AWS DeepRacer Scholarship Challenge from Udacity is now open for enrollment
The race is on! Start your engines! The AWS DeepRacer Scholarship Challenge from Udacity is now open for enrollment.
As mentioned in our previous post, the AWS DeepRacer Scholarship Challenge program introduces you—no matter what your developer skill levels are—to essential machine learning (ML) concepts in a fun and engaging way. Each month, you put your skills to the test in the world’s first global autonomous racing league, the AWS DeepRacer League, and compete for top spots in each month’s unique race course.
Students that record the top lap times in August, September, and October 2019 qualify for one of 200 full scholarships to the Machine Learning Engineer nanodegree program, sponsored by Udacity.
What is AWS DeepRacer?
In November 2018, Jeff Barr announced the launch of AWS DeepRacer on the AWS News Blog as a new way to learn ML. With AWS DeepRacer, you have an opportunity to get hands-on with a fully autonomous 1/18th-scale race car driven by reinforcement learning (RL), a 3D-racing simulator, and a global racing league.
How does the AWS DeepRacer Scholarship Challenge work?
The program begins today, August 1, 2019 and runs through October 31, 2019. You can join the scholarship community at any point during these three months for free.
After enrollment, you go through the AWS DeepRacer: Driven by Reinforcement Learning course developed by AWS Training and Certification. The course consists of short, step-by-step modules (90 minutes in total). The modules prepare you to create, train, and fine-tune an RL model in the AWS DeepRacer 3D racing simulator.
After you complete the course, you can enter the AWS DeepRacer virtual league. The enrolled students who record the top lap times in August, September, and October 2019 qualify for one of 200 full scholarships to the Udacity Machine Learning Engineer nanodegree program.
Throughout the program and during each race, you have access to a supportive community to get pro tips from experts and exchange ideas with your classmates.
“Developers have a great opportunity here to follow a focused learning curriculum designed to get started in Reinforcement Learning”- “Sunil Mallya, principal deep learning scientist, ML Solution Labs AWS”
Expert tips and tricks
Now that you have enrolled and are racing, you may benefit from expert racing tricks to race to the top. In the pit stop, you learn great racing tips and access valuable tools like the log analysis tool. Also, there’s a hack you can use, developed by an AWS DeepRacer participant ARCC, for running the training jobs locally in a Docker container.
“You can clone your previous model to train a better model. I know this sounds complicated but, if you clone a previously trained model as the starting point of a new round of training, you could improve the training efficiency. To do this, you can modify the hyper-parameters to make use of already learned knowledge. “- Law Mei Ching Pearly Jean, youngest AWS DeepRacer League competitor
The tips and tools help you submit a performant model for the challenge—eventually increasing your chance of topping the leaderboard and winning one of the 200 ML nanodegree scholarships from Udacity.
“The AWS DeepRacer League has become quite addictive as the competition is pretty intense. What’s great though is that even though everyone is trying to win, that hasn’t kept people from sharing what they have learned. There is a great community around this product and it’s cool to see the impact it’s having with helping people get introduced to the field of Machine Learning.” – Alex Schultz, machine learning software engineer
You can add more code to the AWS DeepRacer workshop repository on GitHub, and create more tools and tips for the community to make model development using RL easy and useful. To learn more about ML on AWS, see Get Started with Machine Learning – No PhD Required.
About the Author
Tara Shankar Jana is a Senior Product Marketing Manager for AWS Machine Learning. Currently he is working on building unique and scalable educational offerings for the aspiring ML developer communities- to help them expand their skills on ML. Outside of work he loves reading books, travelling and spending time with his family.