Data Science, Machine Learning, and H2O
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Soumith Chintala at AI Frontiers: A Dynamic View of the Deep Learning World
- 1. Soumith Chintala Facebook AI Research A dynamic view of the deep learning world
- 2. Overview • What is Torch? • The Community • Today’s AI • What Next?
- 3. What is ? • Interactive Scientific computing framework
- 4. What is ? • Interactive Scientific computing framework
- 5. What is ? • Similar to Matlab / Python+Numpy
- 6. What is ? • Easy integration into and from C • Example: using CuDNN functions
- 7. What is ? • Strong GPU support
- 8. Neural Networks • nn: neural networks made easy • building blocks of differentiable modules
- 9. autograd by • Write imperative programs • Backprop defined for every operation in the language
- 10. Distributed Learning • Multi-GPU and Multi-Node • distlearn by • MPI-like model • scales to a large amount of nodes
- 11. Core Philosophy • Interactive computing • No compilation time • Imperative programming • Easy to understand, think and debug • Minimal abstraction • Thinking linearly • Maximal Flexibility • No constraints on interfaces or classes
- 12. Community
- 13. Community
- 14. Community
- 15. Community
- 16. Community
- 17. Community
- 18. Community
- 19. Community
- 20. Today’s AI • Reasonably solved to be used in production
- 21. Today’s AI • Classification and Detection • in images, videos, volumetric data, sparse datasets DenseCap: Johnson et. al. https://github.com/jcjohnson/densecap
- 22. Today’s AI • Segmentation DeepMask: Pinhero et. al.
- 23. Today’s AI • Text Classification (sentiment analysis etc.) • Text Embeddings • Graph embeddings • Machine Translation • Ads ranking
- 24. Today’s AI static datasets + static model structure
- 25. Today’s AI static datasets + static model structure model does not change over training time
- 26. Today’s AI static datasets + static model structure model does not change over training time offline learning
- 27. Active and Future AI Research • Agents training in different and new environments
- 28. Active and Future AI Research • RL Agents in different and new environments • Example: OpenAI Universe
- 29. Active and Future AI Research • RL Agents in different and new environments • Example: OpenAI Universe • Online Learning • Example: self-driving cars
- 30. Active and Future AI Research • RL Agents in different and new environments • Example: OpenAI Universe • Online Learning • Example: self-driving cars • Dynamic Neural Networks • self-adding new memory or layers • changing evaluation path based on inputs
- 31. Active and Future AI Research • RL Agents in different and new environments • Example: OpenAI Universe • Online Learning • Example: self-driving cars • Dynamic Neural Networks • self-adding new memory or layers • changing evaluation path based on inputs • Structured Prediction • Viterbi style decoders
- 32. A Next-Gen Framework • Interop with environments • Like Universe, VizDoom, etc. • Easy data loaders • Rich scientific ecosystem (such as SciPy) • Neural Networks are not the only methods to be used • Dynamic Neural Networks • with no specific or static structure • Minimal Abstractions • better debugging and low-level programming in the hands of researcher • Graph Compilers • To speed up structured prediction and fuse operations • Numba, XLA, Theano
- 33. Let’s share! Torch Caffe TensorFlow Theano MXNet, etc. Eigen TH CuDNN MKL Numba XLA NNVM MPI NCCL
