There's so much opportunity for students to learn AI -- and it doesn't have to be hard. Learning AI sounds daunting and until recently, it's been challenging for students to find enough guidance and support to build AI projects. There are great resources to help students learn coding languages, but how do students go from learning the basics of Python and Linux to building an autonomous robot (especially on a budget)? Guidelines to build with AI should be accessible to all. After all -- creativity and innovation happen when barriers are taken down, not added.

Over the last year or two there has been a flood of custom silicon intended to speed up machine learning on the edge. First to arrive was Intel with their Moividius-based hardware, and more recently we've seen the appearance of Google's Edge TPU-based hardware. However traditionally NVIDIA's offering in this space -- built as it was around their GPU-based hardware -- has been higher powered, and comparatively expensive. However, with everyone moving towards the edge it is perhaps unsurprising to see them introduce something a bit more affordable. So last month we saw the arrival of the Jetson Nano Module and Developer Kit. Still based around their existing GPU technology, the new Jetson Nano is therefore "upwards compatible" with the much more expensive Jetson TX and AGV Xavier boards. The Jetson Nano Developer Kit arrives in yet another unassuming box. Inside the box is the carrier board itself with the Jetson Nano module and a heatsink already fitted.

Jump-start your AI exploration with our free, self-paced Deep Learning Institute course, "Getting Started with AI on Jetson Nano". You'll learn how to collect image data and use it to train, optimize, and deploy AI models for custom tasks like recognizing hand gestures and image regression for locating a key point in an image. You can even use Jupyter notebooks to build a deep learning classification project with computer vision models. And you'll earn a certificate upon completion.