TWiML Talk #42 – Marrying Physics-Based and Data Driven ML Models with Josh Bloom

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NOTE: I host a weekly podcast on all things machine learning and AI. The main page for this show is over on my This Week in Machine Learning & AI podcast web site. Check out the complete notes for this show here:
Josh Bloom – Marrying Physics-Based and Data Driven ML Models

 
Recently I had a chance to catch up with a friend and friend of the show, Josh Bloom, vice president of data & analytics at GE Digital. If you’ve been listening for a while, you already know that Josh was on the show around this time last year, just prior to the acquisition of his company Wise.io by GE Digital.

It was great to catch up with Josh on his journey within GE, and the work his team is doing around Industrial AI, now that they’re part of the one of the world’s biggest industrial companies. We talk about some really interesting things in this show, including how his team is using autoencoders to create training datasets, and how they incorporate knowledge of physics and physical systems into their machine learning models.

Continue on to the complete show notes:
Josh Bloom – Marrying Physics-Based and Data Driven ML Models

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