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Deep Learning/Machine Learning Technical Interest Group (TIG)

Applying new analytics, neural networks, computational approaches using structured and unstructured data, and also training neural networks with supervised and unsupervised algorithms. Chaired by Patrick Ng and Andrew Munoz.

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Last Post 15 Apr 2020 10:58 AM by Patrick Ng

Dr. Michael Pyrcz offers course in ML for Geoscientists

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Susan Nash

New Member
Posts:27

24 Feb 2020 10:40 AM
Have you been curious about implementing Machine Learning into your Geoscience and Subsurface workflows? Where should you start? The one-day course, Intro to Energy Machine Learning for Geoscientists, is a great starting point and will introduce the theory and practical applications of machine learning within the Subsurface. Date And Time Tue, March 31, 2020 8:00 AM – 4:30 PM CDT Location Aramco Services Company 1200 Smith Street Houston, TX 77002 View Map The purpose of this workshop is: 1. To introduce methods that improve data-driven decision making within geoscience teams. 2. To get familiar with a variety of Machine Learning methodologies by learning them through real industry applications. The course will be delivered by renowned Geostatistics and Reservoir Modeling expert, Dr. Michael Pyrcz. (@geostatsguy) Dr. Pyrcz is also Chief Science Officer at daytum and a Distinguished Lecturer of AAPG. Course materials will consist of web-hosted lecture materials, instructor led, well-documented methods, workflow demonstrations, and coding examples. The workshop will be delivered via a cloud-based Jupyter Lab session, so the only thing you need to bring are your laptop and your fingers! Breakfast, lunch, coffee and light snacks included! We hope to see you there. Event page with outline and agenda: Eventbrite https://www.eventbrite.co...-tickets-95952509539 Please direct any questions to: [email protected] If you are an AAPG member, please use discount code AAPGmember55 to get $55 off registration!
Attachments daytum_AAPG_Workshop.png

Patrick Ng

Basic Member
Posts:148

15 Apr 2020 10:58 AM
March 31, 2020 - Houston we all observed stay-at-home guideline and the course was Zoomed. Impression - efficient compressed delivery in half the time (1-5 pm), virtual and yet rich learning experience (in particular the potential of marrying geostats and machine learning). Going forward, AAPG can maximize reach using more of the Energy ML format. i.e., Zoom + GitHub = real-time hands-on exercise without logistics hiccups / downtime* *no need to install Python and Tensorflow libraries on own machine.