Getting Started with AI on Jetson Nano

Getting Started with AI on Jetson Nano

Learning Objectives

The power of AI is now in the hands of makers, self-taught developers, and embedded technology enthusiasts everywhere with the NVIDIA Jetson Nano Developer Kit. This easy-to-use, powerful computer lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. In this course, you'll use Jupyter iPython notebooks on your own Jetson Nano to build a deep learning classification project with computer vision models.

You'll learn how to:

  • Set up your Jetson Nano and camera
  • Collect image data for classification models
  • Annotate image data for regression models
  • Train a neural network on your data to create your own models
  • Run inference on the Jetson Nano with the models you create

Upon completion, you'll be able to create your own deep learning classification and regression models with the Jetson Nano.

Course Details

Prerequisites: Basic familiarity with Python (helpful, not required)

Tools, libraries, frameworks used: PyTorch, Jetson Nano

Certificate: Available

Assessment Type: Multiple-choice

Required Hardware

  • NVIDIA Jetson Nano Developer Kit
  • High-performance microSD card: 32GB minimum (we've tested and recommend¬†this one)
  • 5V 4A power supply with 2.1mm DC barrel connector (we've tested and recommend¬†this one)
  • 2-pin jumper: must be added to the Jetson Nano Developer Kit board to enable power from the barrel jack power supply (here‚Äôs an¬†example)
  • Logitech C270 USB Webcam (we've tested and recommend¬†this one).
  • USB cable: Micro-B To Type-A with DATA enabled (we've tested and recommend¬†this one)

The complete package is also available from Sparkfun either with the Jetson Nano included or without the Jetson Nano included.

Additional Computer Requirements

  • A computer with an internet connection and the ability to flash your microSD card
  • An available USBA port on your computer (you may need an adapter if you only have USBC ports)

For additional hands-on training through the NVIDIA Deep Learning Institute, visit