Algorithms and Data Structures for Massive Datasets

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Algorithms  and  Data  Structures  for  Massive  Datasets  
관련 링크
  1. [ TutGee.com ] Algorithms and Data Structures for Massive Datasets (MEAP v3).zip 19.18MB
  2. [ DevCourseWeb.com ] Algorithms and Data Structures for OOP With C# 39.47MB
  3. Edet T. Algorithms and Data Structures for OOP With C# 2023 15.71MB
  4. Medjedovic D. Algorithms and Data Structures for...Datasets 2021 20.40MB
  5. [FreeCourseSite.com] Udemy - JavaScript Algorithms and Data Structures Masterclass 3.99GB
  6. [GigaCourse.Com] Udemy - JavaScript Algorithms and Data Structures Masterclass 3.99GB
  7. JavaScript Algorithms and Data Structures Masterclass 4.09GB
  8. Algorithms and Data Structures - Niklaus Wirth.pdf 5.87MB
  9. [ FreeCourseWeb.com ] Algorithms and Data Structures in C + + (2021) - Learn Algorithms and Data structures in C + + , get ready for enginnering interview.zip 1.37MB
  10. algorithms-and-data-structures-in-cplusplus---alan-parker-amp-algorithms-and-data-structures-the-sci - Downloader.exe 782.95KB
파일 리스트
  1. 001. Chapter 1. Introduction.mp4 26.79MB
  2. 002. Chapter 1. An example How to solve it.mp4 16.57MB
  3. 003. Chapter 1. How to solve it, take two A book walkthrough.mp4 21.42MB
  4. 004. Chapter 1. The structure of this book.mp4 25.15MB
  5. 005. Chapter 1. Latency vs. bandwidth.mp4 21.18MB
  6. 006. Part 1. Hash-based sketches.mp4 6.34MB
  7. 007. Chapter 2. Review of hash tables and modern hashing.mp4 35.70MB
  8. 008. Chapter 2. Usage scenarios in modern systems.mp4 36.61MB
  9. 009. Chapter 2. Collision resolution Theory vs. practice.mp4 31.13MB
  10. 010. Chapter 2. Hash tables for distributed systems Consistent hashing.mp4 20.50MB
  11. 011. Chapter 2. Adding a new noderesource.mp4 26.89MB
  12. 012. Chapter 3. Approximate membership Bloom and quotient filters.mp4 30.55MB
  13. 013. Chapter 3. A simple implementation.mp4 24.20MB
  14. 014. Chapter 3. A bit of theory.mp4 17.28MB
  15. 015. Chapter 3. Bloom filter adaptations and alternatives.mp4 23.54MB
  16. 016. Chapter 3. Understanding metadata bits.mp4 23.32MB
  17. 017. Chapter 3. Python code for lookup.mp4 20.93MB
  18. 018. Chapter 3. Comparison between Bloom filters and quotient filters.mp4 19.65MB
  19. 019. Chapter 4. Frequency estimation and count-min sketch.mp4 31.50MB
  20. 020. Chapter 4. Update.mp4 34.40MB
  21. 021. Chapter 4. Error vs. space in count-min sketch.mp4 19.24MB
  22. 022. Chapter 4. Range queries with count-min sketch.mp4 35.26MB
  23. 023. Chapter 5. Cardinality estimation and HyperLogLog.mp4 23.14MB
  24. 024. Chapter 5. HyperLogLog incremental design.mp4 28.71MB
  25. 025. Chapter 5. LogLog.mp4 24.68MB
  26. 026. Chapter 5. Use case Catching worms with HLL.mp4 18.39MB
  27. 027. Chapter 5. The effect of the number of buckets (m).mp4 25.23MB
  28. 028. Part 2. Real-time analytics.mp4 5.47MB
  29. 029. Chapter 6. Streaming data Bringing everything together.mp4 44.66MB
  30. 030. Chapter 6. Streaming data system A meta example.mp4 23.54MB
  31. 031. Chapter 6. Deduplication.mp4 28.03MB
  32. 032. Chapter 6. Practical constraints and concepts in data streams.mp4 28.19MB
  33. 033. Chapter 6. Math bit Sampling and estimation.mp4 24.69MB
  34. 034. Chapter 6. Biased sampling strategy.mp4 32.39MB
  35. 035. Chapter 7. Sampling from data streams.mp4 42.81MB
  36. 036. Chapter 7. Reservoir sampling.mp4 30.85MB
  37. 037. Chapter 7. Biased reservoir sampling.mp4 37.65MB
  38. 038. Chapter 7. Sampling from a sliding window.mp4 38.79MB
  39. 039. Chapter 7. Priority sampling.mp4 24.02MB
  40. 040. Chapter 7. Sampling algorithms comparison.mp4 27.34MB
  41. 041. Chapter 8. Approximate quantiles on data streams.mp4 30.16MB
  42. 042. Chapter 8. Approximate quantiles.mp4 22.21MB
  43. 043. Chapter 8. T-digest How it works.mp4 18.48MB
  44. 044. Chapter 8. Scale functions.mp4 22.26MB
  45. 045. Chapter 8. Merging t-digests.mp4 29.34MB
  46. 046. Chapter 8. Q-digest.mp4 30.45MB
  47. 047. Chapter 8. Quantile queries with q-digests.mp4 40.41MB
  48. 048. Part 3. Data structures for databases and external memory algorithms.mp4 5.86MB
  49. 049. Chapter 9. Introducing the external memory model.mp4 36.60MB
  50. 050. Chapter 9. Example 1 Finding a minimum.mp4 20.10MB
  51. 051. Chapter 9. Example 2 Binary search.mp4 20.58MB
  52. 052. Chapter 9. Optimal searching.mp4 30.43MB
  53. 053. Chapter 9. External memory model Simple or simplistic.mp4 16.45MB
  54. 054. Chapter 10. Data structures for databases B-trees, Bε-trees, and LSM-trees.mp4 21.03MB
  55. 055. Chapter 10. Data structures in this chapter.mp4 26.33MB
  56. 056. Chapter 10. B-tree balancing.mp4 20.34MB
  57. 057. Chapter 10. Delete.mp4 29.05MB
  58. 058. Chapter 10. Math bit Why are B-tree lookups optimal in external memory.mp4 22.46MB
  59. 059. Chapter 10. Bε-trees.mp4 23.71MB
  60. 060. Chapter 10. Lookups.mp4 31.33MB
  61. 061. Chapter 10. Log-structured merge-trees (LSM-trees).mp4 30.60MB
  62. 062. Chapter 10. LSM-tree cost analysis.mp4 19.25MB
  63. 063. Chapter 11. External memory sorting.mp4 21.47MB
  64. 064. Chapter 11. Challenges of sorting in external memory An example.mp4 17.03MB
  65. 065. Chapter 11. External memory merge-sort (MB-way merge-sort).mp4 23.89MB
  66. 066. Chapter 11. What about external quick-sort.mp4 22.11MB
  67. 067. Chapter 11. Finding good enough pivots.mp4 29.20MB
  68. Algorithms-and-Data-Structures-for-Massive-Datasets-Video-Edition.jpg 39.44KB