Xavier Amatriain on Practical Deep Learning Systems - November 2019

Xavier Amatriain on Practical Deep Learning Systems - November 2019

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Machine learning infrastructure

40 of 44

40 of 44

Machine learning infrastructure

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Xavier Amatriain on Practical Deep Learning Systems - November 2019

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  1. 1 Introduction
  2. 2 Xaviers background
  3. 3 What is Qi
  4. 4 Publications
  5. 5 Lessons Learned
  6. 6 Question
  7. 7 Netflix Price
  8. 8 Meta Metadata
  9. 9 Unreasonable Effectiveness
  10. 10 Netflix example
  11. 11 Data
  12. 12 Transfer Learning
  13. 13 Fine Tuning
  14. 14 Simple Models
  15. 15 Occams Razor
  16. 16 More connections to deep learning
  17. 17 Recommended papers
  18. 18 Real life example
  19. 19 Complex models
  20. 20 Avoid this trap
  21. 21 Feature engineering
  22. 22 Reusable features
  23. 23 Examples
  24. 24 Architecture Engineering
  25. 25 Supervised vs Supervised
  26. 26 Models in Deep Learning
  27. 27 Self Supervision
  28. 28 Insample
  29. 29 Netflix Prize
  30. 30 Deep Models
  31. 31 Data Bias
  32. 32 Bias
  33. 33 Fairness
  34. 34 Models in Production
  35. 35 Models in Other Domains
  36. 36 Evaluation Approach
  37. 37 Metrics
  38. 38 Systems frameworks
  39. 39 Systems and frameworks
  40. 40 Machine learning infrastructure
  41. 41 Machine learning beyond deep learning
  42. 42 Deep learning vs linear models
  43. 43 Conclusions
  44. 44 Questions

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