ArtNet - IBM OpenPOWER Cognitive Cup Contest Winning talk

ArtNet - IBM OpenPOWER Cognitive Cup Contest Winning talk

Transcript

1. ArtNet The new age Art Connoisseur ;) OpenPower Cognitive Cup Winning Entry
2. Developer Challenge Experience Praveen Sridhar @psbots
3. About me Praveen Sridhar @psbots Machine Learning R&D for Recently joined as Machine Learning Engineer at
4. Cognitive Cup Challenge Problem Can computers think “deeply” :P about art?
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
6. Implementation ArtNet uses • a Convolution Neural Network to figure out patterns in a painting • and classifies it into different genres
7. Implementation It uses the Keras Deep Learning library to train the CNN model
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
9. Hyperparameter Optimization The Real Deal HYPER WHAT?
10. Hyperparameter Optimization The Real Deal Enter the “hyperas” library A very simple convenience wrapper around hyperopt for fast prototyping with keras models.
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! ;)
12. Going Forward • Predict the artist given a painting • Help Social Science people in understanding art influences : ✴ which artist influenced whom? ✴ to what extent?
13. Thanks! Praveen Sridhar @psbots