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Integrating Hardware into the NVIDIA DLI Course

Today we'll go over the hardware, software and step-by-step build of a project using the NVIDIA Jetson Nano and some SparkFun parts!

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Yesterday, we talked about the new Jetson Resource Page and all of the features and information you can find there. Today we're introducing a new step you can take with the SparkFun DLI Kit for Jetson Nano to get started with AI on the Jetson Nano.

Machine Learning is not an especially new topic - a number of hardware and software platforms have been around for a year or two, and more are emerging on what seems like a daily basis. The NVIDIA Jetson Nano has been around for a while, and we have seen a number of different projects, including some using our kits.

With that in mind, I wanted to capitalize on my “shelter in place” time and learn a little more about the NVIDIA Jetson Nano. I started my exploration by taking the Deep Learning Institute (DLI) course, which is free and can be found here, combined with our kit specifically for the course.

SparkFun DLI Kit for Jetson Nano

SparkFun DLI Kit for Jetson Nano

KIT-16308
$174.95
2

I learned a lot, but it left me wanting more. The way I learn, like many of you, is through building and tinkering with a “Hello World” and adding simple hardware components until we have...something. I call this the “Hello Moon” project, which is that first step beyond the “Hello World."

In the DLI Course, this came to the forefront as many of the examples seemed, on the surface, to be incredibly complicated, with long chunks of code in order to approach Machine Learning the same way. On top of that, I am still working on my fluency in Python, so I had a lot ahead of me to integrate some SparkFun hardware into one of these examples!

After some tinkering, working through errors and experimenting, I finally got the “Thumbs up, Thumbs Down” example working and integrated with some SparkFun hardware. I created a video giving an overview of the project, what hardware I used and where I tinkered with the existing code to get my project up and running.

I still have a lot to learn, but I feel more confident about digging into Machine Learning and integrating it into more and more of my projects in the future. I hope this helps you learn something new, gives you a bit more confidence to take the course and offers a possible starting point for a Machine Learning project you have in mind.


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