[CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning

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CourseClub  Oreilly  Privacy  Preserving  Machine  Learning  
관련 링크
  1. Springer.Privacy-Preserving Machine Learning for Speech Processing.2014.pea 2.78MB
  2. Chang J. Privacy-Preserving Machine Learning 2023 26.41MB
  3. Chang J.M., Zhuang D., Samaraweera D. - Privacy-Preserving Machine Learning - 2023.pdf 26.41MB
  4. [CourseClub.Me] Pluralsight - Building Machine Learning Solutions With Java - Learning Paths 1.56GB
  5. [CourseClub.NET] Coursera - Applied Machine Learning in Python 881.06MB
  6. [CourseClub.ME] AppliedAICourse - Applied Machine Learning Course 25.37GB
  7. [CourseClub.ME] Pluralsight - Creating Machine Learning Models 390.87MB
  8. [CourseClub ME] AppliedAICourse - Applied Machine Learning Course - Downloader.exe 782.95KB
  9. [CourseClub.Me] Coursera – IBM Machine Learning Professional Certificate 1.58GB
  10. OReilly.Hands-On.Machine.Learning.with.Scikit-Learn.and.TensorFlow.1491962291 21.66MB
파일 리스트
  1. 001. Part 1. Basics of privacy-preserving machine learning with differential privacy.mp4 2.34MB
  2. 002. Chapter 1. Privacy considerations in machine learning.mp4 12.17MB
  3. 003. Chapter 1. The threat of learning beyond the intended purpose.mp4 15.55MB
  4. 004. Chapter 1. Threats and attacks for ML systems.mp4 34.41MB
  5. 005. Chapter 1. Securing privacy while learning from data Privacy-preserving machine learning.mp4 28.85MB
  6. 006. Chapter 1. How is this book structured.mp4 6.44MB
  7. 007. Chapter 1. Summary.mp4 3.81MB
  8. 008. Chapter 2. Differential privacy for machine learning.mp4 60.01MB
  9. 009. Chapter 2. Mechanisms of differential privacy.mp4 52.83MB
  10. 010. Chapter 2. Properties of differential privacy.mp4 47.45MB
  11. 011. Chapter 2. Summary.mp4 5.10MB
  12. 012. Chapter 3. Advanced concepts of differential privacy for machine learning.mp4 19.60MB
  13. 013. Chapter 3. Differentially private supervised learning algorithms.mp4 47.46MB
  14. 014. Chapter 3. Differentially private unsupervised learning algorithms.mp4 17.24MB
  15. 015. Chapter 3. Case study Differentially private principal component analysis.mp4 64.12MB
  16. 016. Chapter 3. Summary.mp4 4.54MB
  17. 017. Part 2. Local differential privacy and synthetic data generation.mp4 1.14MB
  18. 018. Chapter 4. Local differential privacy for machine learning.mp4 48.89MB
  19. 019. Chapter 4. The mechanisms of local differential privacy.mp4 45.43MB
  20. 020. Chapter 4. Summary.mp4 3.61MB
  21. 021. Chapter 5. Advanced LDP mechanisms for machine learning.mp4 3.84MB
  22. 022. Chapter 5. Advanced LDP mechanisms.mp4 25.93MB
  23. 023. Chapter 5. A case study implementing LDP naive Bayes classification.mp4 53.74MB
  24. 024. Chapter 5. Summary.mp4 2.49MB
  25. 025. Chapter 6. Privacy-preserving synthetic data generation.mp4 18.00MB
  26. 026. Chapter 6. Assuring privacy via data anonymization.mp4 15.08MB
  27. 027. Chapter 6. DP for privacy-preserving synthetic data generation.mp4 28.43MB
  28. 028. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation.mp4 44.93MB
  29. 029. Chapter 6. Summary.mp4 2.83MB
  30. 030. Part 3. Building privacy-assured machine learning applications.mp4 1.67MB
  31. 031. Chapter 7. Privacy-preserving data mining techniques.mp4 9.68MB
  32. 032. Chapter 7. Privacy protection in data processing and mining.mp4 8.09MB
  33. 033. Chapter 7.3 Protecting privacy by modifying the input.mp4 4.36MB
  34. 034. Chapter 7. Protecting privacy when publishing data.mp4 48.96MB
  35. 035. Chapter 7. Summary.mp4 2.27MB
  36. 036. Chapter 8. Privacy-preserving data management and operations.mp4 4.52MB
  37. 037. Chapter 8. Privacy protection beyond k-anonymity.mp4 29.68MB
  38. 038. Chapter 8. Protecting privacy by modifying the data mining output.mp4 13.89MB
  39. 039. Chapter 8. Privacy protection in data management systems.mp4 80.56MB
  40. 040. Chapter 8. Summary.mp4 3.63MB
  41. 041. Chapter 9. Compressive privacy for machine learning.mp4 14.20MB
  42. 042. Chapter 9. The mechanisms of compressive privacy.mp4 15.76MB
  43. 043. Chapter 9. Using compressive privacy for ML applications.mp4 36.40MB
  44. 044. Chapter 9. Case study Privacy-preserving PCA and DCA on horizontally partitioned data.mp4 103.76MB
  45. 045. Chapter 9. Summary.mp4 3.38MB
  46. 046. Chapter 10. Putting it all together Designing a privacy-enhanced platform (DataHub).mp4 19.64MB
  47. 047. Chapter 10. Understanding the research collaboration workspace.mp4 27.07MB
  48. 048. Chapter 10. Integrating privacy and security technologies into DataHub.mp4 31.84MB
  49. 049. Chapter 10. Summary.mp4 3.42MB
  50. [CourseClub.Me].url 66B