• Home
  • Our Board
  • Meetings
  • Membership
  • Contact
  • Home
  • Our Board
  • Meetings
  • Membership
  • Contact
CTO Roundtable
  • Home
  • Our Board
  • Meetings
  • Membership
  • Contact

    Meetings

    June 2022
    March 2022
    October 2021
    June 2021
    February 2021
    December 2020
    October 2020
    July 2020
    May 2020
    November 2019
    September 2019
    June 2019
    May 2019
    April 2019
    December 2018
    October 2018
    August 2018
    April 2018
    January 2018
    December 2017
    August 2017
    July 2017
    April 2017
    January 2017
    December 2016
    July 2016
    May 2016
    March 2016
    February 2016
    December 2015
    October 2015
    June 2015
    April 2015
    March 2015
    December 2014
    April 2014
    February 2014
    October 2013
    August 2013
    May 2013
    February 2013
    November 2012
    August 2012
    March 2012
    February 2012
    December 2011
    September 2011
    June 2011
    March 2011
    February 2011
    November 2010
    July 2010
    April 2010
    January 2010
    November 2009
    September 2009
    June 2009
    April 2009
    January 2009
    November 2008
    September 2008
    April 2008
    February 2008
    November 2007
    August 2007

    RSS Feed

Back to Blog

How and When to use Deep Learning

12/17/2018

 
In domain after domain, Deep Learning is outperforming both people and competing algorithms at practical tasks:
  • ImageNet Hit@5 object recognition error rates have fallen 88% since 2011 and can now recognize objects in photos faster and better than you can
  • All major speech recognition engines (Google's, Baidu's, Siri, etc.) automatically translate a speaker's voice in one language to the same voice speaking another language.
  • Machines can now beat you at Atari and Go
 
These breakthroughs are visible as both product offerings (Siri, Real-time Ray-tracing on NVIDIA Turing cards, Netflix Recommendations, etc.) as well as breakthrough results on international open benchmarks. All successful deep learning product teams:
  1. Identify the best-matched organizational opportunities to drive ROI with machine learning
  2. Address the talent crunch for deep learning expertise
  3. Warehouse sufficiently large labeled data sets for training and
  4. Update their models at scale as data distributions drift over time
 
We will review all four of these considerations, understand some of the key discriminators between product success and failure, and learn how to identify opportunities best suited for the application of Deep Learning.
 
Dr. John Kaufhold, data scientist and managing partner at Deep Learning Analytics, will lead the discussion. Deep Learning Analytics was one of the four fastest growing companies by revenue in Arlington, Virginia in 2015, and again in 2016. Dr. Kaufhold also serves as Secretary of the Washington Academy of Sciences and is a regular contributor to the DC Data Community, where he moderates the DC2 Deep Learning Discussion list. Prior to founding Deep Learning Analytics, Dr. Kaufhold investigated deep learning algorithms at NIH, SAIC and at GE's Global Research Center. Dr. Kaufhold is named inventor on 10 issued patents in image analysis, and author/coauthor on 40+ publications in the fields of machine learning, image understanding and neuroscience.

WHEN: December 17, 2018  from 8 AM - 10 AM

WHERE: Sphere Of Influence, 1420 Spring Hill Rd., Lobby Level, McLean, VA 22102
​


read more

Comments are closed.
Copyright © 2020 Washington Area CTO Roundtable
All DC photographs Copyright © Nitin Mehrotra (Thank You!)