مطالب قدیمی

سی و هشت مقاله ای که هر داده کاوی باید بخواند

در این پست به شما ۳۸ مقاله منتخبی را معرفی میکنیم که بر روی جنبه های گوناگون و تکنیکی علم داده ها و بیگ دیتا تمرکز کرده اند.

  1. Bigtable: A Distributed Storage System for Structured Data
  2. A Few Useful Things to Know about Machine Learning
  3. Random Forests
  4. A Relational Model of Data for Large Shared Data Banks
  5. Map-Reduce for Machine Learning on Multicore
  6. Pasting Small Votes for Classification in Large Databases and On-Line
  7. Recommendations Item-to-Item Collaborative Filtering
  8. Recursive Deep Models for Semantic Compositionality Over a Sentimen…
  9. Spanner: Google’s Globally-Distributed Database
  10. Megastore: Providing Scalable, Highly Available Storage for Interac…
  11. F1: A Distributed SQL Database That Scales
  12. APACHE DRILL: Interactive Ad-Hoc Analysis at Scale
  13. A New Approach to Linear Filtering and Prediction Problems
  14. Top 10 algorithms on Data mining
  15. The PageRank Citation Ranking: Bringing Order to the Web
  16. MapReduce: Simplified Data Processing on Large Clusters
  17. The Google File System
  18. Amazon’s Dynamo
  19. How to detect spurious correlations, and how to find the …
  20. Automated Data Science: Confidence Intervals
  21. ۱۶ analytic disciplines compared to data science
  22. From the trenches: 360-degree data science
  23. ۱۰ types of regressions. Which one to use?
  24. Practical illustration of Map-Reduce (Hadoop-style), on real data
  25. Jackknife logistic and linear regression for clustering and predict…
  26. A synthetic variance designed for Hadoop and big data
  27. Fast Combinatorial Feature Selection with New Definition of Predict…
  28. Internet topology mapping
  29. ۱۱ Features any database, SQL or NoSQL, should have
  30. ۱۰ Features all Dashboards Should Have
  31. Clustering idea for very large datasets
  32. Hidden decision trees revisited
  33. Correlation and R-Squared for Big Data
  34. What Map Reduce can’t do
  35. Excel for Big Data
  36. Fast clustering algorithms for massive datasets
  37. The curse of big data
  38. Interesting Data Science Application: Steganography
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