Getting Started with AI on Jetson Nano (GSJN)

 

Course Overview

The power of AI is now in the hands of makers, self-taught developers, and embedded technology enthusiasts everywhere with the NVIDIA Jetson developer kits. 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 to build a deep learning classification project with computer vision models.

Prerequisites

Basic familiarity with Python (helpful, not required).

Additional Computer Requirements:

  • A computer with an internet connection and the ability to flash your microSD card
  • An available USB-A port on your computer (you may need an adapter or different cable if you only have USB-C ports)

Course Objectives

You'll learn how to:

  • Set up your NVIDIA 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 NVIDIA 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.

Outline: Getting Started with AI on Jetson Nano (GSJN)

1. Setting up your Jetson Nano

Step-by-step guide to set up your hardware and software for the course projects

  • Introduction and Setup: Video walk-through and instructions for setting up JetPack and what items you need to get started
  • Cameras: Details on how to connect your camera to the Jetson Nano Developer Kit
  • Headless Device Mode: Video walk-through and instructions for running the Docker container for the course using headless device mode (remotely from your computer).
  • Hello Camera: How to test your camera with an interactive Jupyter notebook on the Jetson Nano Developer Kit
  • JupyterLab: A brief introduction to the JupyterLab interface and notebooks

2. Image Classification

Background information and instructions to create projects that classify images using Deep Learning

  • AI and Deep Learning: A brief overview of Deep Learning and how it relates to Artificial Intelligence (AI)
  • Convolutional Neural Networks (CNNs): An introduction to the dominant class of artificial neural networks for computer vision tasks
  • ResNet-18: Specifics on the ResNet-18 network architecture used in the class projects
  • Thumbs Project: Video walk-through and instructions to work with the interactive image classification notebook to create your first project
  • Emotions Project: Build a new project with the same classification notebook to detect emotions from facial expressions
  • Quiz Questions: Answer questions about what you've learned to reinforce your knowledge

3. Image Regression

Instructions to create projects that can localize and track image features in a live camera image

  • Classification vs. Regression: With a few changes, the Classification model can be converted to a Regression model
  • Face XY Project: Video walk-through and instructions to build a project that finds the coordinates of facial features
  • Quiz Questions: Answer questions about what you've learned to reinforce your knowledge

Prices & Delivery methods

Online Training

Duration
1 day

Price
  • US$ 500
Classroom Training

Duration
1 day

Price
  • United States: US$ 500

Schedule

Currently there are no training dates scheduled for this course.