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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Last Post 16 Apr 2021 01:41 PM by  Patrick Ng
EiD - Probing Questions and Seeking Answers
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Patrick Ng
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16 Apr 2021 01:41 PM
    These three observations can benefit taking feedback / sharing experience from a wider community -

    1) Edge computing - lots of applications in edge computing helps us reduce cost and increase efficiency. Edge computing enables cheaper ML model to be built and at different time scales.

    2) Knowledge curation - make open-source movement useful, we need to organize information in one place (reservoir, geoscience, engineering) and make that searchable like Yahoo! in the early days of internet.

    3) Buy vs Build - stimulating exchange from one operator's presentation on "Edge-Stream Processing Solution", we learned that: a) user interface (UI) is critical ("UI not good, have nothing") and b) case for "Build own system" boils down to having resources available, more efficient and laser focused on business needs.

    Perspective - #3 is a more complex business decision. As our Exec member Susan Nash commented "it has to do with how the company thinks of itself. Do they want to accomplish a task, or is the goal to innovate?" That may well be the single most important question to ask in energy transition.

    Now it is your turn to chime in.
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