time image classification using a Ra
 
 #### Key Components:
 
-1. **Flask Web Application**: Serves the user interface and handles requests.
-2. **PiCamera2**: Captures images from the Raspberry Pi camera module.
-3. **TensorFlow Lite**: Runs the image classification model.
-4. **Threading**: Manages concurrent operations for smooth performance.
+1. **Flask Web Application** : Serves the user interface and handles requests.
+2. **PiCamera2** : Captures images from the Raspberry Pi camera module.
+3. **TensorFlow Lite** : Runs the image classification model.
+4. **Threading** : Manages concurrent operations for smooth performance.
 
 #### Main Features:
 
@@ -1491,10 +1491,10 @@ The code creates a web application for real-time image classification using a Ra
 
 #### Key Concepts:
 
-1. **Concurrent Operations**: Using threads to handle camera capture and classification separately from the web server.
-2. **Real-time Updates**: Frequent updates to the classification results without page reloads.
-3. **Model Reuse**: Loading the TFLite model once and reusing it for efficiency.
-4. **Flexible Configuration**: Allowing users to adjust the confidence threshold on the fly.
+1. **Concurrent Operations** : Using threads to handle camera capture and classification separately from the web server.
+2. **Real-time Updates** : Frequent updates to the classification results without page reloads.
+3. **Model Reuse** : Loading the TFLite model once and reusing it for efficiency.
+4. **Flexible Configuration** : Allowing users to adjust the confidence threshold on the fly.
 
 #### Usage: