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ArtNet - IBM OpenPOWER Cognitive Cup Contest Winning talk

ArtNet - IBM OpenPOWER Cognitive Cup Contest Winning talk

ArtNet - IBM OpenPOWER Cognitive Cup Contest Winning talk

Praveen Sridhar

November 15, 2016
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  1. ArtNet
    The new age Art Connoisseur ;)
    OpenPower Cognitive Cup Winning Entry

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  2. Developer Challenge
    Experience
    Praveen Sridhar
    @psbots

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  3. About me
    Praveen Sridhar
    @psbots
    Machine Learning R&D for
    Recently joined as
    Machine Learning Engineer at

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  4. Cognitive Cup Challenge
    Problem
    Can computers think “deeply” :P about art?

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  5. What ArtNet Does
    ArtNet “knows” what a painting is about.
    Be it a landscape or still life or a portrait,
    or any of the different art genres.
    Examples of genres :
    Still Life Cityscape Religious

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  6. Implementation
    ArtNet uses
    • a Convolution Neural Network to figure
    out patterns in a painting
    • and classifies it into different genres

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  7. Implementation
    It uses the Keras Deep Learning library to
    train the CNN model

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  8. Implementation
    The OpenPOWER
    Deep Learning Distribution
    Frameworks like Theano and Tensorflow
    available as Pre-built binaries optimized
    for GPU acceleration
    Adding Keras was a breeze, since it runs
    on top of Theano or Tensorflow

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  9. Hyperparameter Optimization
    The Real Deal
    HYPER WHAT?

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  10. Hyperparameter Optimization
    The Real Deal
    Enter the “hyperas” library
    A very simple convenience wrapper around hyperopt for fast
    prototyping with keras models.

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  11. Enter the “elephas” library
    Distributed Deep Learning with Keras & Spark
    Thought : SuperVessel Cloud makes it a breeze to
    spin up multi node spark instances, why not use it as
    Nitrox! ;)

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  12. Going Forward
    • Predict the artist given a painting
    • Help Social Science people in
    understanding art influences :
    ✴ which artist influenced whom?
    ✴ to what extent?

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  13. Thanks!
    Praveen Sridhar
    @psbots

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