We’re offering machine learning engineers a hands-on tutorial on generating annotated data at scale.
Creating training data sets for visual AI has traditionally been an exhausting and expensive task involving human annotation and low-grade images. The end result is usually frustration, cost and delay together with an unpredictable neural net. In this session, learn how AI.Reverie solves that problem by creating perfectly marked up images in the tens of thousand at a vastly reduced cost and in a fraction of the time. Join us in this seminar and learn/understand how the process works and what synthetic data has over real data in your Computer Vision applications.
When: September 16, 2020, 12:00 PM EST
Speakers: Daeil Kim & Jesse Taylor
Meet the Speakers
Daeil Kim, Co-Founder and CEO @ AI.Reverie
Daeil co-founded AI.Reverie, a company that creates synthetic data to help organizations train their machine learning vision algorithms in a more effective, efficient way. Prior, Daeil was a senior data scientist at The New York Times, where he developed machine learning solutions to optimize audience acquisition. Kim received his Ph.D. in computer science from Brown University. His Ph.D. research focused on the development of scalable machine learning algorithms, and his research has been published in several of the top machine learning conferences (NIPS, ICML, AISTATS).
Jesse Taylor, VP of Engineering @AI.Reverie
Jesse joined AI.Reverie as Vice President of Technology in 2020. Jesse has held senior leadership roles at interactive entertainment companies including Electronic Arts, Sega, Glu mobile, Jam City, NCSOFT and Namco/Bandai.