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Tiny machine learning quickstartmach...

Salerno, Simone.

Tiny machine learning quickstartmachine learning for Arduino microcontrollers /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Tiny machine learning quickstartby Simone Salerno.
Reminder of title:	machine learning for Arduino microcontrollers /
Author:	Salerno, Simone.
Published:	Berkeley, CA :Apress :2025.
Description:	xx, 326 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/979-8-8688-1294-1
ISBN:	9798868812941$q(electronic bk.)

Tiny machine learning quickstartmachine learning for Arduino microcontrollers /
Salerno, Simone.

Tiny machine learning quickstartmachine learning for Arduino microcontrollers /[electronic resource] :by Simone Salerno. - Berkeley, CA :Apress :2025. - xx, 326 p. :ill. (some col.), digital ;24 cm. - Maker innovations series,2948-2550. - Maker innovations series..

Chapter 1: Introduction to Tiny Machine Learning -- Chapter 2: Tabular data classification -- Chapter 3: Tabular data regression -- Chapter 4: Time series classification with Edge Impulse -- Chapter 5: Time series classification without Edge Impulse -- Chapter 6: Audio Wake Word detection with Edge Impulse -- Chapter 7: Object detection with Edge Impulse -- Chapter 8: TensorFlow for Microcontrollers from scratch.

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. You will: 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.

ISBN: 9798868812941$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1294-1doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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505 0 $a Chapter 1: Introduction to Tiny Machine Learning -- Chapter 2: Tabular data classification -- Chapter 3: Tabular data regression -- Chapter 4: Time series classification with Edge Impulse -- Chapter 5: Time series classification without Edge Impulse -- Chapter 6: Audio Wake Word detection with Edge Impulse -- Chapter 7: Object detection with Edge Impulse -- Chapter 8: TensorFlow for Microcontrollers from scratch.
520 $a 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. You will: 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.
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