Deep Learning for IoT : is there a shallow end of the pool?
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Deep Learning for IoT : is there a shallow end of the pool?
- 1. Deep Learning for IoT Is there a shallow end of the pool? Venu Vasudevan, PhD
- 2. Agenda what is deep learning does it deserve the hype? deep learning - IoT lens technology ‘amuse bouche’ deep learning swimming pool getting value vs getting lost ’live code’ | offline resources
- 3. deep learning : brief history like all good things ‘invented’ by Aristotle Failure #2: Shallow networks Are expensive 3rd time’s a charm Deep gets ‘cheap’ Failure #1: Life is not Linear
- 4. why the hype : performance
- 5. why the hype : performance
- 6. why the hype : engineering $$$ Feature Engineering Cat Detector Detectors SiFT HOG Moments FANG .. Labeled Data Set traditional machine learning Credits. https://goo.gl/pb6axr
- 7. why the hype : engineering Feature Engineering Cat Detector Labeled Data Set deep learning Credits. https://goo.gl/pb6axr
- 8. why the hype : optimizing Old : The bias-variance whac-a-mole game
- 9. why the hype : optimizing New : Data (and models) vincit omnia
- 10. Deep Learning : Basics space (images,video) time(digital. time series)
- 11. How CNNs Work Convolution. auto-discover the right filters. turn image =stack of filtered images Pooling. make images smaller ReLU. ‘fix the math’ & because life is not linear
- 13. RNNs simplified Internally. 4 mini Neural Nets - remember, forget,save,add
- 14. RNN : a Pokemon build Credits.Edwin Chen - https://goo.gl/Tw7LTj
- 15. RNN : a Pokemon build
- 16. RNN : a Pokemon build
- 17. where in the ‘pool’ do i want to be? skills data MLpipeline learningcurve people culture Investment
- 18. Data clean plentiful good news! DL is your friend. all you need is an infinite budget!
- 19. Data clean plentiful good news! DL is your friend. all you need is an large budget! high odds you’re headed here
- 20. Data clean plentiful good news! DL is your friend. all you need is an large budget! high odds you’re headed here consider data augmentation techniques https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
- 21. Data https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html Learn enough about ‘catness’ .. to generate ‘two cat’ pictures Real Synthetically generated
- 22. Data clean plentiful good news! DL is your friend. all you need is an large budget! high odds you’re headed here consider data augmentation techniques
- 23. ML Pipeline Complexity DaaS deep learning as a service ‘simple’ deep learning container optimized deep learning container low-level deep learning h/w
- 24. Team/Problem large, clear & clean problem deep bench ensemble learners transference learning Keras
- 25. Keras code snippets CNN RNN code - short & intuitive
- 26. Even ‘deep benches’ use Ensembles Credits. https://goo.gl/K1WGJx dramatic ‘extreme event’ forecasting improvement RNN@Uber
- 27. In summary • Problem. be narrow and specific • Data.‘data problem’? - Kaggle or augment • Framework. Keras gives you simple & capable • Deployment. Cloud container (+hadoop/spark) • Algorithm. Deep Learning as ‘ensemble’ component, not single magic trick
- 28. @venuv62 venu.vasudevan@nextio.co iotslam code at : github.com/venuv