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
Pete Warden
Google公司TensorFlow面向移动和嵌入式设备部分的技术主管，也是TensorFlow团队的创始成员之一。他曾是Jetpac的首席技术官（CTO）和创始人，该公司于2014年被Google收购。
Daniel Situnayake
Google领导TensorFlow Lite的开发宣传工作，并协助运营TinyML meetup小组。他是Tiny Farms的联合创始人，这是美国第一家利用自动化技术以工业规模生产昆虫蛋白的公司。
译者与审校者简介
魏兰
软件开发工程师，现就职于Google北京。机器视觉，Android性能优化爱好者。博客：blog.csdn.net/xiaowei_cqu。
卜杰
毕业于南京邮电大学，现于Google北京担任软件工程师。邮箱：[email protected]。
王铁震
现就职于Go...