Presentation: Self-Racing using Deep Neural Networks: Lap 2

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 11:00am - 11:50am

Day of week: Tuesday

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Abstract

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 navigate all turns on the racetrack. We will discuss the events leading up to the race, development methods used, and future plans including the use of ROS and semantic segmentation.

Speaker: Jendrik Joerdening

Data Scientist @Aurubis

Jendrik is a Data Scientist at Aurubis.He is working on Data Science and Deep Learning in the field of Industry 4.0.After studying Physics and Space Science and Technology, where he determined the communication strategies for the Mars 2020 rover, he worked at Akka Germany as a Data Scientist and now moved to Aurubis staying in the same field. At the same time he takes part in the Udacity Self-Driving Car Nanodegree. In this context, he participated with a group of other Udacity student in the Self-Racing Cars event at the Thunderhill race-track in California. There the group of students taught a car to drive around every turn of the race track autonomously.

Find Jendrik Joerdening at

Speaker: Anthony Navarro

Product Lead- Robotics and Self-Driving Car @Udacity

Anthony currently works as a Product Lead for Udacity leading their Self-Driving Car and Robotics Nanodegree programs in Mountain View, California. After combat service in the US Army, he obtained his Master’s in Computer Engineering from Colorado State University where he focused his studies on the area of robotics. Prior to his employment at Udacity, Anthony worked as a systems and software engineer for Lockheed Martin for 7 years where he supported multiple satellite launches and operations and also conducted applied research and development on autonomous systems. While at Lockheed Martin, he was selected to participate in the Advanced Technical Leadership Program offered by the company. Prior to joining Udacity, he also led the Udacity/ PolySync self-driving car race and was a student in the first cohort of the Udacity Self-Driving Car Nanodegree program.

Find Anthony Navarro at

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    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

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    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

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  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.