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.
Deep Learning - Machine Learning TIG
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0 Replies and 378 Views Read This First!      378  0 Started by  Bogdan Michka Welcome to AAPG N.E.T., an online space where you can Network, Engage and Talk. Please complete the following steps before you begin interacting with the discussion boards: 1. Take time to review your profile and privacy settings, paying attention to your display name and photo, as they will be used to identify your posts. 2. Read the Discussion Boards FAQ page to learn the rules and get familiar with the features: http://www.aapg.org/career/aapg-net/discussion-boards-faq — pa...
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09 May 2017 03:37 PM
0 Replies and 15 Views Energy in Data 2021 - Greeting  15  0 Started by  Patrick Ng Thinking about the two-morning workshop https://www.youtube.com/watchv=t53OXaV_5M8 It is a click away. https://energyindata.org/
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08 Apr 2021 12:16 PM
0 Replies and 46 Views First look at well production data - before tackling how well ML models generalize?  46  0 Started by  Patrick Ng Follow up to prior post “AI teaches itself - no manual labels required” 03.08.2021. Perspective - day in and day out, geologists, engineers, and analysts are active players in asset evaluation and project economics. 80 of the time spent on generating oil and gas projects: exploring ideas with a map, finalizing investment decisions and picking well locations on a map. And what else we may do the other 20 Due diligence and risk assessment. A common issue is the time and effort it takes to p...
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15 Mar 2021 01:09 PM
0 Replies and 46 Views AI teaches itself - no manual labels required  46  0 Started by  Patrick Ng One billion public-facing Instagram photos were used to train an algorithm created by Facebook to learn to recognise images by itself. https://www.bbc.com/news/technology-56321296 Implication - it becomes more important than before when embarking on AI / deep learning projects, we shall ensure adequate QC and comprehend the underlying distribution. Understand what goes into the system and be prepared to flag surprises (e.g., what we cannot comfortably explain among ourselves). Scenario ...
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08 Mar 2021 12:36 PM
0 Replies and 67 Views AI surprised Google developers - tacking like human  67  0 Started by  Patrick Ng https://www.bbc.com/future/article/20210222-how-googles-hot-air-balloon-surprised-its-creators Note - encoding (chaining of alpha-numeric) , e.g., 'Be5' to move a bishop, makes powerful feature engineering when training machine to play chess.
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23 Feb 2021 10:12 PM
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