
Practical Artificial Intelligence with Swift
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Content
- Intro
- Copyright
- Table of Contents
- Preface
- Resources Used in This Book
- Audience and Approach
- Organization of This Book
- Using This Book
- Our Tasks
- Conventions Used in This Book
- Using Code Examples
- O'Reilly Online Learning
- How to Contact Us
- Acknowledgments
- Part I. Fundamentals and Tools
- Chapter 1. Artificial Intelligence!?
- Practical AI with Swift.and Python?
- Code Examples
- Why Swift?
- Why AI?
- What Is AI and What Can It Do?
- Deep Learning versus AI?
- Where Do the Neural Networks Come In?
- Ethical, Effective, and Appropriate Use of AI
- Practical AI Tasks
- A Typical Task-Based Approach
- Chapter 2. Tools for Artificial Intelligence
- Why Top Down?
- Great Tools for Great AI
- Tools from Apple
- CoreML
- CreateML
- Turi Create
- Apple's Other Frameworks
- CoreML Community Tools
- Tools from Others
- Swift for TensorFlow
- TensorFlow to CoreML Model Converter
- Other Converters
- AI-Adjacent Tools
- Python
- Keras, Pandas, Jupyter, Colaboratory, Docker, Oh My!
- Other People's Tools
- What's Next?
- Chapter 3. Finding or Building a Dataset
- Planning and Identifying Data to Target
- Negation as Failure
- Closed-World Assumptions
- Finding a Dataset
- Where to Look
- What to Look Out for
- Building a Dataset
- Data Recording
- Data Collation
- Data Scraping
- Preparing a Dataset
- Getting to Know a Dataset
- Cleaning a Dataset
- Transforming a Dataset
- Verifying the Suitability of a Dataset
- Apple's Models
- Part II. Tasks
- Chapter 4. Vision
- Practical AI and Vision
- Task: Face Detection
- Problem and Approach
- Building the App
- What Just Happened? How Does This Work?
- Improving the App
- Even More Improvements
- Task: Barcode Detection
- Task: Saliency Detection
- Task: Image Similarity
- Problem and Approach
- Building the App
- What Just Happened? How Does This Work?
- Next Steps
- Task: Image Classification
- Problem and Approach
- Building the App
- AI Toolkit and Dataset
- Incorporating the Model in the App
- Improving the App
- Task: Drawing Recognition
- Problem and Approach
- AI Toolkit and Dataset
- Building the App
- What's Next?
- Task: Style Classification
- Converting the Model
- Using the Model
- Next Steps
- Chapter 5. Audio
- Audio and Practical AI
- Task: Speech Recognition
- Problem and Approach
- Building the App
- What Just Happened? How Does This Work?
- What's Next?
- Task: Sound Classification
- Problem and Approach
- Building the App
- AI Toolkit and Dataset
- Creating a Model
- Incorporating the Model in the App
- Improving the App
- Next Steps
- Chapter 6. Text and Language
- Practical AI, Text, and Language
- Task: Language Identification
- Task: Named Entity Recognition
- Task: Lemmatization, Tagging, and Tokenization
- Parts of Speech
- Tokenizing a Sentence
- Task: Sentiment Analysis
- Problem and Approach
- Building the App
- AI Toolkit and Dataset
- Creating a Model
- Incorporating the Model in the App
- Task: Custom Text Classifiers
- AI Toolkit and Dataset
- Next Steps
- Chapter 7. Motion and Gestures
- Practical AI, Motion, and Gestures
- Task: Activity Recognition
- Problem and Approach
- Building the App
- What Just Happened? How Does This Work?
- Task: Gestural Classification for Drawing
- Problem and Approach
- AI Toolkit and Dataset
- Building the App
- Task: Activity Classification
- Problem and Approach
- AI Toolkit and Dataset
- Using the Model
- Task: Using Augmented Reality with AI
- Next Steps
- Chapter 8. Augmentation
- Practical AI and Augmentation
- Task: Image Style Transfer
- Problem and Approach
- Building the App
- AI Toolkit and Dataset
- Creating a Model
- Incorporating the Model in the App
- Task: Sentence Generation
- What Just Happened? How Does This Work?
- Task: Image Generation with a GAN
- Problem and Approach
- AI Toolkit and Dataset
- Building an App
- Task: Recommending Movies
- Problem and Approach
- AI Toolkit and Dataset
- Using a Recommender
- Task: Regressor Prediction
- Problem and Approach
- AI Toolkit and Dataset
- Using the Regressor in an App
- Next Steps
- Chapter 9. Beyond Features
- Task: Installing Swift for TensorFlow
- Adding Swift for TensorFlow to Xcode
- Installing Swift for TensorFlow with Docker and Jupyter
- Using Python with Swift
- Task: Training a Classifier Using Swift for TensorFlow
- Task: Using the CoreML Community Tools
- The Problem
- The Process
- Using the Converted Model
- On-Device Model Updates
- Task: Downloading Models On-device
- Next Steps
- Part III. Beyond
- Chapter 10. AI and ML Methods
- Terminology
- AI/ML Components
- AI/ML Objectives
- Types of Values
- Classification
- Methods
- Applications
- Clustering
- Methods
- Applications
- Next Steps
- Chapter 11. Looking Under the Hood
- A Look Inside CoreML
- Vision
- Face Detection
- Barcode Detection
- Saliency Detection
- Image Classification
- Image Similarity
- Bitmap Drawing Classification
- Audio
- Sound Classification
- Speech Recognition
- Text and Language
- Language Identification
- Named Entity Recognition
- Lemmatization, Tagging, Tokenization
- Recommendations
- Prediction
- Text Generation
- Generation
- The Future of CoreML
- Next Steps
- Chapter 12. The Hard Way
- Behind CoreML's Magic
- Task: Building XOR
- The Shape of Our Network
- The Code
- Building It Up
- Making It Work
- Tearing It Down
- Using the Neural Network
- Approximations of XOR
- Training
- Next Steps
- Index
- About the Authors
- Colophon
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