Tiny Machine Learning QuickStart: Machine Learning for Arduino Microcontrollers
暫譯: 微型機器學習快速入門：Arduino 微控制器的機器學習
Salerno, Simone
- 出版商: Apress
- 出版日期: 2025-04-16
- 售價: $2,760
- 貴賓價: 9.5 折 $2,622
- 語言: 英文
- 頁數: 326
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798868812934
- ISBN-13: 9798868812934
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相關分類:
Arduino、單晶片、Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.
You'll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You'll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you'll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.
Throughout the book, you'll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.
What You Will Learn
- Navigate embedded ML challenges
- Integrate Python with Arduino for seamless data processing
- Implement ML algorithms
- Harness the power of Tensorflow for artificial neural networks
- Leverage no-code tools like Edge Impulse
- Execute real-world projects
Who This Book Is For
Electronics hobbyists and developers with a basic understanding of Tensorflow, ML in Python, and Arduino-based programming looking to apply that knowledge with microcontrollers. Previous experience with C++ is helpful but not required.
商品描述(中文翻譯)
成為不斷擴展的物聯網（IoT）世界中 Tiny Machine Learning（TinyML）革命的一部分。本書探討了在 Arduino 平台上使您的專案更智能所需的概念、工作流程和工具。
您將首先探索嵌入式、資源受限設備中的機器學習，與您強大的、擁有千兆位元組 RAM 的電腦相比。您將回顧它所帶來的獨特挑戰，但也會看到它所開啟的無限可能性。接下來，您將完成九個涵蓋不同數據類型（表格、時間序列、音頻和圖像）和任務（分類和回歸）的專案。每個專案都附有收集、加載、繪製和分析每種類型數據的提示和技巧。
在整本書中，您將應用三種不同的 TinyML 方法：傳統算法（決策樹、邏輯回歸、支持向量機 SVM）、Edge Impulse（無代碼在線工具）和微控制器的 TensorFlow。每種方法都有其優缺點，您將學習如何選擇最適合您用例的方案。TinyML 快速入門 將為您未來的所有專案提供堅實的參考，並且成本和努力都最小化。
您將學到什麼
- 應對嵌入式機器學習挑戰
- 將 Python 與 Arduino 整合以實現無縫數據處理
- 實現機器學習算法
- 利用 TensorFlow 的人工神經網絡能力
- 利用 Edge Impulse 等無代碼工具
- 執行實際專案
本書適合誰
對 TensorFlow、Python 中的機器學習和基於 Arduino 的編程有基本了解的電子愛好者和開發者，尋求將這些知識應用於微控制器。具備 C++ 的經驗會有幫助，但不是必需的。
作者簡介
Simone Salerno has been tinkering with microcontrollers for nearly 10 years and is committed to bringing his knowledge of software engineering to the world of Arduino programming. With the advent of Tensorflow for Microcontrollers he began developing leaner, faster alternatives to neural networks for microcontrollers and started porting many traditional ML algorithms such as Decision Tree, Random Forest, and Logistic Regression from Python to self-contained, hardware-independent C++, ready to be deployed to any microcontroller. Today, he continues to focus on the development of TinyML tools and tutorials with his low-code libraries and no-code online platforms like Edge Impulse.
作者簡介(中文翻譯)
Simone Salerno 已經在微控制器領域摸索了近十年，並致力於將他的軟體工程知識帶入 Arduino 編程的世界。隨著 Tensorflow for Microcontrollers 的出現，他開始開發更精簡、更快速的微控制器神經網路替代方案，並將許多傳統的機器學習演算法，如決策樹 (Decision Tree)、隨機森林 (Random Forest) 和邏輯回歸 (Logistic Regression)，從 Python 移植到獨立於硬體的 C++，以便能夠部署到任何微控制器上。如今，他繼續專注於開發 TinyML 工具和教程，並利用他的低代碼庫和無代碼的在線平台，如 Edge Impulse。
