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Machine learning for mortal developers - Fellyph Cintra

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Machine Learning is one of the most trendy things in IT world right now, a bunch of new services pop-up every single moment, large companies have started to implement different features that utilise Machine Learning(ML). But.... how mortal developers and small and medium business can effectively use machine learn to improve their solutions?

In this talk, we are going to discuss the basic concepts around ML and using high-level libraries in JavaScript to implement on our project. Described as a friendly Machine Learning library ml5.js will be our entry door to the Machine Learning world.

To wrap up the introduction journey, we will see how we can use ml5.js in a real project. As I mentioned before ml5.js is a Javascript library - the talk is focused on a front-end stack but is open for people who want to learn more about the basic concepts behind machine learning.

Let's get coding!!!

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Machine learning for mortal developers - Fellyph Cintra

  1. 1. Machine learning for mortal developers Fellyph Cintra
  2. 2. Senior Solutions Engineer at @DeloitteDigital GDE Web @fellyph
  3. 3. Artificial Intelligence ?
  4. 4. Artificial Intelligence ???
  5. 5. “Field of study that gives computers the ability to learn without being explicitly programmed.” - Arthur Samuel
  6. 6. “Field of study that gives computers the ability to learn without being explicitly programmed.” - Arthur Samuel - 1959
  7. 7. Normal computation 250 300 100 270 4 151 If (x > 200) { side = right; } else { side = left; } Right - 250 Right - 300 Left - 100 Right - 270 Left - 4 Left - 151
  8. 8. AI computation Right - 250 Right - 300 Left - 100 Right - 270 Left - 4 Left - 151 Predict next results
  9. 9. AI computation
  10. 10. AI Terms
  11. 11. AI Terms
  12. 12. ML5.js
  13. 13. ML5.js
  14. 14. What we can classify? Images Sounds Text
  15. 15. DEMO
  16. 16. Steps 1.Define labels(output) - Product ID 2.Training model 3.Export Pre-trained model 4.Use our model to classify products
  17. 17. LET’S CODE
  18. 18. Image Classification
  19. 19. Image classification
  20. 20. Using ImageNET beer glass: 94%pole: 76%
  21. 21. Training
  22. 22. Main functions
  23. 23. You can test it in a browser!
  24. 24. Coming soon! https://wicg.github.io/shape-detection-api/
  25. 25. Chrome support(behind flag) https://developers.google.com/web/updates/2019/01/shape-detection
  26. 26. Links https://p5js.org/ https://ml5js.org/ https://www.youtube.com/watch?v=jmznx0Q1fP0

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