Tag Archives: Python

AI On the Edge, Tutorial

AI on the Edge LESSON 12: Introduction to Python Threading on the Raspberry Pi

Hey everyone, and welcome back to the AI on the Edge series!

In today’s lesson, we’re tackling one of the most important programming concepts you’ll need as we build more advanced AI and robotics projects — Python Threading.

Up until now, our programs have been pretty linear — they do one thing at a time. But as our projects get smarter and more interactive, we often need several things happening at the same time. That’s exactly where threading comes in. In this lesson, I give you a gentle, practical introduction to threading by creating a program that blinks an LED while simultaneously listening for your commands to change the blink speed — all without one task blocking the other.

You’ll see how to create a separate thread that handles user input while the main program continues blinking the LED smoothly. We also use a Queue to safely pass data between the threads. This is a foundational skill that becomes incredibly valuable later in the class when we need to run voice recognition, camera processing, sensor reading, and motor control all at the same time.

I designed this lesson to be very beginner-friendly. If you’ve never used threading before, don’t worry — I walk you through every line of code and explain why we do things the way we do. By the end of this video, you’ll have a solid understanding of how to launch background threads, manage shared variables safely, and keep your main program responsive.

This lesson is a big stepping stone in our AI on the Edge journey. The ability to run multiple tasks concurrently is what separates simple scripts from real-world intelligent systems that can listen, think, and act at the same time.

So grab your SunFounder Fusion AI Hat, hook up an LED, and get ready to take your Raspberry Pi programming skills to the next level. Once you understand threading, a whole new world of possibilities opens up!

As always, I strongly encourage you to code along with me in the video and then experiment on your own. Try adding more LEDs, change the commands, or combine it with things we’ve learned in previous lessons. That hands-on practice is where the real learning happens.

I’m really excited for you to learn this one — it’s going to make the rest of the class a lot more fun and powerful!

In today’s lesson, this is the code we developed.

 

AI On the Edge, Tutorial

AI on the Edge LESSON 7: Homework Solution for Dimmable LED

In Lesson 6, I gave you a homework challenge: build a dimmable LED using a potentiometer. In today’s Lesson 7, we go through the solution together step-by-step.

This lesson is all about taking analog input from a potentiometer and converting it into smooth PWM output to control the brightness of an LED. It’s a very practical project because it teaches you how to read real-world analog values and turn them into useful control signals — skills we’ll use again and again as we build smarter AI-powered projects.

In the video, I walk you through the complete working code. You’ll see how we read the potentiometer value (0 to 4095), convert that raw number into a proper brightness percentage using a bit of math (with a nice logarithmic curve so the brightness feels natural to the human eye), and then send that value to the LED using PWM. The result is a very smooth, responsive dimmer that feels professional.

Even though this seems like a simple project, it’s actually an important stepping stone. Understanding how to read sensors and smoothly control outputs is fundamental to building real AI on the Edge systems — whether you’re controlling motors, adjusting screen brightness, or varying the speed of a robot based on sensor input.

By the end of this lesson, you should have a solid understanding of how to combine the ADC (Analog to Digital Converter) with PWM output, and more importantly, how to think about mapping real-world inputs to useful outputs.

So if you did the homework, great job! If you got stuck, don’t worry — we go through the full solution together. And as always, I strongly encourage you to take the code and make it your own. Try changing the response curve, add multiple LEDs with different colors, or combine it with things we’ve learned in earlier lessons.

This is the kind of foundational hardware skill that will serve you well as we continue moving deeper into the AI on the Edge class. You’re doing great — keep going!

We are still using the schematic from our earlier project.

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

In this lesson, this is the code which we came up with:

 

AI On the Edge

AI on the Edge LESSON 4: Python Averaging Grades Homework Solution

Hey everyone, and welcome back to the AI on the Edge series!

In Lesson 3, I gave you your first real programming homework — to create a program that lets the user enter multiple grades and then calculates the average. In today’s Lesson 4, we go through the solution together step-by-step.

This lesson is all about learning how to work with lists (also called arrays), using for loops effectively, and building clean, organized code. Even though averaging grades might seem simple, these are fundamental programming skills that we will use constantly as we move forward in this class. Whether we’re averaging sensor readings, smoothing camera data, calculating confidence scores from AI models, or processing batches of information — the ability to collect data, store it, and process it is extremely important.

In the video, I walk you through a clean solution that uses a list to store all the grades, then loops through that list to calculate the total before dividing by the number of grades. You’ll also see how to display the original grades back to the user and present the final average in a nice, readable way.

I really enjoy these early lessons because this is where you start developing good programming habits. The techniques you learn here — using lists, loops, and organizing your code — will become the building blocks for much more powerful AI projects later in the series.

By the end of this lesson, you should feel much more comfortable working with lists and loops in Python. These skills are going to be used over and over again as we start reading sensors, processing camera frames, and handling data from AI models.

So if you tried the homework, awesome! If you got stuck, that’s perfectly okay — that’s exactly why we go through the solution together. Take the code, run it, and then I strongly encourage you to modify it. Try adding letter grades (A, B, C), calculate the highest and lowest grade, or make it keep running until the user wants to quit. The more you play with it, the faster you’ll learn.

You’re doing great! These early Python lessons are the foundation we need before we start combining code with real hardware and AI in the coming lessons. Keep going — we’re building something really cool here!

This is my homework solution.

 

AI On the Edge

AI on the Edge LESSON 3: Learn Python Essentials In One Session

Hey everyone, and welcome back to the AI on the Edge series!

In today’s lesson, we’re doing something really important. Since this entire class is going to be taught using Python, I wanted to make sure everyone — even complete beginners — has a solid foundation before we start attaching hardware and diving into real AI projects.

In this marathon session, I take you through all the Python essentials you’ll need for the rest of the class. We cover variables, data types, if statements, for loops, while loops, lists, getting user input, and how to organize your code in a clean, readable way. I even share my personal philosophy on keeping code simple (you’ll notice I avoid nested else statements like the plague because I like my logic clean and easy to follow).

This lesson is designed to be a complete “Python boot camp” in one video. Even if you’ve never written a line of code before, by the end of this lesson you’ll have the core skills needed to keep up with everything we’re going to build in this series. And if you’re already comfortable with Python, this is still a great refresher with my specific style and approach that we’ll be using throughout the class.

I really believe that strong programming fundamentals are the key to success in AI on the Edge. Once you’re comfortable with these basics, we can focus on the fun stuff — controlling hardware, reading sensors, processing camera images, using voice commands, and building intelligent systems.

So whether you’re brand new to programming or just need a solid review, grab a cup of coffee, settle in, and let’s spend some quality time getting you up to speed with Python. I encourage you to code along with me in the video and actually type every example. That hands-on practice is what makes it stick.

By the end of this lesson, you’ll be ready for the homework assignment, which will test everything we covered today. Once you’ve got that down, we’ll be ready to start connecting real hardware in the very next lessons.

You’ve got this! Let’s turn you into a confident Python programmer so we can go build some amazing AI projects together.

AI On the Edge

AI on the Edge LESSON 2: Raspberry Pi Operating System for Artificial Intelligence

The major challenge we face in this AI on the Edge class is getting a Raspberry Pi 5 configures where you have all the AI Models, Libraries, Modules and Methods installed, and where they all play nicely together. Often, when you add a new model, the old model becomes broken. This is because when you install something new, it often times updates the dependencies. That means it updates a library already on your system. For example, lets say you have numpy 14, working with YOLO 11. Now you install mediapipe, and it updates numpy 14 to numpy 15. This then ‘Breaks’ your YOLO, as it wanted a different version of numpy.  Likely you will get frustrated and quit before you get the dependency problems solved. In order to get around this, you can use a special education version of the Bookworm OS, which has all the needed libraries installed already and working nicely with each other. The video above shows you how to install this OS. Once you do, no not update it, do not upgrade it, do not touch it. Use it to develop your programs and projects for this class. If you want to do something else with your pi, have a separate SD card.