Secondary School AI Curriculum

Jetbot / JetRacer deep learning course

Jetbot is an intelligent robot car designed by Nvidia, a world-leading smart hardware company. Jetbot is powered by the built-in AI chip Nvidia Jetson Nano, which supports artificial intelligence deep learning models such as face recognition, object detection, and tracking.

This course will explain artificial intelligence hardware and software, including how AI chips control different parts of robotic vehicles, and develop deep learning computer vision applications, such as autonomous driving.

Course topics include:

What will you learn?

Course content

Students will learn the process of building deep learning models using Python programming language and its toolbox. They will gain knowledge in specific AI topics, including types of computer vision models, transfer learning, TensorFlow operations, etc. Students will also be exposed to hardware knowledge, such as the functions of various hardware connectors.

Jetbot hardware basics

The Jetbot is equipped with the Jetson Nano, which is a GPU mini-computer designed by Nvidia, a leading AI hardware company. It is capable of efficiently processing deep learning models. The first part of this course will cover the mechanical knowledge required to assemble the Jetbot.

Power Supply Equipment
Communication and Power Cables
Uses and Precautions for Various Types of Screws
Motor Connection Guidelines
Usage of Graphics Processing Unit

Jetbot Software Fundamentals

Jetson Nano is indeed a computer in essence. However, the operating system used on Jetson Nano is Linux. Therefore, to develop deep learning models on Jetbot, you would need to have knowledge of the following software:

Common Linux Commands
Jupyter Lab
Wireless Operation of Jetbot
Robot Operating System

Fundamentals of Deep Learning

The cutting-edge fields of Artificial Intelligence- Deep Learning,what exactly is it? Why is deep learning referred to as artificial neural networks? In this part, we will explain the fundamental concepts necessary for developing deep learning models, including:

Artificial neurons
Training deep learning models
Transfer learning
Classification of computer vision models

Python Deep Learning

What deep learning frameworks are available in Python? How to develop a deep learning model from scratch? In this part, we will explain how to develop a deep learning model using Python and deploy it on Jetbot.

Deep learning tool: TensorFlow
Developing a face recognition model using TensorFlow
Developing an object tracking model using TensorFlow

Fun AI fact

What is Jetbot/JetRacer? What does it have to do with AI?

JetBot is an open-source robot platform based on the NVIDIA Jetson Nano development board. Jetson Nano is an edge computing device specifically designed for AI and machine learning applications. JetBot features a wide range of hardware expansion interfaces and supports Python as well as deep learning frameworks such as TensorFlow and PyTorch. JetBot can be used for developing various AI applications, including object recognition, autonomous navigation, and face recognition.

JetRacer is a high-speed AI racing platform based on the NVIDIA Jetson Nano development board. It is specifically designed for autonomous driving and remote-controlled racing applications, featuring high performance and low latency. JetRacer supports Python and deep learning frameworks, enabling the development of intelligent racing functionalities such as autonomous navigation and track following.

The relationship between JetBot and JetRacer with AI lies in the fact that both platforms are built on the foundation of the NVIDIA Jetson Nano, a development board designed specifically for AI applications. Both JetBot and JetRacer support deep learning and machine learning algorithms, enabling intelligent functionalities for robots and racing cars, such as object recognition and autonomous navigation. With JetBot and JetRacer, developers and students can more easily develop and deploy AI applications, applying AI technology to real-world scenarios.

Why choose Vinci AI?

University lecturer teaching team

VInci AI's teaching team is rich in experience, including university teachers who teach master's AI courses in various colleges and universities.

Curriculum developed by PhD-level experts

Vinci AI's　PhD-level AI expert team, providing the most professional artificial intelligence courses

Recognized by research institutions

The teaching platform developed by Vinci AI has received support from Cyberport. Vinci AI is also a STEM education partner of the Productivity Council.

Primary and Secondary School STEM Curriculum Catalog

STEM on-campus courses

STEM regular courses

STEM on-campus teacher training

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Vinci AI offers on-campus courses, including STEM Day events, competition training, and after-school programs. We welcome you to contact our expert consultants to arrange suitable topics and formats for your needs.