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Practical Artificial Intelligence with Swift. From Fundamental Theory to Development of AI-Driven Apps - Helion

Practical Artificial Intelligence with Swift. From Fundamental Theory to Development of AI-Driven Apps
ebook
Autor: Mars Geldard, Jonathon Manning, Paris Buttfield-Addison
ISBN: 9781492044765
stron: 526, Format: ebook
Data wydania: 2019-09-03
Księgarnia: Helion

Cena książki: 177,65 zł (poprzednio: 206,57 zł)
Oszczędzasz: 14% (-28,92 zł)

Dodaj do koszyka Practical Artificial Intelligence with Swift. From Fundamental Theory to Development of AI-Driven Apps

Tagi: iPhone

Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Taking a task-based approach, you’ll learn how to build features that use powerful AI features to identify images, make predictions, generate content, recommend things, and more.

AI is increasingly essential for every developer—and you don’t need to be a data scientist or mathematician to take advantage of it in your apps. Explore Swift-based AI and ML techniques for building applications. Learn where and how AI-driven features make sense. Inspect tools such as Apple’s Python-powered Turi Create and Google’s Swift for TensorFlow to train and build models.

  • I: Fundamentals and Tools—Learn AI basics, our task-based approach, and discover how to build or find a dataset.
  • II: Task Based AI—Build vision, audio, text, motion, and augmentation-related features; learn how to convert preexisting models.
  • III: Beyond—Discover the theory behind task-based practice, explore AI and ML methods, and learn how you can build it all from scratch... if you want to

Dodaj do koszyka Practical Artificial Intelligence with Swift. From Fundamental Theory to Development of AI-Driven Apps

 

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Dodaj do koszyka Practical Artificial Intelligence with Swift. From Fundamental Theory to Development of AI-Driven Apps

Spis treści

Practical Artificial Intelligence with Swift. From Fundamental Theory to Development of AI-Driven Apps eBook -- spis treści

  • 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
    • OReilly Online Learning
    • How to Contact Us
    • Acknowledgments
  • I. Fundamentals and Tools
  • 1. Artificial Intelligence!?
    • Practical AI with Swiftand 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
  • 2. Tools for Artificial Intelligence
    • Why Top Down?
    • Great Tools for Great AI
    • Tools from Apple
      • CoreML
        • CoreML doeswhat?
        • The CoreML model
        • The MLModel format
        • Why offline?
        • Understanding the pieces of CoreML
      • CreateML
        • Understanding the pieces of CreateML
      • Turi Create
        • Understanding the pieces of Turi Create
      • Apples 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 Peoples Tools
    • Whats Next?
  • 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
    • Apples Models
  • II. Tasks
  • 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
        • Creating a model
      • Incorporating the Model in the App
      • Improving the App
    • Task: Drawing Recognition
      • Problem and Approach
      • AI Toolkit and Dataset
        • Creating a model
      • Building the App
      • Whats Next?
    • Task: Style Classification
      • Converting the Model
      • Using the Model
    • Next Steps
  • 5. Audio
    • Audio and Practical AI
    • Task: Speech Recognition
      • Problem and Approach
      • Building the App
      • What Just Happened? How Does This Work?
      • Whats 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
  • 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
        • Creating a model
        • Using the model
    • Next Steps
  • 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
        • Preparing the data
        • Creating a model
    • Using the Model
    • Task: Using Augmented Reality with AI
    • Next Steps
  • 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
        • Creating the model
      • Building an App
    • Task: Recommending Movies
      • Problem and Approach
      • AI Toolkit and Dataset
        • Preparing the data
        • Creating a model
      • Using a Recommender
    • Task: Regressor Prediction
      • Problem and Approach
      • AI Toolkit and Dataset
        • Preparing the data
        • Creating a model
      • Using the Regressor in an App
    • Next Steps
  • 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
  • III. Beyond
  • 10. AI and ML Methods
    • Terminology
      • AI/ML Components
      • AI/ML Objectives
      • Types of Values
    • Classification
      • Methods
        • Naive Bayes
        • Decision trees
        • Distance metrics
        • Nearest neighbor
        • Support vector machine
        • Linear regression
        • Logistic regression
        • Neural network
      • Applications
        • Image recognition
        • Sound recognition
        • Estimation
        • Decision making
        • Recommendation systems
    • Clustering
      • Methods
        • Hierarchical
        • K-means
        • DBSCAN
        • Mean shift
      • Applications
        • Data exploration
        • Behavior profiling
        • Compression
    • Next Steps
  • 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
  • 12. The Hard Way
    • Behind CoreMLs 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

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