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Swift Cookbook

Swift Cookbook

By : Keith Moon, D. Moon, Chris Barker
5 (10)
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Swift Cookbook

Swift Cookbook

5 (10)
By: Keith Moon, D. Moon, Chris Barker

Overview of this book

Swift is an exciting, multi-platform, general-purpose programming language, and with this book, you'll explore the features of its latest version, Swift 5.3. The book begins with an introduction to the basic building blocks of Swift 5.3, its syntax, and the functionalities of Swift constructs. You’ll then discover how Swift Playgrounds provide an ideal platform to write, execute, and debug your Swift code. As you advance through the chapters, the book will show you how to bundle variables into tuples or sets, order your data with an array, store key-value pairs with dictionaries, and use property observers. You’ll also get to grips with the decision-making and control structures in Swift, examine advanced features such as generics and operators, and explore functionalities outside of the standard library. Once you’ve learned how to build iOS applications using UIKit, you'll find out how to use Swift for server-side programming, run Swift on Linux, and investigate Vapor. Finally, you'll discover some of the newest features of Swift 5.3 using SwiftUI and Combine to build adaptive and reactive applications, and find out how to use Swift to build and integrate machine learning models along with Apple’s Vision Framework. By the end of this Swift book, you'll have discovered solutions to boost your productivity while developing code using Swift 5.3.
Table of Contents (14 chapters)
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12
About Packt

Using CoreML models to detect objects in images

In this recipe, we'll take the app we just built and incorporate the CoreML framework in order to detect objects in our images.

We'll also take a look at the generated CoreML models available for us to use and download directly from Apple's Developer portal.

Getting ready

For this recipe, you'll need the latest version of Xcode available from the Mac App Store.

Next, head on over to the Apple Developer portal at the following address: https://developer.apple.com/machine-learning/models/.

Here, you will find out a little bit more about the models available for us to download and use in our Xcode project.

You'll notice there are options for image models and text models. For this recipe, we're going to be using image models, specifically one called Resnet50, which uses a residual neural network that attempts to identify and classify what it perceives to be the dominant object in an image.

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