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Open Source Deep Learning & Machine Learning with Microsoft CNTK & LightGBM

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Session at Microsoft Open Source Camp 2017
https://msoscamp.io/
Introducing Microsoft Cognitive Toolkit, a deep-learning framework for neural networks to model, train & use popular neural networks architectures for large range of machine learning scenarios.We’ll see how to start with custom image classification & computer vision scenarios using CNTK, running on CPU and GPUs, together with LightGBM (Microsoft open source gradient boosting framework).

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Open Source Deep Learning & Machine Learning with Microsoft CNTK & LightGBM

  1. 1. Open Source Deep Learning & Machine Learning with Microsoft CNTK & LightGBM Rui Quintino, DevScope
  2. 2. #MSOSCAMP
  3. 3. Agenda •Deep Learning vs Machine Learning •CNTK & LightGBM • (also a bit of awesome Docker, Python & Jupyter) •Test Drive: •Kaggle Data Science Bowl 2017 Competition
  4. 4. Source: http://vision03.csail.mit.edu/cnn_art/ https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf https://info.microsoft.com/rs/157-GQE-382/images/EN-WBNR-Slidedeck-AAIoT-Cancer-Image-Detection- Competition-23Mar17.pdf
  5. 5. Source: Dr. Miguel González-Fierro https://info.microsoft.com/rs/157-GQE-382/images/EN-WBNR-Slidedeck-AAIoT-Cancer-Image-Detection- Competition-23Mar17.pdf
  6. 6. 1. 2. 3. 4. 5.
  7. 7. 1. 2. 3. 4. 5. 6. 7.
  8. 8. Source: https://blogs.technet.microsoft.com/machinelearning/2017/02/17/quick-start-guide-to-the-data-science-bowl- lung-cancer-detection-challenge-using-deep-learning-microsoft-cognitive-toolkit-and-azure-gpu-vms/
  9. 9. https://www.kaggle.com/c/data-science-bowl-2017
  10. 10. https://gallery.cortanaintelligence.com/Notebook/Medical-Image-Recognitin-for-the-Kaggle-Data-Science-Bowl-2017-with-CNTK-and-LightGBM-1
  11. 11. https://www.kaggle.com/rquintino/data-science-bowl-2017/fast-lung-segmentation-roi-search-features-calc

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