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).

2. Open Source Deep
Learning &
Machine Learning
with Microsoft CNTK
& LightGBM
Rui Quintino, DevScope

3. #MSOSCAMP

4. Agenda
•Deep Learning vs Machine Learning
•CNTK & LightGBM
• (also a bit of awesome Docker, Python & Jupyter)
•Test Drive:
•Kaggle Data Science Bowl 2017 Competition

6. 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

7. Source:
Dr. Miguel González-Fierro
https://info.microsoft.com/rs/157-GQE-382/images/EN-WBNR-Slidedeck-AAIoT-Cancer-Image-Detection-
Competition-23Mar17.pdf

8. 1.
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10. 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/

12. https://www.kaggle.com/c/data-science-bowl-2017

13. https://gallery.cortanaintelligence.com/Notebook/Medical-Image-Recognitin-for-the-Kaggle-Data-Science-Bowl-2017-with-CNTK-and-LightGBM-1

30. https://www.kaggle.com/rquintino/data-science-bowl-2017/fast-lung-segmentation-roi-search-features-calc