AWS DeepRacer Scholarship Challenge

The AWS DeepRacer Scholarship Challenge is open to all students, 18 years of age or older, interested in machine learning. We recommend students have a basic knowledge of Python.

Description

The program begins

August 1 and will run through October 31, 2019. You can join the scholarship community at any point during these 3 months and immediately enroll in Udacity’s specialized AWS DeepRacer course.

Once you enroll, you’ll work through the brief AWS DeepRacer course consisting of several short modules that will prepare you to create, train, and fine-tune a reinforcement learning model in the AWS DeepRacer 3D racing simulator. Throughout the program–including while you progress through the course and while you work on your racing submissions–you’ll have access to a custom scholarship student community where you can get pro tips from experts and exchange ideas with your classmates.

Each month you’ll be able to pit your skills against others in virtual races in the AWS DeepRacer console. Students will compete for top spots in each month’s unique race course. Students that record the top lap times in August, September, and October 2019 will qualify for one of 200 full scholarships to the Machine Learning Engineer Nanodegree program.

SCHOLARSHIP CHALLENGE

AWS DEEPRACER SCHOLARSHIP CHALLENGE

Learn the fundamentals of machine learning and reinforcement learning in a fun and engaging way through autonomous driving with AWS DeepRacer.

Dedicate just a few hours going through the initial course and as many as you’d like on your AWS DeepRacer model submissions.

Participate in a vibrant student community where you’ll support and collaborate with classmates and receive tips and guidance from experts.

Use the skills you learn to compete with your peers in the AWS DeepRacer League Virtual Circuit.

Vacncies: 200 SEATS

Top performers in the AWS DeepRacer Scholarship Challenge will earn one of 200 full scholarships to:

MACHINE LEARNING ENGINEER

NANODEGREE PROGRAM

Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment.

Official Website