Intro to Machine Learning for Android developers

2696500a913e29a26f38115f8ea56f71?s=47 Adrien Couque
November 23, 2016

Intro to Machine Learning for Android developers

2696500a913e29a26f38115f8ea56f71?s=128

Adrien Couque

November 23, 2016
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  1. Intro to ML for Android developers November 2016

  2. 2010s : emergence of Machine Learning 1999 : neural networks

    used for handwritten digits recognition
  3. 2010s : emergence of Machine Learning 1999 : neural networks

    used for handwritten digits recognition 2012 : @Google, 16k processors, 10M Youtube videos, 1 week
  4. 2010s : emergence of Machine Learning 1999 : neural networks

    used for handwritten digits recognition 2012 : @Google, 16k processors, 10M Youtube videos, 1 week Able to find cats
  5. 2010s : emergence of Machine Learning • Self-driving cars •

    Human interaction : ◦ Handwriting ◦ Speech ◦ Natural language • OCR • Image recognition • Information retrieval • Artificial personal assistants • Recommendations systems • Drones • Game playing • ...
  6. Machine Learning

  7. Explaining Machine Learning Machine learning is the idea that there

    are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.
  8. Explaining Machine Learning Different categories : • Supervised learning ◦

    continous answer : regression ex : estimate a price, a volume, ... ◦ discrete answer : classification ex : spam detection, cancerous tumors • Unsupervised learning • Reinforced learning continuous environment and constant feedback, learns by itself
  9. Unsupervised learning

  10. Linear regression

  11. Intuitions from linear regression • algorithm is generic, results depends

    on data • system is both the algorithm and the data • starts with a hypothesis about how we can represent the data (for linear regression : a straight line) • only as good as your data • can deal poorly with outliers • lots of calculation to learn, but very fast to apply (can run on mobile)
  12. Tensorflow Playground

  13. Deep learning

  14. State of the art : Google

  15. None
  16. SyntaxNet and Parsey McParseface

  17. TensorFlow ML framework Opensourced in 2016 New standard

  18. Implications for mobile

  19. None
  20. Interactions between mobile and ML • Mobile as a source

    of data
  21. Assistant API d.android.com/training/articles/assistant.html

  22. Interactions between mobile and ML • Mobile as a source

    of data • Mobile as UI ◦ still need UI to access services (even AI services) ◦ bots are not for everyone : alternative UI for “power users” • ML to enrich mobile apps (offline ML)
  23. ML offline

  24. Applications of NLP at Quora - automatic grammar correction -

    question quality - duplicate question detection - related question suggestion - topic biography quality (= qualifications of writer) - topic labeler (from “science” to narrow topics like “tennis Courts in Mountain View”) - search - answer summaries - automatic answers wiki - hate speech/harassment detection - spam detection - question edit quality
  25. Questions? November 2016

  26. None
  27. Overfitting Need to split the data into : - training

    set (60%) - cross-validation set (20%) - evaluation set (20%)
  28. SyntaxNet and Parsey McParseface Parsey McParseface can correctly read: •

    The old man the boat. • While the man hunted the deer ran into the woods. • While Anna dressed the baby played in the crib. • Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. It makes mistakes on: • I convinced her children are noisy. • The coach smiled at the player tossed the frisbee. • The cotton clothes are made up of grows in Mississippi. • James while John had had had had had had had had had had had a better effect on the teacher