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Machine Learning In JavaScript
- 1. Machine Learning in JavaScript
- 3. Smart?
- 4. Smart
- 5. Curious
- 6. Two years •Secret sauce at SpotterRF •StackOverflow.com •Random side projects
- 7. What is it?
- 9. Math
- 10. Math
- 11. Math
- 12. Math
- 15. “Making computers modify or adapt their actions so these actions get more accurate.” - Stephen Marsland
- 16. Teaching computers to recognize patterns
- 18. Why do you care?
- 20. An avalanche of data
- 21. “The purpose of computing is insight, not numbers.” - Richard Hamming
- 22. Why JavaScript?
- 23. Atwood’s Law
- 24. Atwood’s Law
- 26. Naive Bayes and puppies
- 27. Spam Filtering? Illustrious sir/madame, I have recently acquired a bounteous cache of 7 million semicolons. They can all be yours if you send a money order for 30 semicolons. Most graciously, A spammer
- 28. An idea • Count up all words in all spam • Count up all words in not-spam • Compare counts to words in new documents
- 29. Class Word 1 Word 2 Word 3 Word 4
- 30. Spam / Not Spam Word 1 Word 2 Word 3 Word 4
- 31. Spam / Not Spam Word 1 Word 2 Word 3 Word 4 Spam Not 50 45
- 32. Spam / Not Spam Word 1 Word 2 Word 3 Word 4 Spam Not 15 27
- 33. Spam / Not Spam Word 1 Word 2 Word 3 Word 4 Spam Not 33 14
- 34. Spam / Not Spam Word 1 Word 2 Word 3 Word 4 Spam Not 4 55
- 39. Bayes Theorem P(A|B) = (P(B|A)*P(A)) / P(B)
- 40. In English P(class|email) = (P(email|class)*P(class)) / P(word1 and word2 and word3)
- 42. “Spam” or “Not Spam” P(class|email) = (P(email|class)*P(class))/P(email)
- 43. Words in the email P(class|email) = (P(email|class)*P(class))/P(email)
- 44. What we think the probability of spam or not spam is P(class|email) = (P(email|class)*P(class))/P(email)
- 45. P(spam|email) = (P(email|spam)*P(spam))/P(email) P(not spam|email) = (P(email|not spam)*P(not spam))/P(email) Pick the largest one
- 46. P(spam|email) = (P(email|spam)*P(spam))/P(email) P(not spam|email) = (P(email|not spam)*P(not spam))/P(email) These are the same
- 47. P(spam|email) = (P(email|spam)*P(spam)) P(not spam|email) = (P(email|not spam)* P(not spam)) Assume these are the same
- 48. P(spam|email) = P(email|spam) P(not spam|email) = P(email|not spam)
- 49. P(words|spam) = P(word1|spam) * P(word2|spam) . . . * P(word_n|spam)
- 54. In The Wild
- 55. Everyone Hates Hacker News
- 63. Crap
- 64. Crap Crap
- 65. Crap Crap Crap
- 66. Crap Crap Crap Crap
- 67. Crap Crap Crap Crap Crap
- 68. Crap Crap Crap Crap Crap Crap
- 69. Crap Crap Crap Crap Crap Crap Crap
- 70. Crap Crap Crap Crap Crap Crap Crap Crap
- 71. Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 72. Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 73. Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 74. Mostly Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 75. Yehuda Katz hurt my feelings rails sucks node rules lol
- 76. Yehuda Katz hurt my feelings DRAMA rails sucks node rules lol
- 82. Some Good Stuff
- 83. Automatically Find The Good Stuff
- 85. scraping with jsdom
- 87. An aside
- 88. 90% data prep 10% learning
- 89. naive bayes with credulous
- 90. Like / Dislike Word 1 . . . Word n username hostname
- 91. programmer ui with node
- 92. recommend posts from HN api
- 93. In The Browser
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