Machine learning: How it can help your business - Microsoft Future Decoded - Budapest, March 2018

Ce8e94cc306ba164175f693fb01aa8b0?s=47 szilard
March 21, 2018
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Machine learning: How it can help your business - Microsoft Future Decoded - Budapest, March 2018

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szilard

March 21, 2018
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  1. Machine Learning: How It Can Help Your Business Szilárd Pafka,

    PhD Chief Scientist, Epoch (USA) Microsoft Future Decoded, Budapest March 2018
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  3. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
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  5. Source: Andrew Ng

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  7. y = f(x) “Learn” f from data Source: Hastie etal,

    ESL 2ed
  8. Machine Learning linear/logistic regression decision trees neural networks support vector

    machines random forests gradient boosting deep learning neural networks
  9. Machine Learning linear/logistic regression (early 1900s/60s) decision trees (60s/80s) neural

    networks (60s/80s) support vector machines (90s) random forests (90s) gradient boosting (90s) deep learning neural networks (2000s)
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  11. data mining Source: Szilard Pafka

  12. data science Source: Szilard Pafka

  13. data science Source: Szilard Pafka

  14. CRISP-DM, 1999

  15. data $$$

  16. How?

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  19. Source: Andrew Ng

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  23. Source: @iamdevloper (twitter)

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  27. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL
  28. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL better answer: it depends
  29. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all
  30. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all extra accuracy: combine them (ensembles)
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  33. 10x

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  37. ML training: lots of CPU cores lots of RAM limited

    time
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  43. Source: Szilard Pafka

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  45. Random forest GBM GBM + cross validation GBM + hyperparameter

    tuning Logistic regression Neural Nets / Deep Learning Ensembles
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  52. Backup Slides

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  54. 10x

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  58. Source: Szilard Pafka: 10 Pitfalls in Data Science, LA Data

    Science Meetup, February, 2014
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