In this episode, I’m joined by Kiran Vajapey, a human-computer interaction developer at Figure Eight.
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In this interview, Kiran shares some of what he’s has learned through his work developing applications for data collection and annotation at Figure Eight and earlier in his career. We explore techniques like data augmentation, domain adaptation, and active and transfer learning for enhancing and enriching training datasets. We also touch on the use of Imagenet and other public datasets for real-world AI applications. If you like what you hear in this interview, Kiran will be speaking at my AI Summit April 30th and May 1st in Las Vegas and I’ll be joining Kiran at the upcoming Figure Eight TrainAI conference, May 9th&10th in San Francisco.
Thanks to our Sponsor
A huge thanks to Figure Eight for sponsoring this episode of the podcast! At Figure Eight’s Train AI you can join me and Rob, along with a host of amazing speakers like Garry Kasparov, Andrej Karpathy, Marti Hearst and many more and receive hands-on AI, machine learning and deep learning training through real-world case studies on practical machine learning applications. For more information on TrainAI, head over to www.figure-eight.com/train-ai, and be sure to use code TWIMLAI for 30% off your registration!
Mentioned in the Interview
- Figure Eight
- Train AI Conference
- Register for the AI Summit
- Check out @ShirinGlander’s Great TWIML Sketches!
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0