Machine Learning in JavaScript

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Jamison Dance

Machine learning in JavaScript

Technology News & Politics

Jamison Dance
i.tv
@jergason
http://jamisondance.com

Two years
•Secret sauce at SpotterRF

•StackOverﬂow.com
•Random side projects

“Making computers
modify or adapt their
actions so these actions
get more accurate.” -
Stephen Marsland

Teaching computers to
recognize patterns

“The purpose of
computing is insight, not
numbers.” - Richard
Hamming

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

An idea
• Count up all words in all spam
• Count up all words in not-spam
• Compare counts to words in new
documents

Spam / Not Spam

Word 1 Word 2 Word 3 Word 4

Spam / Not Spam

Word 1 Word 2 Word 3 Word 4

Spam Not
50 45

Spam / Not Spam

Word 1 Word 2 Word 3 Word 4

Spam Not
15 27

Spam / Not Spam

Word 1 Word 2 Word 3 Word 4

Spam Not
33 14

Spam / Not Spam

Word 1 Word 2 Word 3 Word 4

Spam Not
4 55

Bayes Theorem
P(A|B) = (P(B|A)*P(A)) / P(B)

In English
P(class|email) = (P(email|class)*P(class)) /
P(word1 and word2 and word3)

P(class|email) = (P(email|class)*P(class))/P(email)

“Spam” or “Not Spam”

P(class|email) = (P(email|class)*P(class))/P(email)

Words in the email

P(class|email) = (P(email|class)*P(class))/P(email)

What we think the
probability of spam or
not spam is

P(class|email) = (P(email|class)*P(class))/P(email)

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

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

P(spam|email) = (P(email|spam)*P(spam))

P(not spam|email) = (P(email|not spam)*
P(not spam))

Assume these are the
same

P(spam|email) = P(email|spam)

P(not spam|email) = P(email|not spam)

P(words|spam) = P(word1|spam) *
P(word2|spam) . . . * P(word_n|spam)

Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap

Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap

Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap
Crap

Mostly
Crap
Crap
Crap
Crap
Crap

Crap
Crap
Crap
Crap
Crap
Crap
Crap

Yehuda Katz
hurt my feelings

rails sucks
node rules
lol

Yehuda Katz
hurt my feelings

DRAMA
rails sucks
node rules
lol

Like / Dislike

Word 1 . . . Word n username hostname

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From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world. With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively. The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage. Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience. See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers See the original article on Forbes here: http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b

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Machine Learning in JavaScript

1. Machine Learning in JavaScript

2. Jamison Dance i.tv @jergason http://jamisondance.com

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

13.

14.

15. “Making computers modify or adapt their actions so these actions get more accurate.” - Stephen Marsland

16. Teaching computers to recognize patterns

17.

18. Why do you care?

19.

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

25.

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

35. Emergency! Puppies!

36.

37.

38.

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)

41. P(class|email) = (P(email|class)*P(class))/P(email)

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)

50. Emergency! Kittens!

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54. In The Wild

55. Everyone Hates Hacker News

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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

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82. Some Good Stuff

83. Automatically Find The Good Stuff

84. Step 1 Gather Data

85. scraping with jsdom

86. storage with mongoose

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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