{Machine, Deep} Learning for software engineers

An opinionated recommendation for data science: Python environemnt, scikit-learn for Machine Learning and Keras for Deep Learning. At Product Tech Stories Meetup @ Codility

Transcript

1. {Machine, Deep} Learning
   for software engineers
   dr Piotr Migdał
   freelancer / deepsense.io
   Product Tech Stories Meetup @ Codility

   Warsaw, 30 Nov 2016
   

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2. http://xkcd.com/1425/
   

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3. But…
   https://laughingsquid.com/park-or-bird-a-national-park-and-bird-identifying-app-inspired-by-an-xkcd-comic/
   …and now a simple exercise in Deep Learning
   

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4. http://deepsense.io/deep-learning-right-whale-recognition-kaggle/
   

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5. ML and DL progress
   • image recognition, neural style, word analogies,
   per-char translations, playing ATARI games, Go,
   [no idea what’s next]
   • fast-paced (more than my quantum physics PhD):

   6 month ago a breakthrough, now a baseline
   • (no questions about Singularity please!)
   

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6. https://devblogs.nvidia.com/parallelforall/mocha-jl-deep-learning-julia/
   

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7. Challenges
   • data science is both statistics and programming
   • ML algorithms base on randomness and data
   • trying a wide array of options & parameters
   • unavoidable research-production overlap
   

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8. http://www.economist.com/news/business/21695908-
   silicon-valley-fights-talent-universities-struggle-hold-their
   

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9. What I {use, teach}?
   • general Machine Learning:

   scikit-learn (in Python)
   • general Deep Learning:

   Keras (in Python)
   • spaCy+gensim, SparkML, Neptune, …
   

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10. Why Python?
    • de facto standard for ML/DL
    • sane language + new stuff + Jupyter Notebook
    • not R, MATLAB or Julia?

    http://sebastianraschka.com/blog/2015/why-python.html
    • not JavaScript? oh, wait…

    http://cs.stanford.edu/people/karpathy/convnetjs/
    • warning: Python 2.7 is still the default :/
    

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11. Machine Learning
    scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
    

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12. scikit-learn

    http://scikit-learn.org
    • many popular techniques with the same interface
    • fast, reliable
    • good documentation
    • XGBoost has its interface
    • not much for time series (statsmodels, R forecast)
    • or natural language processing (spaCy, gensim)
    

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13. Code example
    

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14. Performance
    of 500 trees
    https://github.com/szilard/benchm-ml
    http://datascience.la/benchmarking-random-forest-implementations/
    

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15. Deep Learning
    

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17. http://arxiv.org/abs/1508.06576
    research paper
    on arXiv
    code
    on GitHub
    online tools
    (e.g. deepart.io)
    Aug ‘15
    Dec ‘15
    Oct ‘15
    movies Apr ‘16
    

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18. Keras
    https://keras.io/
    • Theano or TensorFlow backend
    • abstraction at the right level

    (the rule of least power)
    • a LOT of EASY examples for NEW techniques
    • (yes, we can do a sparse Matrix Factorisation)
    • also for JavaScript, with GPU support :)

    https://github.com/transcranial/keras-js
    

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19. Code example
    

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20. VGG16 in Keras
    

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21. https://twitter.com/fchollet/status/765212287531495424
    DL framework popularity
    

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22. keras2ascii
    https://gist.github.com/stared/8411d4e7e457b0f14f39d700afc8511c
    

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23. other tools
    

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24. spaCy + gensim + pyLDAvis
    http://press.deepsense.io/10012-deepsense-io-brings-big-data-to-the-
    united-nations-office-of-information-and-communications-technology
    

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25. Neptune
    http://neptune.deepsense.io and Try Neptune (private beta)
    and wait a few days… or drop me an email of you want it now :)
    

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26. Where to learn…?
    • scikit-learn:

    http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html
    

    and the Machine Learning section
    • Keras:

    https://gist.github.com/stared/70daf8e0334abf6e7527259e7221f568
    

    and references there
    • everything:

    http://workshops.deepsense.io
    

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27. Thank you!
    http://p.migdal.pl
    pmigdal@gmail.com
    “linear space of words (word2vec vis)”
    “dating for nerds”
    coming soon:
    see: data science stuff + quantum game
    

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