TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
PDF, ePUB

Book Description
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size—small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
  • Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
  • Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
  • Understand how to work with Arduino and ultralow-power microcontrollers
  • Use techniques for optimizing latency, energy usage, and model and binary size

Content

1. Introduction
2. Getting Started
3. Getting Up to Speed on Machine Learning
4. The “Hello World” of TinyML: Building and Training a Model
5. The “Hello World” of TinyML: Building an Application
6. The “Hello World” of TinyML: Deploying to Microcontrollers
7. Wake-Word Detection: Building an Application
8. Wake-Word Detection: Training a Model
9. Person Detection: Building an Application
10. Person Detection: Training a Model
11. Magic Wand: Building an Application
12. Magic Wand: Training a Model
13. TensorFlow Lite for Microcontrollers
14. Designing Your Own TinyML Applications
15. Optimizing Latency
16. Optimizing Energy Usage
17. Optimizing Model and Binary Size
18. Debugging
19. Porting Models from TensorFlow to TensorFlow Lite
20. Privacy, Security, and Deployment
21. Learning More

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