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AI and Machine Learning On–Device Development

A Programmer's Guide

Paperback Engels 2021 9781098101749
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Samenvatting

AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating models and running them on popular mobile platforms such as iOS and Android.

Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today.

- Explore the options for implementing ML and AI on mobile devices--and when to use each
- Create ML models for iOS and Android
- Write ML Kit and TensorFlow Lite apps for iOS and Android and Core ML/Create ML apps for iOS
- Understand how to choose the best techniques and tools for your use case: on-device inference versus cloud-based inference, high-level APIs versus low-level APIs, and more

Specificaties

ISBN13:9781098101749
Taal:Engels
Bindwijze:paperback
Aantal pagina's:300
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:24-8-2021
Hoofdrubriek:IT-management / ICT

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Inhoudsopgave

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?
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 Pod Files
Step 3. Create the user interface.
Step 4. Add the application logic
Summary

4. Computer Vision Apps with ML Kit on Android
Image Labelling 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 Object Analyzer Class
The ObjectGraphic Class
Putting it all together
Summary

5. Computer Vision Apps with ML Kit on iOS
Image Labelling and Classification
Step 1. Create the App in Xcode
Step2. Create the Pod File
Step 3. Set up the Storyboard
Step 4. Edit the View Controller Code to use ML Kit
Object Detection in iOS with ML Kit
Step1. Get Started
Step 2. Create your UI on the Storyboard
Step 3. Create a Sub View 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

6. 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

7. Ethics, Fairness and Privacy for Mobile Apps
Ethics, Fairness and Privacy with Responsible AI
Responsibly defining your problem
Avoiding Bias in your Data
Building and training your model
Evaluating your Model
Google’s AI Principles

Summary

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