Track:

AI Meets the Physical World

Day of week: Tuesday

Artificial intelligence and machine learning algorithms became an essential part of many online services. Lately many companies started bringing AI to the physical world. Mobile phone apps use more and more on-board machine learning algorithms, cars are becoming self-driving, and drones process their images on the fly!

In this track we explore use-cases of machine learning for on-board processing. We discuss best-practices for training and inference and explore algorithms that work well on embedded devices. Learn how your AI can meet the physical world!

Track Host:
Roland Merteens
Machine Learning and Computer Vision Research Engineer

Roland is developing smart computer-vision algorithms for self-driving vehicles at Audi's AID. Previously he worked on deep learning approaches for natural language processing (NLP) problems, social robotics, and computer vision for drones.


by Anthony Navarro
Product Lead- Robotics and Self-Driving Car @Udacity

by Jendrik Joerdening
Data Scientist @Akka Germany

This talk details a team of 17 Udacity Self-Driving Car students as they attempted to apply deep learning algorithms to win an autonomous vehicle race.
At the 2017 Self Racing Cars event held at Thunderhill Raceway in California, the team received a car and had two days before the start of the event to work on the car.
In this time, we developed a neural network using Keras and Tensorflow which steered the car based on the input from just one front-facing camera in order to...


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