Machine Learning: deciphering the hype

With the advent of Core ML in iOS 11, machine learning is making its mainstream debut. Let's discover what machine learning actually is, what you can do with it, how it works and see how Core ML fits in.

Transcript

1. Machine Learning deciphering the hype
2. Core ML
3. With Core ML, you can integrate trained machine learning models into your app. — devloper.apple.com
6. classification tell me which of these groups the input falls into
7. regression estimate an output value given this input
8. clustering how many different classes does the data fall into?
9. 1 collect data
10. 2 ground truth
11. 3 feature vectors
12. 4 train model
13. 5 test model
14. 6 repeat 3, 4 & 5
15. 7 deploy model
16. 8 perform prediction
17. Machine Learning 1. Collect data 2. Ground truth 3. Feature vectors 4. Train model 5. Test model 6. Repeat 3, 4 & 5 7. Deploy model 8. Perform prediction
18. Machine Learning 1. Collect data 2. Ground truth 3. Feature vectors 4. Train model 5. Test model 6. Repeat 3, 4 & 5 7. Deploy model 8. Perform prediction
19. Linear Regression Support Vector Machine Tree Ensembles Bayesian Networks Neural Networks
20. Core ML provides an abstraction across different model types
21. model interchange format
22. device optimised implementation
23. consistent API
24. that just leaves the hard part
25. SVM support vector machine
28. example: sentiment analysis
29. bag of words
30. demo
31. convolutional neural networks
45. example: salient object subitizing
46. demo
47. conclusion
48. Core ML is a small part of the story
49. useful nonetheless
50. format optimisation API
51. potentially exciting times
52. github.com/sammyd /iOSDevUK17_CoreML - @iwantmyrealname