book

AI and Machine Learning for On-Device Development

Name: AI and Machine Learning for On-Device Development
Author: Laurence Moroney
ISBN: 9781098101749

by Laurence Moroney

August 2021

Intermediate to advanced

326 pages

8h 31m

English

O'Reilly Media, Inc.

Audiobook available

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Preface
Who Should Read This Book?Why I Wrote This BookNavigating This BookTechnology You Need to UnderstandConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgements
1. Introduction to AI and Machine Learning
What Is Artificial Intelligence?What Is Machine Learning?Moving from Traditional Programming to Machine LearningHow Can a Machine Learn?Comparing Machine Learning with Traditional ProgrammingBuilding and Using Models on MobileSummary
2. Introduction to Computer Vision
Using Neurons for VisionYour First Classifier: Recognizing Clothing ItemsThe Data: Fashion MNISTA Model Architecture to Parse Fashion MNISTCoding the Fashion MNIST ModelTransfer Learning for Computer VisionSummary
3. Introduction to ML Kit
Building a Face Detection App on AndroidStep 1: Create the App with Android StudioStep 2: Add and Configure ML KitStep 3: Define the User InterfaceStep 4: Add the Images as AssetsStep 5: Load the UI with a Default PictureStep 6: Call the Face DetectorStep 7: Add the Bounding RectanglesBuilding a Face Detector App for iOSStep 1: Create the Project in XcodeStep 2: Using CocoaPods and PodfilesStep 3: Create the User InterfaceStep 4: Add the Application LogicSummary
4. Computer Vision Apps with ML Kit on Android
Image Labeling and ClassificationStep 1: Create the App and Configure ML KitStep 2: Create the User InterfaceStep 3: Add the Images as AssetsStep 4: Load an Image to the ImageViewStep 5: Write the Button Handler CodeNext StepsObject DetectionStep 1: Create the App and Import ML KitStep 2: Create the Activity Layout XMLStep 3: Load an Image into the ImageViewStep 4: Set Up the Object Detector OptionsStep 5: Handling the Button InteractionStep 6: Draw the Bounding BoxesStep 7: Label the ObjectsDetecting and Tracking Objects in VideoExploring the LayoutThe GraphicOverlay ClassCapturing the CameraThe ObjectAnalyzer ClassThe ObjectGraphic ClassPutting It All TogetherSummary
5. Text Processing Apps with ML Kit on Android
Entity ExtractionStart Creating the AppCreate the Layout for the ActivityWrite the Entity Extraction CodePutting It All TogetherHandwriting and Other RecognitionStart the AppCreating a Drawing SurfaceParsing the Ink with ML KitSmart Reply to ConversationsStart the AppMock a ConversationGenerating a Smart ReplySummary
6. Computer Vision Apps with ML Kit on iOS
Image Labeling and ClassificationStep 1: Create the App in XcodeStep 2: Create the PodfileStep 3: Set Up the StoryboardStep 4: Edit the View Controller Code to Use ML KitObject Detection in iOS with ML KitStep 1: Get StartedStep 2: Create Your UI on the StoryboardStep 3: Create a Subview for AnnotationStep 4: Perform the Object DetectionStep 5: Handle the CallbackCombining Object Detection with Image ClassificationObject Detection and Tracking in VideoSummary
7. Text Processing Apps with ML Kit on iOS
Entity ExtractionStep 1: Create the App and Add the ML Kit PodsStep 2: Create the Storyboard with Actions and OutletsStep 3: Allow Your View Controller to be Used for Text EntryStep 4: Initialize the ModelStep 5: Extract Entities from TextHandwriting RecognitionStep 1: Create the App and Add the ML Kit PodsStep 2: Create the Storyboard, Actions, and OutletsStep 3: Strokes, Points, and InkStep 4: Capture User InputStep 5: Initialize the ModelStep 6: Do the Ink RecognitionSmart Reply to ConversationsStep 1: Create an App and Integrate ML KitStep 2: Create Storyboard, Outlets, and ActionsStep 3: Create a Simulated ConversationStep 4: Get Smart ReplySummary
8. Going Deeper: Understanding TensorFlow Lite
What Is TensorFlow Lite?Getting Started with TensorFlow LiteSave the ModelConvert the ModelTesting the Model with a Standalone InterpreterCreate an Android App to Host TFLiteImport the TFLite FileWrite Kotlin Code to Interface with the ModelGoing Beyond the BasicsCreate an iOS App to Host TFLiteStep 1: Create a Basic iOS AppStep 2: Add TensorFlow Lite to Your ProjectStep 3: Create the User InterfaceStep 4: Add and Initialize the Model Inference ClassStep 5: Perform the InferenceStep 6: Add the Model to Your AppStep 7: Add the UI LogicMoving Beyond “Hello World”: Processing ImagesExploring Model OptimizationQuantizationUsing Representative DataSummary
9. Creating Custom Models
Creating a Model with TensorFlow Lite Model MakerCreating a Model with Cloud AutoMLUsing AutoML Vision EdgeCreating a Model with TensorFlow and Transfer LearningCreating Language ModelsCreate a Language Model with Model MakerSummary

10. Using Custom Models in Android
Bridging Models to AndroidBuilding an Image Classification App from a Model Maker OutputUsing a Model Maker Output with ML KitUsing Language ModelsCreating an Android App for Language ClassificationSummary
11. Using Custom Models in iOS
Bridging Models to iOSA Custom Model Image ClassifierStep 1: Create the App and Add the TensorFlow Lite PodStep 2: Create the UI and Image AssetsStep 3: Load and Navigate Through the Image AssetsStep 4: Load the ModelStep 5: Convert an Image to an Input TensorStep 6: Get Inference for the TensorUse a Custom Model in ML KitBuilding an App for Natural Language Processing in SwiftStep 1: Load the VocabStep 2: Convert the Sentence to a SequenceStep 3: Extend Array to Handle Unsafe DataStep 4: Copy the Array to a Data BufferStep 5: Run Inference on the Data and Process the ResultsSummary
12. Productizing Your App Using Firebase
Why Use Firebase Custom Model Hosting?Create Multiple Model VersionsUsing Firebase Model HostingStep 1: Create a Firebase ProjectStep 2: Use Custom Model HostingStep 3: Create a Basic Android AppStep 4: Add Firebase to the AppStep 5: Get the Model from Firebase Model HostingStep 6: Use Remote ConfigurationStep 7: Read Remote Configuration in Your AppNext StepsSummary
13. Create ML and Core ML for Simple iOS Apps
A Core ML Image Classifier Built Using Create MLMaking a Core ML App That Uses a Create ML ModelAdd the MLModel FileRun the InferenceUsing Create ML to Build a Text ClassifierUse the Model in an AppSummary
14. Accessing Cloud-Based Models from Mobile Apps
Installing TensorFlow ServingInstalling Using DockerInstalling Directly on LinuxBuilding and Serving a ModelAccessing a Server Model from AndroidAccessing a Server Model from iOSSummary
15. Ethics, Fairness, and Privacy for Mobile Apps
Ethics, Fairness, and Privacy with Responsible AIResponsibly Defining Your ProblemAvoiding Bias in Your DataBuilding and Training Your ModelEvaluating Your ModelGoogle’s AI PrinciplesSummary
Index

Content preview from AI and Machine Learning for On-Device Development

Chapter 13. Create ML and Core ML for Simple iOS Apps

In this book so far, you’ve been looking at technologies that bring machine learning to multiple devices, so that you could use a single API to reach Android, iOS, embedded systems, microcontrollers, and more. This was made possible by the TensorFlow ecosystem, and in particular TensorFlow Lite, which underpins ML Kit, which you used as a higher level API. And while we didn’t go into embedded systems and microcontrollers, the concepts are the same, other than hardware limitations the smaller you go. To learn more about that space, check out the great book TinyML by Pete Warden and Daniel Situnayake (O’Reilly).

But I would be remiss if I didn’t at least cover the iOS-specific Create ML tool and Core ML libraries from Apple, which are designed to let you use ML models when creating apps for iOS, iPadOS or MacOS. Create ML in particular is a really nice visual tool that lets you create models without any prior ML programming experience.

We’ll look at a few scenarios, starting with creating a model that recognizes flowers, similar to the ones we did earlier with TensorFlow and TensorFlow Lite.

A Core ML Image Classifier Built Using Create ML

We’ll start with creating our model. We can do this codelessly using the Create ML tool. You can find this by right-clicking on Xcode in the doc, and then in the Open Developer Tool menu, you can find Create ML. See Figure 13-1.

Figure 13-1. Launching Create ML

When the tool launches, you’ll ...

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