Book description
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.
- Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures
- Work with Arduino and ultra-low-power microcontrollers
- Learn the essentials of ML and how to train your own models
- Train models to understand audio, image, and accelerometer data
- Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML
- Debug applications and provide safeguards for privacy and security
- Optimize latency, energy usage, and model and binary size
Publisher resources
Table of contents
- Preface
- 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
- What Is TensorFlow Lite for Microcontrollers?
- Build Systems
- Supporting a New Hardware Platform
- Supporting a New IDE or Build System
- Integrating Code Changes Between Projects and Repositories
- Contributing Back to Open Source
- Supporting New Hardware Accelerators
- Understanding the File Format
- Porting TensorFlow Lite Mobile Ops to Micro
- Wrapping Up
- 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
- A. Using and Generating an Arduino Library Zip
- B. Capturing Audio on Arduino
- Index
Product information
- Title: TinyML
- Author(s):
- Release date: December 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492051992
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