Next Meetup

DC Deep Learning Working Group Meetup at Walmart Labs
This is a special co-hosted meeting of the DC Deep Learning Working Group and Walmart Labs. The DCDLWG is planning to resume our interactive, hands-on meetings again in August, but in the meanwhile, this is an opportunity to gather and hear about Walmart Lab's vision and approach to machine learning. Hope to see you there. Location: Walmart Labs, 10790 Parkridge Blvd, Suite 200, Reston, VA 20191 - Park in either side of the building - Walmart Labs Volunteers will receive and bring the associates for registration and to the meeting hall The agenda would be : 6:00pm - 7:00pm - Networking and light refreshments 7:00pm - 8:30pm - Presentation(s) 8:30pm - 9:00pm - Q & A Presentation 1: GPU Accelerated Computing for AI and beyond Presentation 2: The lifecycle of a data science project in Walmart, how machine learning platform is enabling data science teams to develop and deploy modes faster and how various teams are utilizing the platform. Speaker 1: Richard Ulrich Richard Ulrich has been at Walmart almost 28 years and has spent his entire career innovating in retail technology. Richard has innovated in each area of responsibility throughout his career and has approximately eighteen granted or pending patents in areas such general computing, RFID, analytics, and workforce management. Richard is currently working on high performance computing for advanced analytics, optimization, and machine learning, specifically focusing on the use of GPU to accelerate parallel computation. Speaker 2: Rakshith Uj Rakshith Uj is the product manager for the machine learning platform at Walmart Labs. He has been with Walmart for more than 4 years and has worked across customer experience, supply chain and machine learning as a product manager.

Walmart Labs

10790 Parkridge Blvd, Suite 200, Reston, VA 20191 · Reston, VA

What we're about

The DC Deep Learning Working Group (DC DLWG) is for those interested in becoming proficient with Deep Learning concepts and coding. Our approach is highly collaborative and we learn through discussion rather than by just listening to lectures. We use journal papers, video lectures and Deep Learning courses to drive our discussions and provide coding exercises. Occasionally group members present special related topics helpful to the group’s understanding. Our group grew out of the Machine Learning Journal Club, led by Don Vetal, which focused on studying academic papers related to machine learning, optimization and data science and pursued knowledge of the real guts of the algorithms. We continue keeping to that spirit, but now focus on coding as well.

We generally meet weekly. The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group.

We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning. We use Python and Google’s TensorFlow framework. We assume participants are comfortable enough with programming to pick up Python on their own. In August 2016 we finished covering the Udacity course on Deep Learning and TensorFlow (https://www.udacity.com/course/deep-learning--ud730). It would be highly beneficial, though not required, to review that material, as we proceed in September 2016 to follow Stanford’s CS224d Deep Learning for Natural Language Processing (http://cs224d.stanford.edu/).

Members (1,171)