Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers (Paperback)
暫譯: TinyML：在 Arduino 和超低功耗微控制器上使用 TensorFlow Lite 的機器學習 (平裝本)

Warden, Pete, Situnayake, Daniel

Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers (Paperback)

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商品描述

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

商品描述(中文翻譯)

神經網絡正在變得更小，變得非常小。例如，OK Google 團隊運行的機器學習模型僅有 14 KB 大小——足夠在 Android 手機的數位信號處理器上運行。這本實用的書籍將教你如何使用 TensorFlow Lite for Microcontrollers，這是一個微小的機器學習庫，允許你在微型硬體上運行機器學習算法。

作者 Pete Warden 和 Daniel Situnayake 解釋了如何訓練足夠小的模型，以適應任何環境，包括可以在單顆鈕電池上運行一年或更長時間的小型嵌入式設備。這本指南非常適合希望使用機器學習構建嵌入式設備的軟體和硬體開發人員，逐步展示如何創建一個 TinyML 項目。無需具備機器學習或微控制器的經驗。

- 學習在嵌入式設備上的實用機器學習應用，包括語音識別和手勢檢測等簡單用途
- 訓練語音、加速度計和圖像識別等模型，並可以在 Arduino 和其他嵌入式平台上部署
- 了解如何使用 Arduino 和超低功耗微控制器
- 使用優化延遲、能量使用以及模型和二進位檔大小的技術

作者簡介

Pete Warden is technical lead for mobile and embedded TensorFlow. He was CTO and founder of Jetpac, which was acquired by Google in 2014, and previously worked at Apple. He was a founding member of the TensorFlow team, and blogs about practical deep learning at https: //petewarden.com.

Daniel Situnayake leads developer advocacy for TensorFlow Lite at Google. He co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.

作者簡介(中文翻譯)

Pete Warden 是移動和嵌入式 TensorFlow 的技術負責人。他是 Jetpac 的首席技術官和創始人，該公司於 2014 年被 Google 收購，並曾在 Apple 工作。他是 TensorFlow 團隊的創始成員，並在 https://petewarden.com 上撰寫有關實用深度學習的博客。

Daniel Situnayake 在 Google 負責 TensorFlow Lite 的開發者倡導工作。他共同創立了 Tiny Farms，這是美國第一家使用自動化技術在工業規模生產昆蟲蛋白的公司。他的職業生涯始於伯明翰城市大學教授自動識別和數據捕獲。

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