))
4. Deep Learning in Embedded Electronics for Short-Term Storm Forecasting with Max von Wolff
Fetch error
Hmmm there seems to be a problem fetching this series right now.
Last successful fetch was on December 09, 2021 19:25 (
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 313356328 series 3267799
Content provided by Brendon Matusch, Anish Singhani, Brendon Matusch, and Anish Singhani. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brendon Matusch, Anish Singhani, Brendon Matusch, and Anish Singhani 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.
Brendon and Anish interview Max von Wolff, a student from Mayen, Germany, about his research on short-term predictions of storm movement using deep learning with a network of distributed weather observation devices. We discuss the advantages and difficulties of processing data collected with embedded devices in the field, the use of machine learning methods such as autoencoders for processing this data, and Max's plans to scale up his research! Please send comments to shatteredgradients@gmail.com.
…
continue reading
7 episodes
Share
