دانلود کتاب AI at the Edge: Solving Real-World Problems with Embedded Machine Learning

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Foreword Preface About This Book What to Expect What You Need to Know Already Responsible, Ethical, and Effective AI Further Resources Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments 1. A Brief Introduction to Edge AI Defining Key Terms Embedded The Edge (and the Internet of Things) Artificial Intelligence Machine Learning Edge AI Embedded Machine Learning and Tiny Machine Learning Digital Signal Processing Why Do We Need Edge AI? To Understand the Benefits of Edge AI, Just BLERP Edge AI for Good Key Differences Between Edge AI and Regular AI Summary 2. Edge AI in the Real World Common Use Cases for Edge AI Greenfield and Brownfield Projects Real-World Products Types of Applications Keeping Track of Objects Understanding and Controlling Systems Understanding People and Living Things Transforming Signals Building Applications Responsibly Responsible Design and AI Ethics Black Boxes and Bias Technology That Harms, Not Helps Summary 3. The Hardware of Edge AI Sensors, Signals, and Sources of Data Types of Sensors and Signals Acoustic and Vibration Visual and Scene Motion and Position Force and Tactile Optical, Electromagnetic, and Radiation Environmental, Biological, and Chemical Other Signals Processors for Edge AI Edge AI Hardware Architecture Microcontrollers and Digital Signal Processors System-on-Chip Deep Learning Accelerators FPGAs and ASICs Edge Servers Multi-Device Architectures Devices and Workloads Summary 4. Algorithms for Edge AI Feature Engineering Working with Data Streams Digital Signal Processing Algorithms Combining Features and Sensors Artificial Intelligence Algorithms Algorithm Types by Functionality Algorithm Types by Implementation Optimization for Edge Devices On-Device Training Summary 5. Tools and Expertise Building a Team for AI at the Edge Domain Expertise Diversity Stakeholders Roles and Responsibilities Hiring for Edge AI Learning Edge AI Skills Tools of the Trade Software Engineering Working with Data Algorithm Development Running Algorithms On-Device Embedded Software Engineering and Electronics End-to-End Platforms for Edge AI Summary 6. Understanding and Framing Problems The Edge AI Workflow Responsible AI in the Edge AI Workflow Do I Need Edge AI? Describing a Problem Do I Need to Deploy to the Edge? Do I Need Machine Learning? Practical Exercise Determining Feasibility Moral Feasibility Business Feasibility Dataset Feasibility Technological Feasibility Making a Final Decision Planning an Edge AI Project Summary 7. How to Build a Dataset What Does a Dataset Look Like? The Ideal Dataset Datasets and Domain Expertise Data, Ethics, and Responsible AI Minimizing Unknowns Ensuring Domain Expertise Data-Centric Machine Learning Estimating Data Requirements A Practical Workflow for Estimating Data Requirements Getting Your Hands on Data The Unique Challenges of Capturing Data at the Edge Storing and Retrieving Data Getting Data into Stores Collecting Metadata Ensuring Data Quality Ensuring Representative Datasets Reviewing Data by Sampling Label Noise Common Data Errors Drift and Shift The Uneven Distribution of Errors Preparing Data Labeling Formatting Data Cleaning Feature Engineering Splitting Your Data Data Augmentation Data Pipelines Building a Dataset over Time Summary 8. Designing Edge AI Applications Product and Experience Design Design Principles Scoping a Solution Setting Design Goals Architectural Design Hardware, Software, and Services Basic Application Architectures Complex Application Architectures and Design Patterns Working with Design Patterns Accounting for Choices in Design Design Deliverables Summary 9. Developing Edge AI Applications An Iterative Workflow for Edge AI Development Exploration Goal Setting Bootstrapping Test and Iterate Deployment Support Summary 10. Evaluating, Deploying, and Supporting Edge AI Applications Evaluating Edge AI Systems Ways to Evaluate a System Useful Metrics Techniques for Evaluation Evaluation and Responsible AI Deploying Edge AI Applications Predeployment Tasks Mid-Deployment Tasks Postdeployment Tasks Supporting Edge AI Applications Postdeployment Monitoring Improving a Live Application Ethics and Long-Term Support What Comes Next 11. Use Case: Wildlife Monitoring Problem Exploration Solution Exploration Goal Setting Solution Design What Solutions Already Exist? Solution Design Approaches Design Considerations Environmental Impact Bootstrapping Define Your Machine Learning Classes Dataset Gathering Edge Impulse Choose Your Hardware and Sensors Data Collection iNaturalist Dataset Limitations Dataset Licensing and Legal Obligations Cleaning Your Dataset Uploading Data to Edge Impulse DSP and Machine Learning Workflow Digital Signal Processing Block Machine Learning Block Testing the Model Live Classification Model Testing Test Your Model Locally Deployment Create Library Mobile Phone and Computer Prebuilt Binary Flashing Impulse Runner GitHub Source Code Iterate and Feedback Loops AI for Good Related Works Datasets Research 12. Use Case: Food Quality Assurance Problem Exploration Solution Exploration Goal Setting Solution Design What Solutions Already Exist? Solution Design Approaches Design Considerations Environmental and Social Impact Bootstrapping Define Your Machine Learning Classes Dataset Gathering Edge Impulse Choose Your Hardware and Sensors Data Collection Data Ingestion Firmware Uploading Data to Edge Impulse Cleaning Your Dataset Dataset Licensing and Legal Obligations DSP and Machine Learning Workflow Digital Signal Processing Block Machine Learning Block Testing the Model Live Classification Model Testing Deployment Prebuilt Binary Flashing GitHub Source Code Iterate and Feedback Loops Related Works Research News and Other Articles 13. Use Case: Consumer Products Problem Exploration Goal Setting Solution Design What Solutions Already Exist? Solution Design Approaches Design Considerations Environmental and Social Impact Bootstrapping Define Your Machine Learning Classes Dataset Gathering Edge Impulse Choose Your Hardware and Sensors Data Collection Data Ingestion Firmware Cleaning Your Dataset Dataset Licensing and Legal Obligations DSP and Machine Learning Workflow Digital Signal Processing Block Machine Learning Blocks Testing the Model Live Classification Model Testing Deployment Prebuilt Binary Flashing GitHub Source Code Iterate and Feedback Loops Related Works Research News and Other Articles Index About the Authors