May 21, 2020
In this episode, our guest is Eugene Dubossarsky, who is the chief data scientist at AlphaZetta and co-founder at multiple data science companies in Australia. He is a managing partner of the Global Training Academy and he is teaching too. Eugene is dealing with machine learning since the 80s, so you can imagine he has a very strong opinion about different topics in this industry. We talked about random forests, neural nets, boosting strategies, the importance of understanding data and statistics. We couldn't skip talking about the effects of the current and upcoming crisis either.
Eugene's LinkedIn: https://www.linkedin.com/in/eugene-dubossarsky-09208a1/
Reask Track cyclons: https://reask.earth/
The Prediction Machines book:
Hopfield network: https://en.wikipedia.org/wiki/Hopfield_network
Finnian Lattimore and Cheng Soon Ong Paper
About the host: Miklos Toth is a Machine Learning enthusiast who is also teaching Machine Learning and Deep Learning at international companies, working on various ML projects as an engineer.
About the co-host: Levente Szabados is a Deep tech leader, consultant, and manager with a special interest in artificial intelligence, cognitive sciences, data science, and deep learning. He is teaching various ML courses at the Frankfurt School of Finance and Management.
About the podcast: The podcast was created to serve the technical community with details of ML algorithms, use-cases. The episodes are recorded in Budapest, Hungary, Europe.
Website of the podcast: http://machinelearningcafe.org/
Host's LinkedIn: https://www.linkedin.com/in/miklostoth/
Co-Host LinkedIn: https://www.linkedin.com/in/levente-szabados-76334728/
Write an email to the host: email@example.com
Equipment: On the host's side the show is recorded with Rode Procaster and with Roland Quad-Capture audio interface.
Music is from https://filmmusic.io, intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro "Acid Trumpet" by Kevin MacLeod (https://incompetech.com), License: CC BY (http://creativecommons.org/licenses/by/4.0/)