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 23 Oct 2019 03:34 PM by  Patrick Ng
Azure, AWS and GCP snapshot Q3, 2019
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Patrick Ng
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23 Oct 2019 03:34 PM
    Take a specific challenge - portfolio allocation, and focus on how we put it together.

    Visual reference, figures associated with the two Hybrid model articles.

    “Hybrid approach to Well Economics”
    “Operating profitably with $ 50 Oil”

    Azure Power BI - do 80/20 of what we consider minimum set (e.g., map of well locations and bubbles, linked dynamically to crossplots and charts of CUM and initial production), and the rest with Custom Python visual.

    Cost vs benefit - min start up cost and max reach. Power BI is free and comes with Windows 10. Once authored, can be accessible from any iOS and Android device (using Power BI App).

    AWS voice - the natural UI when it comes to portfolio allocation (e.g., four assets, four weights). Alexa is one voice platform, and Google Assistant another (each has around ⅓ market share).

    Example - “Alexa start Basin Game.”

    “Conservative allocation: asset A thirty, asset B thirty, asset C forty percent”

    GCP - speed. Beyond highly tuned Formula-one TPU (tensor processing unit) for Tensorflow in ML, Google’s Sycamore is a tantalizing first step into quantum computing.

    Scenario - when using Simulation to drive DeepMind-like GAN (generative adversarial network), in reinforcement ML, say training asset valuation with the challenges of many commodities price scenarios (e.g., two states, P90 $ 48 and P10 $ 80 oil), and macroeconomic factors (e.g., interest rates and cost of financing project), going supersonic on Sycamore qubits will surely overtake F1 on TPU.
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