If you are into machine learning and racing cars, this event is for you! Join us, October 22nd, when Trifork is in the house to give you an intro to the extremely cool Donkey Cars – a small-scale remote-controlled car mounted with a RaspberryPi, a sensor and a camera.
During the hands-on workshop part of the program, you will be guided through how to set up the Donkey Car, how to install pre-recorded data, how to train and tweak machine learning models and deploy them on the car.
When you have attended this event, you will be fully ready to enter the Donkey Car Race Event later this year (the date has yet to be announced).
Please bring your own laptop!
There will be food and drink to fuel your brain.
About Donkey Cars
Donkey is an open-source DIY platform for small scale cars with a high-level self-driving library written in Python. A Donkey Car consists of standalone, independently configurable components that can be combined to make a car. Read more about Donkey Cars.
16.15: Doors open
16.30: Introduction to Donkey Cars by Simon Kæseler, Iulia Popa and David Zachariae, Trifork
17.15: Hands-on workshop: Setup, Import, Deploy!
18.30: Food and drinks, courtesy of Trifork
19.00: Racing time!
19:30: Sum-up and thank you for today
About the speakers
Iulia Adriana Popa
Iulia is a software pilot at Trifork. She is passionate about coding and learning new technologies. When she is not coding, she can be found stuck with her nose in a book, playing WoW or on a yoga mat
Simon is an ICT engineer with a degree from Aarhus University, as well as a former ORBIT Lab mission specialist, who now works as a Software Pilot at Trifork. As a part of the ML team at Trifork, he specializes in applied natural-language understanding within the service industry.
David finished his Master’s degree in Computer Science from Aarhus University last year, where he specialized in ML, NLP and functional programming. He currently works as a Software Pilot and backend developer at Trifork. In his free time, he enjoys running, League of Legends and working on spare time coding projects.
Who can participate
This event is free and open for students, tech startups and other people with an interest in the topic. You don’t have to be an expert in machine learning, as we will deal with entry-level machine learning.
The participant limit is 20. There is room for four teams of five participants. You are welcome to form a team in advance – however, please note that all team members have to register separately. Individuals are also welcome to register, and we will place you in teams. The first 20 people to sign up will get a place.
This event will be in English.
At all times, we follow the current guidelines to prevent the spread of the coronavirus. However, we need your help as well to make sure we all stay safe:
– Please wash your hands frequently or use hand sanitizer (will be available at the event)
– Please sneeze/cough into your sleeve – not your hand
– Please refrain from physical contact with the other participants and keep a safe distance – no handshakes, hugs (or kisses ;))
– Please do not show up, if you have any symptoms of illness
If there are any changes to the circumstances of this event, you will be notified via email by registering as described below.
By signing up for this event, you agree to receive email updates from ORBIT Lab, e.g. changes in connection with this event. You can manage your preferences and unsubscribe at any time, however, please remain “subscribed” until after the event, as we will communicate potential changes to subscribers only. Please note, the signup form may not work in Firefox for Windows. Please use another browser.
If you have already formed a team of five people, please indicate the names of your other group members in the designated field. Please note, that all group members have to register individually.
Technical questions may be addressed to Trifork Software Engineer, Simon Kæseler on firstname.lastname@example.org.
General questions may be addressed to Litte Dalsgaard, ORBIT Lab Community Manager: email@example.com / 93 52 14 47.