The Machine Learning Intervention

The Machine Learning Intervention

This talk is an attempt to provide the crucial information needed when you start doing Machine Learning work. Johann du Toit (our speaker) had some *aha* moments throughout his failures, and this talk tries to condense those learnings into one set of slides.

While talking and uncovering these topics in a simple to digest way he'll be sprinkling learnings and demos/examples from his recent work.

Johann will cover a few topics, including:

-> What machine learning actually is, and is not; to level the playing the playing field.
-> The different types of models
-> How you would go about training a model with examples to each
-> How to run your awesome model

We hope the takeaway from this talk will be a structured mindset for those now newly approaching machine learning; be they developers/designers or product managers. You should also be able to walk away with the ability to talk ML and know what to expect when being faced with an ML project.

For the experts, you might at least get to know a few new tools :)

Expect real-time demos and some wackiness sprinkled in.

46b656be55256ced0695c9f6af82c7d4?s=128

Johann du Toit

August 10, 2018
Tweet

Transcript

  1. THE MACHINE LEARNING INTERVENTION

  2. Johann du Toit

  3. Google Developer Expert for Cloud since 2014 Google Developers Launchpad

    Mentor
  4. HydraCorp.

  5. None
  6. me@johanndutoit.net Email Website @signedness

  7. This Talk

  8. None
  9. None
  10. None
  11. None
  12. None
  13. THANKS!

  14. None
  15. This Talk Attem pt #2

  16. None
  17. None
  18. None
  19. None
  20. None
  21. ~ 25,000 attendees

  22. ~ 6 buildings

  23. None
  24. None
  25. None
  26. None
  27. None
  28. • Framing the problem • Training a model • Running

    your model The plan
  29. • Framing the problem • Training a model • Running

    your model The plan
  30. • Find a suitable algorithm for your use case •

    Figure out how you are going to express your data 2 steps to framing
  31. • Find a suitable algorithm for your use case •

    Figure out how you are going to express your data 2 steps to framing
  32. What is Machine Learning?

  33. None
  34. None
  35. "People worry that computers will get too smart and take

    over the world, but the real problem is that they're too stupid and they've already taken over the world." Pedro Domingos
  36. None
  37. None
  38. None
  39. None
  40. None
  41. None
  42. Machine Learning AI

  43. Machine learning (ML) is a category of algorithm that allows

    software applications to become more accurate in predicting outcomes without being explicitly programmed. Merriam-Webster
  44. None
  45. None
  46. None
  47. None
  48. Linear Regression Decision Tree Naive Bayes K-Means Random Forest Logistic

    Regression Support Vector Machine (SVM) k- Nearest Neighbours (kNN)
  49. Linear regression Predicting real numbers Classification Divide up into groups

    Reinforcement Learning Learn by doing and failing K-Means Find Patterns Unsupervised Supervised Supervised Supervised
  50. Ensemble

  51. Specific.

  52. • Find a suitable algorithm for your use case •

    Figure out how you are going to express your data 2 steps to framing
  53. "Your app/service is either collecting data or missing opportunities" Me

  54. None
  55. [ 11, 223.44, 5125.5 ]

  56. • Text • Images • Sound Representation

  57. • Text • Images • Sound Representation

  58. [ 11, 223.44, 5125.5 ]

  59. “hello world” != a number …

  60. “cat1.jpg”,”cat”
 “dog1.jpg”,”dog” “cat2.jpg”,”cat”
 “cat3.jpg”,”cat”

  61. One Hot Encoding

  62. “cat1.jpg”,”0”
 “dog1.jpg”,”1” “cat2.jpg”,”0”
 “cat3.jpg”,”0”

  63. • Text • Images • Sound Representation

  64. None
  65. None
  66. None
  67. [ 11, 223.44, 5125.5 ]

  68. • Text • Images • Sound Representation

  69. None
  70. None
  71. “hey”

  72. None
  73. “hey”

  74. [ 11, 223.44, 5125.5 ]

  75. Example

  76. None
  77. None
  78. Weather that night

  79. Weather that night How much pizza was bought Average number

    that pitch Average percentage that pitch
  80. Weather that night How much pizza was bought Average number

    that pitch Average percentage that pitch Average RSVP
  81. Weather that night How much pizza was bought Average number

    that pitch Average percentage that pitch Average RSVP Time of Meetup Location of Meetup
  82. Weather that night How much pizza was bought Average number

    that pitch Average percentage that pitch Average RSVP Time of Meetup Location of Meetup
  83. None
  84. How much data do I need ?

  85. depends.

  86. “You can always hire a better machine learning engineer, but

    they won’t be able to fix having no data” Me ;)
  87. • Framing the problem • Training a model • Running

    your model The plan
  88. • Datasets • Tools • Running Training

  89. • Datasets • Tools • Running Training

  90. Training Set Test Set Don’t touch this until the very

    end The actual training data used
  91. None
  92. Linear Regression Decision Tree Naive Bayes K-Means Random Forest Logistic

    Regression Support Vector Machine (SVM) k- Nearest Neighbours (kNN)
  93. Ensemble

  94. Training Set Test Set Don’t touch this until the very

    end The actual training data used Validation Set For cross validation and model selection
  95. pre-process

  96. • Datasets • Tools • Running Training

  97. None
  98. None
  99. None
  100. Vs

  101. Single Process

  102. None
  103. None
  104. None
  105. None
  106. None
  107. Auto ML / BigQuery-ML

  108. None
  109. None
  110. None
  111. None
  112. None
  113. None
  114. None
  115. None
  116. None
  117. None
  118. • Datasets • Tools • Running Training

  119. Local Demo

  120. None
  121. To the terminal matey!

  122. AutoML

  123. None
  124. None
  125. None
  126. None
  127. None
  128. None
  129. None
  130. Considerations

  131. None
  132. None
  133. Train Regularly

  134. • Framing the problem • Training a model • Running

    your model The plan
  135. • Hosted • Local Options

  136. • Hosted • Local Options

  137. None
  138. Demo

  139. None
  140. • Hosted • Local Options

  141. None
  142. Demo

  143. in closing

  144. None
  145. A model's blind spots reflect the judgments and priorities of

    its creators Cathy O'Neil
  146. Thanks!