Getting Started with AI on Jetson Nano Training
Course Authors
Getting Started with AI on Jetson Nano Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
Getting Started with AI on Jetson Nano Training: 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.
WHAT'S INCLUDED?
- 1 day of Getting Started with AI on Jetson Nano Training with an expert instructor
- Getting Started with AI on Jetson Nano Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Getting Started with AI on Jetson Nano – https://www.wiley.com/
- Getting Started with AI on Jetson Nano – https://www.packtpub.com/
- Getting Started with AI on Jetson Nano – https://store.logicaloperations.com/
- Getting Started with AI on Jetson Nano – https://us.artechhouse.com/
- Getting Started with AI on Jetson Nano Training – https://www.amazon.com/
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ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Getting Started with AI on Jetson Nano Training course, participants can:
- 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
CUSTOMIZE IT
- We can adapt this Getting Started with AI on Jetson Nano Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Getting Started with AI on Jetson Nano course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Getting Started with AI on Jetson Nano course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Getting Started with AI on Jetson Nano course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Getting Started with AI on Jetson Nano Training course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Getting Started with AI on Jetson Nano Training course are:
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 SYLLABUS
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
