TensorFlow 2.0 & Documentation Sprint

GDG Gulu
Sat, Jun 8, 2019, 7:00 PM (EAT)

About this event

TensorFlow 2.0 is a major release focused on ease of use and clarity while maintains its flexibility. Are you interested in learning about what is new with TensorFlow 2.0?

Would you like to contribute to TensorFlow 2.0 and learn the PR and review process? You can get started by improving its documentation.

Come join the pilot of the global docs sprint at GDG Seattle, where Paige Bailey and Margaret Maynard-Reid will walk through how to review and create pull requests for making changes to TensorFlow documentation. There will be other TensorFlow contributors joining remotely as well.

Target audience: ideally you know Machine Learning, TensorFlow or Python but it's not a pre-requisite in order to contribute to TensorFlow docs.

9:30AM Check-in & breakfast
10:00AM Intro to TensorFlow 2.0, Paige Bailey
10:30 AM Contribute to TensorFlow Docs, Paige Bailey & Margaret Maynard-Reid
10:45AM Hands-on. [Please bring your own computer]
12:00PM Lunch
12:45PM Wrap up and next steps

Paige Bailey (twitter handle: @DynamicWebPaige)
Paige is a TensorFlow Developer Advocate at Google, based in Mountain View, CA. Prior to joining Google, Paige worked as a senior software engineer in the office of the Azure CTO; as a Cloud Developer Advocate for machine learning at Microsoft; and as a data scientist for Chevron in Houston, TX. Paige has over a decade of experience using Python for data analysis, five years of experience doing machine learning - and can't wait to show you about the new capabilities in TensorFlow 2.0.

Margaret Maynard-Reid (twitter handle: @margaretmz)
Margaret is a Google Developer Expert (GDE) for Machine Learning, and she develops Android apps with intelligence. She leads GDG Seattle and co-organizes Seattle Data/Analytics/Machine Learning meetup groups. She writes blogs and speaks at conferences on TensorFlow, deep learning and Android. She is passionate about community building and helping others get into Artificial Intelligence and Machine Learning.

This will be held in building B (room KIR-6THB-1-Backstreet/Banana Seat) at Google Kirkland. When you arrive you can park in a guest parking spot by building A or B.