TinyML Talks - On-Device Model Fine-Tuning for Industrial Anomaly Detection Applications

TinyML Talks - On-Device Model Fine-Tuning for Industrial Anomaly Detection Applications

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Statistical methods

35 of 40

35 of 40

Statistical methods

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TinyML Talks - On-Device Model Fine-Tuning for Industrial Anomaly Detection Applications

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  1. 1 Introduction
  2. 2 Strategic Partners
  3. 3 TinyML Summit 2022
  4. 4 Tiny Multiplayer Series
  5. 5 Meetup Groups
  6. 6 Reminders
  7. 7 Next talk
  8. 8 Main event
  9. 9 Welcome
  10. 10 Company introduction
  11. 11 Partners
  12. 12 Maintenance types
  13. 13 Possible approaches
  14. 14 Project overview
  15. 15 Infineon extensive maintenance evaluation kit
  16. 16 Cloud architecture reference cloud architectures
  17. 17 Robust random cut
  18. 18 Pros and cons
  19. 19 Questions
  20. 20 Neural Networks
  21. 21 Demo
  22. 22 Question
  23. 23 Demonstration
  24. 24 Catastrophic forgetting
  25. 25 Our approach
  26. 26 Next steps
  27. 27 QA
  28. 28 Green Grass
  29. 29 Constantine
  30. 30 Response requirements
  31. 31 Random cut forest
  32. 32 Power support
  33. 33 Signal input
  34. 34 Anomalies
  35. 35 Statistical methods
  36. 36 Deployment
  37. 37 Custom layers
  38. 38 Q A
  39. 39 Thank you
  40. 40 Outro

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