WWDC 2017
Session video and resources: https://developer.apple.com/videos/play/wwdc2017/703/
- Real time image recognition
- Text prediction
- Entity recognition
- Style transfer
- Speaker identification and many more...
- Learning algorithm -> Model
- Input -> Model -> Desired Output
- Challenges
- Correctness
- Performance
- Energy efficiency
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VisionKit
- Object tracking
- Face detection
-
NLP (Natural language processing)
- Language identification
- Named entity recognition API
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Core ML
- Music tagging (tag parts of the music with data)
- Image captioning (image -> text)
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All these are powered with Accelerate and MPS
- High performance math
- Privacy
- Prevent data cost
- Server cost
- Always available (24/7)
- Function learned from data
- Observed inputs
- Predicts outputs
- Single document
- Public format
- Sample models
https://developer.apple.com/machine-learning
- Core ML models
- Ready to use
- Convert to Core ML using python packages (This is fully open source)
- Add your ML Model to your project -> autogeneration to Swift file