In this episode, i’m joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course.
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The courses are designed to make deep learning more accessible to those without the extensive math backgrounds some other courses assume. Rachel and I cover a lot of ground in this conversation, starting with the philosophy and goals behind the Fast AI courses. We also cover Fast AI’s recent decision to switch to their courses from Tensorflow to Pytorch, the reasons for this, and the lessons they’ve learned in the process. We discuss the role of the Fast AI deep learning library as well, and how it was recently used to held their team achieve top results on a popular industry benchmark of training time and training cost by a factor of more than ten.
Join us at the TWiML Online Meetup!
Tomorrow, May 15th, the TWiML Online Meetup is back! Our main event will be a presentation by Santosh GSK on the paper YOLO9000: Better, Faster, Stronger by Joseph Redmon and Ali Farhadi. YOLO, or You Only Look Once, is a very popular image object detection system, and we’ll thoroughly review it along with the broader object detection landscape, the current state of detection algorithms and the various challenges ahead. If you aren’t already signed up, head over to twimlai.com/meetup to register. See you there!
Mentioned in the Interview
- Fast.ai Forums
- Deep Learning Part 1: Practical Deep Learning for Coders
- Deep Learning Part 2: Cutting Edge Deep Learning for Coders
- Computational Linear Algebra: Online textbook and Videos
- Onyx Platform
- Fast.ai Library
- 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