GDC is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

View, browse and sort the ever-growing list of GDC sessions by time, pass type, topic, and format. With GDC Session Scheduler, you can build your schedule in advance and access it during the show via export or with the GDC Mobile App, once live.

If you've registered for GDC, you can use the same login credentials for your Session Scheduler account. Adding a session to your schedule does not guarantee you a seat. Sessions do fill up, so please arrive early to sessions that you would like to attend.

Technical Artist Summit: Data for Machine Learning

Greg Amato (Senior Technical Artist, Certain Affinity)

Location: Room 210, South Hall

Date: Tuesday, March 17

Time: 2:10pm - 2:40pm

Pass Type: All Access Pass, Core+Summits Pass, Summits Pass - Get your pass now!

Topic: Visual Arts

Format: Session

Vault Recording: TBD

Audience Level: All

Many people are exposed to machine learning with a general understanding of how it can be used to classify images, identify objects, translate text. Fewer actually understand the data and its importance to driving the model. In this presentation, the speaker explains some of the different types of data used and an understanding of what data is good and bad. The speaker will present common examples of good and bad data, how to handle bad data, and showing how data driven solutions can strengthen technical artists toolbelt.

Takeaway

Takeaways will be for attendees to have a larger understanding of the importance of good data, what to look for within data to identify bad data, and how to develop foundational skills to maintain and apply data for machine learning solutions as a technical artist.

Intended Audience

The intended audience is for attendees who show an interest in where and how machine learning will be a worthwhile investment within a production pipeline and what questions to ask themselves when looking into or actively developing for a machine learning based solution.