“This is a must-read book for anyone interested in machine learning on resource-constrained devices. It is a milestone in the development of AI.”Massimo Banzi, Cofounder, Arduino
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
Free PDF Preview
If you’re interested in trying out the book before you buy, you can download a free PDF of the first hundred pages, containing six chapters that introduce you to the basic concepts you need, and walk you through building a simple embedded machine learning application from training a model to deploying it on a device.
There’s a growing collection of screencasts working through each tutorial chapter in detail available free on YouTube!
For questions or comments, join the community list at email@example.com
TinyML is a registered trademark of the TinyML foundation, and is used with permission.