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 13 Jul 2021 03:18 PM by  Patrick Ng
Deep Learning with Deep Earth Data
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Patrick Ng
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13 Jul 2021 03:18 PM
    Remember the movie "Inception", DiCaprio explained about planting a thought inside someone's mind - "We need to go deeper"?

    This is our opportunity to do really deep dive into Earth's data universe, e.g., is 5-layer RNN good enough, or go deeper with 128, and if quantum computing is handy, why not 1,024 kind of deep?

    No doubt what DeepTime Digital Earth reveals will span years of work and lead to hundreds of papers published. But for data sleuths, all it takes is a click to start learning more.

    Happy reading and feel free to share items of interest in this blog.

    Update -

    Actionable - Energy Transition is at the forefront of AAPG-SEG-SPE, aka Energy in Data

    Pivot from fossil to CCUS, geothermal and alternate energy storage.

    As a community, we shall focus on application of data and tools to solve business problems.

    Request for comments - what action will you take to engage DDE in that effort?

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