UW DeepLens Hackathon, Nov 1
Deadline: Registration due Friday, November 1, 2019.
In this hackathon, you will get a chance to bring your ideas to reality, and create a future which helps with augmenting human intelligence. In the process you will also learn the end-to-end process of building machine learning (ML) models. You will then learn how to apply pre-trained ML API services to a wide spectrum of business and project challenges. You will also learn how AI/ML enabled devices are making it easy for developers to get started with deep learning and other forms of ML. Finally, you will learn how to build, train, and deploy ML models for scale using Amazon SageMaker, a fully-managed service covering the entire machine learning workflow.
Empower University of Washington students and faculty to use AWS DeepLens to create machine learning projects that can have a positive impact on the world.
- Registration deadline: Friday November 1, 2019
- Team announcements and hackathon dates: Thursday November 8, 2019, or sooner
- Hackathon: November 8 – 17, 2019
- Winner announcements: November 25, 2019
- Presentation to AWS leadership: January 2020
Prerequisite: Active UW student, graduate or undergraduate
Preferred: Coding experience
Participants are encouraged to develop project ideas that benefit the following goals, and show how deep learning and computer vision can accelerate our progress towards each goal using the DeepLens platform:
- Increase human productivity
- Increase developer productivity
- ML for Humanitarian
- ML for Earth
- Any other idea which augments and helps human intelligence
- 1st place – DeepLens 2018, AWS DeepRacer Hoodie, and $250 AWS Credits
- 2nd place – DeepLens 2018, AWS DeepRacer T-Shirt and $150 AWS Credits
- 3rd place – DeepLens 2018, AWS DeepRacer T-Shirt and $100 AWS Credits
- All the winners get a chance to pitch the idea to AI and ML Leadership at AWS.
Projects will be evaluated across 3 criteria:
- Technical completeness
You will need to deliver a video presentation and the source code to your solution.
- 90 seconds
- Should include:
- Problem statement
- Approach to solution
Your repository must host the .json model definition, model parameter file, lambda function, gist log and Readme file. The Readme file should contain model location and access instructions, step by step instructions on how to use the trained model and lambda functions, references to any other applicable documents or arxiv papers your project is based on, and testing instructions needed for testing your model. If needed for your solution, also include any side scripts or lambda functions needed to test.
Q. What is AWS DeepLens?
Q. How large should the teams be?
• Each team should have at least two technical members who can implement the solution.
• It’s best if you form the team amongst yourselves; however, if you do not have a team and wish to participate, just tell us in the registration form and we will assign you to a team.
Q. How many hours do we expect you to work?
• Projects should take about 20 hours total to complete.
Q. Who do we contact if we have questions about logistics (not technical questions)?
Tara Shankar Jana | Senior Product Marketing Manager | AWS Machine Learning, AI Devices
Q. Who do we contact if we have technical questions?
Phu Nguyen | Senior Product Manager | AWS Machine Learning, AI Devices