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
