Boston Data Mining - A High-Performance Implementation of Bayesian Clustering

Boston Data Mining - A High-Performance Implementation of Bayesian Clustering

Open Data Science via YouTube Direct link

Overview

4 of 31

4 of 31

Overview

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Boston Data Mining - A High-Performance Implementation of Bayesian Clustering

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 About us
  3. 3 Why this topic
  4. 4 Overview
  5. 5 What is crp
  6. 6 Clustering tree
  7. 7 No categories
  8. 8 Why not Kmeans
  9. 9 General Models
  10. 10 Mixture Model
  11. 11 Chinese Restaurant
  12. 12 Dinosaur Diamonds
  13. 13 Dirichlet Compound
  14. 14 Plate Diagram
  15. 15 Beta
  16. 16 Standard Trick
  17. 17 Why Markov
  18. 18 Priors
  19. 19 How to do it
  20. 20 Why it exploded
  21. 21 Reseeding
  22. 22 Profiler
  23. 23 Functional Programming
  24. 24 Fast Care
  25. 25 Immutable
  26. 26 C Function
  27. 27 Super Abstraction
  28. 28 Who are Bayesian Data Scientists
  29. 29 Performance Expectations
  30. 30 Performance Issues
  31. 31 Code Base

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.