Udacity

AWS DeepRacer (Udacity)

Offered by Udacity, AWS,
AWS DeepRacer (Udacity)

Learn the fundamentals of machine learning and reinforcement learning in a fun and engaging way through autonomous driving with AWS DeepRacer. This course will prepare you to create, train, and fine-tune reinforcement learning models in the AWS DeepRacer 3D racing simulator. You will be able to utilize the car's tech specs, assembly, and calibration to train and deploy your racing model using AWS in both simulated and real-world tracks.

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AWS DeepRacer is a 1/18th scale race car which gives you an interesting and fun way to get started with reinforcement learning (RL). With AWS DeepRacer, you now have a way to get hands-on with RL, experiment, and learn through autonomous driving. You can get started with the virtual car and tracks in the cloud-based 3D racing simulator. For a real-world experience, you can deploy your trained models onto AWS DeepRacer and race in the global AWS DeepRacer league, the world’s first global autonomous racing league for developers.

What You Will Learn

Lesson 1
Intro to AWS DeepRacer

  • Get an overview of what you’ll be learning and doing in the course.
  • Learn about the items that come in the AWS DeepRacer box.
  • Assemble and calibrate your vehicle.

Lesson 2
Reinforcement learning in DeepRacer

  • DeepRacer definition.
  • Types of machine learning.

Lesson 3
Unboxing AWS DeepRacer

  • Optional lesson for those who do not own the AWS DeepRacer hardware.
  • Learn about the platform by following what's included in the box.

Lesson 4
Under the Hood recap

  • Learn what else is included in the vehicle beside the vehicle chassis.

Lesson 5
DeepRacer Assembly

  • Install the battery.
  • Boot the operating system and connect to wi-fi.
  • Calibrate.

Lesson 6
Steering calibration

  • Follow these steps to calibrate your vehicle.

Lesson 7
Throttle Calibration

  • Follow these steps to calibrate your throttle.

Lesson 8
Track Preview

  • Preview one possible AWS DeepRacer track.
  • Build your own track.
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