The Duckietown Foundation is offering a free course using the latest addition to NVIDIA’s Jetson AI at the Edge platform.
Duckietown first started as an MIT class in 2016 before evolving into an open-source platform for robotics and AI education, research, and outreach.
Jetson is a series of embedded computing boards from Nvidia which brings accelerated AI performance to the edge in a power-efficient and compact form factor.
Earlier this week, NVIDIA announced the Jetson Nano 2GB.
The Jetson Nano 2GB features a 128-core NVIDIA Maxwell GPU, quad-core Arm A57 1.43GHz CPU, 2GB 64-bit LPDDR4 25.6GB/s memory, and all the usual connectivity options you’d expect including HDMI, ethernet, USB 3.0, MicroSD, 802.11ac wireless, and more.
Deepu Talla, Vice President of Edge Computing at NVIDIA, said:
“While today’s students and engineers are programming computers, in the near future they’ll be interacting with, and imparting AI to, robots.
The new Jetson Nano is the ultimate starter AI computer that allows hands-on learning and experimentation at an incredibly affordable price.”
Despite the accessible $59 price, the Nano 2GB allows complex DNN models and ML frameworks to be deployed at the edge with high-performance inferencing.
Here are some benchmarks of popular DNN models running on the Nano compared to other devices in the Jetson family:
However, it’s just a fancy paperweight unless you know how to use it.
NVIDIA itself has released free online training and AI-certification programs which teach students how to get started with their Jetson Nano. This is in addition to the content supplied by the huge Jetson community.
Matthew Tarascio, Vice President of AI at Lockheed Martin, commented:
“Acquiring new technical skills with a hands-on approach to AI learning becomes critical as AIoT drives the demand for interconnected devices and increasingly complex industrial applications.
We’ve used the NVIDIA Jetson platform as part of our ongoing efforts to train and prepare our global workforce for the AI revolution.”
Duckietown’s course also uses Jetson for a hands-on approach. AI and robotics components are combined to address challenges for self-driving vehicles.
“The Duckietown educational platform provides a hands-on, scaled-down, accessible version of real-world autonomous systems,” said Emilio Frazzoli, Professor of Dynamic Systems and Control at ETH Zurich.
The fun of Duckietown’s course is seeing the algorithms you create deployed in actual Duckiebots. Advanced students can go on to take part in the AI Driving Olympics competition which features increasingly complex tasks from lane-following to fleet management.
Enrolment in NVIDIA’s course requires at least basic knowledge of Python and Linux. Duckietown’s course prerequisites are the same but it also lists elements of linear algebra, probability, calculus, kinematics, and dynamics as being required.
You can sign up for NVIDIA’s certification program here.
Or check out Duckietown’s course here.
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