))
Deep learning tools create business complications
Archived series ("HTTP Redirect" status)
Replaced by: Talking Data Podcast » Episodes
When?
This feed was archived on April 04, 2018 03:10 (
Why? HTTP Redirect status. The feed permanently redirected to another series.
What now? If you were subscribed to this series when it was replaced, you will now be subscribed to the replacement series. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 180419339 series 1006425
Content provided by Talking Data Podcast » Episodes. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Talking Data Podcast » Episodes or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Deep learning is great and all, but how do you implement in a production environment?
That’s a question that a lot of businesses are asking. Deep learning may look tempting when you think about how powerful it is. Research projects have shown it capable of replicating human vision and other types of thought processes. But it also can bring some complications to business processes.
In this edition of Talking Data we look at some of the business implications of deep learning, particularly when taking it from research projects to production environments.
The post Deep learning tools create business complications appeared first on Talking Data Podcast » Episodes.
80 episodes
Share
