AI and Machine Learning for On-Device Development Laurence Moroney

Spis treści książki

• Preface
  • Who Should Read This Book?
  • Why I Wrote This Book
  • Navigating This Book
  • Technology You Need to Understand
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgements
• 1. Introduction to AI and Machine Learning
  • What Is Artificial Intelligence?
  • What Is Machine Learning?
    • Moving from Traditional Programming to Machine Learning
    • How Can a Machine Learn?
      • Step 1: Guess the answer
      • Step 2: Measure the accuracy of our guess
      • Step 3: Optimize our guess
      • Implementing machine learning in code
    • Comparing Machine Learning with Traditional Programming
  • Building and Using Models on Mobile
  • Summary
• 2. Introduction to Computer Vision
  • Using Neurons for Vision
    • Your First Classifier: Recognizing Clothing Items
    • The Data: Fashion MNIST
    • A Model Architecture to Parse Fashion MNIST
    • Coding the Fashion MNIST Model
  • Transfer Learning for Computer Vision
  • Summary
• 3. Introduction to ML Kit
  • Building a Face Detection App on Android
    • Step 1: Create the App with Android Studio
    • Step 2: Add and Configure ML Kit
    • Step 3: Define the User Interface
    • Step 4: Add the Images as Assets
    • Step 5: Load the UI with a Default Picture
    • Step 6: Call the Face Detector
    • Step 7: Add the Bounding Rectangles
  • Building a Face Detector App for iOS
    • Step 1: Create the Project in Xcode
    • Step 2: Using CocoaPods and Podfiles
    • Step 3: Create the User Interface
    • Step 4: Add the Application Logic
  • Summary
• 4. Computer Vision Apps with ML Kit on Android
  • Image Labeling and Classification
    • Step 1: Create the App and Configure ML Kit
    • Step 2: Create the User Interface
    • Step 3: Add the Images as Assets
    • Step 4: Load an Image to the ImageView
    • Step 5: Write the Button Handler Code
    • Next Steps
  • Object Detection
    • Step 1: Create the App and Import ML Kit
    • Step 2: Create the Activity Layout XML
    • Step 3: Load an Image into the ImageView
    • Step 4: Set Up the Object Detector Options
    • Step 5: Handling the Button Interaction
    • Step 6: Draw the Bounding Boxes
    • Step 7: Label the Objects
  • Detecting and Tracking Objects in Video
    • Exploring the Layout
    • The GraphicOverlay Class
    • Capturing the Camera
    • The ObjectAnalyzer Class
    • The ObjectGraphic Class
    • Putting It All Together
  • Summary
• 5. Text Processing Apps with ML Kit on Android
  • Entity Extraction
    • Start Creating the App
    • Create the Layout for the Activity
    • Write the Entity Extraction Code
    • Putting It All Together
  • Handwriting and Other Recognition
    • Start the App
    • Creating a Drawing Surface
    • Parsing the Ink with ML Kit
  • Smart Reply to Conversations
    • Start the App
    • Mock a Conversation
    • Generating a Smart Reply
  • Summary
• 6. Computer Vision Apps with ML Kit on iOS
  • Image Labeling and Classification
    • Step 1: Create the App in Xcode
    • Step 2: Create the Podfile
    • Step 3: Set Up the Storyboard
    • Step 4: Edit the View Controller Code to Use ML Kit
  • Object Detection in iOS with ML Kit
    • Step 1: Get Started
    • Step 2: Create Your UI on the Storyboard
    • Step 3: Create a Subview for Annotation
    • Step 4: Perform the Object Detection
    • Step 5: Handle the Callback
    • Combining Object Detection with Image Classification
    • Object Detection and Tracking in Video
  • Summary
• 7. Text Processing Apps with ML Kit on iOS
  • Entity Extraction
    • Step 1: Create the App and Add the ML Kit Pods
    • Step 2: Create the Storyboard with Actions and Outlets
    • Step 3: Allow Your View Controller to be Used for Text Entry
    • Step 4: Initialize the Model
    • Step 5: Extract Entities from Text
  • Handwriting Recognition
    • Step 1: Create the App and Add the ML Kit Pods
    • Step 2: Create the Storyboard, Actions, and Outlets
    • Step 3: Strokes, Points, and Ink
    • Step 4: Capture User Input
    • Step 5: Initialize the Model
    • Step 6: Do the Ink Recognition
  • Smart Reply to Conversations
    • Step 1: Create an App and Integrate ML Kit
    • Step 2: Create Storyboard, Outlets, and Actions
    • Step 3: Create a Simulated Conversation
    • Step 4: Get Smart Reply
  • Summary
• 8. Going Deeper: Understanding TensorFlow Lite
  • What Is TensorFlow Lite?
  • Getting Started with TensorFlow Lite
    • Save the Model
    • Convert the Model
    • Testing the Model with a Standalone Interpreter
  • Create an Android App to Host TFLite
    • Import the TFLite File
    • Write Kotlin Code to Interface with the Model
    • Going Beyond the Basics
  • Create an iOS App to Host TFLite
    • Step 1: Create a Basic iOS App
    • Step 2: Add TensorFlow Lite to Your Project
    • Step 3: Create the User Interface
    • Step 4: Add and Initialize the Model Inference Class
    • Step 5: Perform the Inference
    • Step 6: Add the Model to Your App
    • Step 7: Add the UI Logic
    • Moving Beyond Hello World: Processing Images
  • Exploring Model Optimization
    • Quantization
    • Using Representative Data
  • Summary
• 9. Creating Custom Models
  • Creating a Model with TensorFlow Lite Model Maker
  • Creating a Model with Cloud AutoML
    • Using AutoML Vision Edge
      • Step 1: Enable the API
      • Step 2: Install the gcloud command-line tool
      • Step 3: Set up a service account
      • Step 4: Set up a Cloud Storage bucket and store training data in it
      • Step 5: Turn your images into a dataset and train the model
      • Step 6: Download the model
  • Creating a Model with TensorFlow and Transfer Learning
  • Creating Language Models
    • Create a Language Model with Model Maker
  • Summary
• 10. Using Custom Models in Android
  • Bridging Models to Android
  • Building an Image Classification App from a Model Maker Output
  • Using a Model Maker Output with ML Kit
  • Using Language Models
  • Creating an Android App for Language Classification
    • Create a layout file
    • Code the activity
  • Summary
• 11. Using Custom Models in iOS
  • Bridging Models to iOS
  • A Custom Model Image Classifier
    • Step 1: Create the App and Add the TensorFlow Lite Pod
    • Step 2: Create the UI and Image Assets
    • Step 3: Load and Navigate Through the Image Assets
    • Step 4: Load the Model
    • Step 5: Convert an Image to an Input Tensor
    • Step 6: Get Inference for the Tensor
  • Use a Custom Model in ML Kit
  • Building an App for Natural Language Processing in Swift
    • Step 1: Load the Vocab
    • Step 2: Convert the Sentence to a Sequence
    • Step 3: Extend Array to Handle Unsafe Data
    • Step 4: Copy the Array to a Data Buffer
    • Step 5: Run Inference on the Data and Process the Results
  • Summary
• 12. Productizing Your App Using Firebase
  • Why Use Firebase Custom Model Hosting?
  • Create Multiple Model Versions
  • Using Firebase Model Hosting
    • Step 1: Create a Firebase Project
    • Step 2: Use Custom Model Hosting
    • Step 3: Create a Basic Android App
    • Step 4: Add Firebase to the App
    • Step 5: Get the Model from Firebase Model Hosting
    • Step 6: Use Remote Configuration
    • Step 7: Read Remote Configuration in Your App
    • Next Steps
  • Summary
• 13. Create ML and Core ML for Simple iOS Apps
  • A Core ML Image Classifier Built Using Create ML
    • Making a Core ML App That Uses a Create ML Model
    • Add the MLModel File
    • Run the Inference
      • The Core ML inference pattern
      • Writing the code
  • Using Create ML to Build a Text Classifier
  • Use the Model in an App
  • Summary
• 14. Accessing Cloud-Based Models from Mobile Apps
  • Installing TensorFlow Serving
    • Installing Using Docker
    • Installing Directly on Linux
  • Building and Serving a Model
  • Accessing a Server Model from Android
  • Accessing a Server Model from iOS
  • Summary
• 15. Ethics, Fairness, and Privacy for Mobile Apps
  • Ethics, Fairness, and Privacy with Responsible AI
    • Responsibly Defining Your Problem
    • Avoiding Bias in Your Data
      • The What-If Tool
      • Facets
      • TensorFlow Model Card Toolkit
      • TensorFlow data validation
    • Building and Training Your Model
      • Model remediation
      • Model privacy
      • Federated learning
    • Evaluating Your Model
      • Fairness Indicators
      • TensorFlow Model Analysis
      • Language Interpretability Toolkit
  • Googles AI Principles
  • Summary
• Index

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