Neural Concept: Deep Learning for Engineering - Pierre Baqué | Podcast #28

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Pierre is Chief Executive Officer at Neural Concept, which he co-founded in 2018. Pierre received an engineering degree in Applied Mathematics and a Masters's degree in Operations Research from Ecole Polytechnique in France. After working as an optimization and machine learning engineer for Credit-Suisse in London, he joined the Computer Vision Laboratory at EPFL, where he obtained his doctoral degree under the supervision of Prof. Pascual Fua and Prof. Francois Fleuret. His research focused on Deep Structured Learning and Variational Inference applied to Computer Vision. During and after his thesis, Pierre worked as a consultant in Machine Learning and optimization for multiple companies such as Thales, EFCables, Sonalytic (Spotify), and Honywell.

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TIME STAMPS
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0:00 : Sponsor mention & Intro
2:03 : Who is Pierre Baqué?
3:45 : Deep Learning for Engineering Applications
7:19 : Steps involved in the Deep Learning process
12:00 : How many samples are needed?
12:47 : Which industries does Neural Concept work in?
14:14 : Steps involved when using Neural Concept
15:32 : Why Deep Learning + Engineering is so important
17:28 : Light vs. Expert package
20:16 : Collaboration with AirShaper
22:48 : Neural Concept's world records
24:35 : The future of Deep Learning in engineering & Neural Concept
26:57 : Reaching out to Pierre & Career at Neural Concept
27:50 : Demos for Neural Concept available?
28:50 : Last words from Jon & Closing Remarks

Podcast Recorded: November, 14th 2020 - Subscriber Release Count: 9,378.
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