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Mastering iOS 12 Programming

You're reading from   Mastering iOS 12 Programming Build professional-grade iOS applications with Swift and Xcode 10

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789133202
Length 750 pages
Edition 3rd Edition
Languages
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Author (1):
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Donny Wals Donny Wals
Author Profile Icon Donny Wals
Donny Wals
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Table of Contents (29) Chapters Close

Preface 1. UITableView Touch-up FREE CHAPTER 2. A Better Layout with UICollectionView 3. Creating a Detail Page 4. Immersing Your Users with Animation 5. Understanding the Swift Type System 6. Writing Flexible Code with Protocols and Generics 7. Improving the Application Structure 8. Adding Core Data to Your App 9. Fetching and Displaying Data from the Network 10. Being Proactive with Background Fetch 11. Syncing Data with CloudKit 12. Using Augmented Reality 13. Improving Apps With Location Services 14. Making Smarter Apps with CoreML 15. Tracking Activity Using HealthKit 16. Streamlining Experiences with Siri 17. Using Media in Your App 18. Implementing Rich Notifications 19. Instant Information with a Today Extension 20. Exchanging Data With Drag And Drop 21. Improved Discoverability with Spotlight and Universal Links 22. Extending iMessage 23. Ensuring App Quality with Tests 24. Discovering Bottlenecks with Instruments 25. Offloading Tasks with Operations and GCD 26. Submitting Your App to the App Store 27. Answers 28. Other Books You May Enjoy

Summary

In this chapter, you have seen how you can make use of the machine learning capabilities that iOS provides. You saw that adding a machine learning model to your app is extremely simple, since you only have to drag it to Xcode and add it to your target app. You also learned how you can obtain models, and where to look to convert existing models to CoreML models. Creating a machine learning model is not simple, so it's great that Apple has made it so simple to implement machine learning by embedding trained models in your apps.

In addition to CoreML, you also learned about the Vision and Natural Language frameworks. Vision combines the power of CoreML and smart image analysis to create a compelling framework that can perform a massive amount of work on images. Convenient requests, such as facial landmark detection, text analysis, and more are available out of the box...

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