This document discusses machine learning for developers and provides an overview of key concepts. It defines artificial intelligence and machine learning, gives examples of tasks like image, sound, and text classification. It demonstrates using ML5.js to build an image classification model with steps for defining labels, training a model, and exporting/using the pre-trained model. Code examples are provided for image classification using pre-trained models from ImageNet and for building custom models with training data. Browser-based demos and upcoming browser APIs for shape detection are also mentioned.

2. Machine learning for mortal developers
Fellyph Cintra

3. Senior Solutions Engineer at
@DeloitteDigital
GDE Web
@fellyph

4. Artificial
Intelligence ?

5. Artificial Intelligence ???

6. “Field of study that gives
computers the ability to learn
without being explicitly
programmed.”
- Arthur Samuel

7. “Field of study that gives
computers the ability to learn
without being explicitly
programmed.”
- Arthur Samuel - 1959

8. 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

9. AI computation
Right - 250
Right - 300
Left - 100
Right - 270
Left - 4
Left - 151
Predict
next
results

10. AI computation

11. AI Terms

12. AI Terms

16. ML5.js

17. ML5.js

18. What we can classify?
Images Sounds Text

19. DEMO

21. Steps
1.Define labels(output) - Product ID
2.Training model
3.Export Pre-trained model
4.Use our model to classify products

22. LET’S CODE

23. Image Classification

25. Image classification

27. Using ImageNET
beer glass: 94%pole: 76%

28. Training

32. Main functions

34. You can test it in a browser!

37. Coming soon!
https://wicg.github.io/shape-detection-api/

38. Chrome support(behind flag)
https://developers.google.com/web/updates/2019/01/shape-detection

39. Links
https://p5js.org/
https://ml5js.org/
https://www.youtube.com/watch?v=jmznx0Q1fP0