Paradoxes and theorems every developer should know

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1. 1 Joshua Thijssen jaytaph <?php namespace
2. 2 Joshua Thijssen Consultant and trainer @ NoxLogic Founder of TechAnalyze.io Symfony Rainbow Books author Mastering the SPL author Blog: http://adayinthelifeof.nl Email: jthijssen@noxlogic.nl Twitter: @jaytaph Tech nalyze WWW.TECHANALYZE.IO
3. 3 https://dutchtechrecruitment.nl/ Text
4. Disclaimer: I'm not a (mad) scientist nor a mathematician. 4
5. German Tank Problem 5
6. 6
7. 6 15
8. 7
9. 7 53 72 8 15
10. 8 k = number of elements m = largest number
11. 72 + (72 / 4) - 1 = 89 9
12. 10 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem
13. 10 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem 122
14. 10 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem 122 271
15. 10 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem 122 271 342
16. 11
17. 11 ➡ Data leakage.
18. 11 ➡ Data leakage. ➡ User-id's, invoice-id's, etc
19. 11 ➡ Data leakage. ➡ User-id's, invoice-id's, etc ➡ Used to approximate the number of iPhones sold in 2008.
20. 11 ➡ Data leakage. ➡ User-id's, invoice-id's, etc ➡ Used to approximate the number of iPhones sold in 2008. ➡ Calculate approximations of datasets with (incomplete) information.
21. 12
22. ➡ Avoid (semi) sequential data to be leaked. ➡ Adding randomness and offsets will NOT solve the issue. ➡ Use UUIDs (better: timebased short IDs, you don't need UUIDs) 13
23. 14 Collecting (big) data is easy Analyzing big data is the hard part.
24. Confirmation Bias 15
25. 2 4 6 16 Z={…,−2,−1,0,1,2,…}
26. 21% 17
27. 18 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
28. < 10% 19
29. 20 coke beer 35 17 If you drink beer then you must be 18 yrs or older.
30. 20 coke beer 35 17 If you drink beer then you must be 18 yrs or older.
31. 20 coke beer 35 17 If you drink beer then you must be 18 yrs or older.
32. Cognitive Adaption for social exchange 21
33. hint: Try and place your "technical problem" in a more social context. 22
34. BDD 23
35. 24 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
36. 24 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
37. 24 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
38. TESTING 25
39. 26 ➡ Step 1: Write code ➡ Step 2: Write tests ➡ Step 3: Profit
40. public function isLeapYeap($year) { return ($year % 4 == 0); } 27 https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing testIs1996ALeapYeap(); testIs2000ALeapYeap(); testIs2004ALeapYeap(); testIs2008ALeapYeap(); testIs2012ALeapYeap(); testIs1997NotALeapYear(); testIs1998NotALeapYear(); testIs2001NotALeapYear(); testIs2013NotALeapYear();
41. public function isLeapYeap($year) { return ($year % 4 == 0); } 27 https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing testIs1996ALeapYeap(); testIs2000ALeapYeap(); testIs2004ALeapYeap(); testIs2008ALeapYeap(); testIs2012ALeapYeap(); testIs1997NotALeapYear(); testIs1998NotALeapYear(); testIs2001NotALeapYear(); testIs2013NotALeapYear();
42. public function isLeapYeap($year) { return ($year % 4 == 0); } 28 https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing
43. 29 ➡ Tests where written based on actual code. ➡ Tests where written to CONFIRM actual code, not to DISPROVE actual code!
44. 30 TDD
45. 31 ➡ Step 1: Write tests ➡ Step 2: Write code ➡ Step 3: Profit, as less prone to confirmation bias (as there is nothing to bias!)
46. Birthday paradox 32
47. Question: 33 > 50% chance 4 march 18 september 5 december 25 juli 2 februari 9 october
48. 23 people 34
49. 366 persons = 100% 35
50. Collisions occur more often than you realize 36
51. Hash collisions 37
52. 16 bits means 300 values before >50% collision probability 38
53. Watch out for: 39 ➡ Too small hashes. ➡ Unique data. ➡ Your data might be less "protected" as you might think.
54. Heisenberg uncertainty principle 40
55. It's not about star trek (heisenberg compensators) 41
56. nor crystal meth 42
57. 43 x position p momentum (mass x velocity) ħ 0.0000000000000000000000000000000001054571800 (1.054571800E-34)
58. The more precise you know one property, the less you know the other. 44
59. This is NOT about observing! 45
60. Observer effect 46 heisenbug
61. It's about trade-offs 47
62. Benford's law 48
63. Numbers beginning with 1 are more common than numbers beginning with 9. 49
64. Default behavior for natural numbers. 50
65. 51
66. find . -name \*.php -exec wc -l {} \; | sort | cut -b 1 | uniq -c 52
67. find . -name \*.php -exec wc -l {} \; | sort | cut -b 1 | uniq -c 52 1073 1 886 2 636 3 372 4 352 5 350 6 307 7 247 8 222 9
68. 53
69. Bayesian filtering 54
70. What's the probability of an event, based on conditions that might be related to the event. 55
71. What is the chance that a message is spam when it contains certain words? 56
72. 57 P(A|B) P(A) P(B) P(B|A) Probability event A, if event B (conditional) Probability event A Probability event B Probability event B, if event A
73. 58 ➡ Figure out the probability a {mail, tweet, comment, review} is {spam, negative} etc.
74. ➡ 10 out of 50 comments are "negative". ➡ 25 out of 50 comments uses the word "horrible". ➡ 8 comments with the word "horrible" are marked as "negative". 59
75. 60 negative "horrible" 10 comments 25 comments 8 comments
76. 61
77. 62 ➡ More words? ➡ Complex algorithm, ➡ but, we can assume that words are not independent from eachother ➡ Naive Bayes approach
78. 63
79. 64 We must know beforehand which comments are negative?
80. TRAINING SET 65
81. 66 "Your product is horrible and does not work properly. Also, you suck." "I had a horrible experience with another product. But yours really worked well. Thank you!" Negative: Positive:
82. 67 ➡ You might want to filter stop-words first. ➡ You might want to make sure negatives are handled property "not great" => negative. ➡ Bonus points if you can spot sarcasm.
83. ➡ Collaborative filtering (mahout): ➡ If user likes product A, B and C, what is the chance that they like product D? 68
84. 69 Mess up your (training) data, and nothing can save you (except a training set reboot)
85. 70 ➡ 30% change of acceptance for CFP ➡ 5 CFP's Binomial probability
86. 70 ➡ 30% change of acceptance for CFP ➡ 5 CFP's 1 - (0.7 * 0.7 * 0.7 * 0.7 * 0.7) = 1 - 0.168 = 0.832 83% on getting selected at least once! Binomial probability
87. http://farm1.static.flickr.com/73/163450213_18478d3aa6_d.jpg 71
88. 72 Find me on twitter: @jaytaph Find me for development and training: www.noxlogic.nl / www.techademy.nl Find me on email: jthijssen@noxlogic.nl Find me for blogs: www.adayinthelifeof.nl