Synthesized answer
TinyML is a field where deep learning and embedded systems come together to enable incredible capabilities in small devices [1, 2]. The core concept is to make deep learning models much smaller so they can run on microcontrollers [1]. For example, a word detection model can be as small as 14 kilobytes [1].
This book guides readers through training models that are small enough to operate in various environments [1]. It covers how to work with devices like Arduino and low-power microcontrollers, learn machine learning essentials, and train custom models for different types of data such as audio, image, and accelerometer readings [1]. The book also explores tools like TensorFlow Lite for Microcontrollers, Google's kit for TinyML, and discusses debugging, privacy, security, and optimizing performance aspects like latency, energy usage, and model size [1].
Synthesized from the book passages below. Chat with the book on Feynman for follow-up.
From the 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…
Categories: Computers Pages: 478 Snippet: 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.
Title: TinyML by Pete Warden, Daniel Situnayake