國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Tiny machine learning quickstartmach...

Salerno, Simone.

Tiny machine learning quickstartmachine learning for Arduino microcontrollers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Tiny machine learning quickstartby Simone Salerno.
其他題名:	machine learning for Arduino microcontrollers /
作者:	Salerno, Simone.
出版者:	Berkeley, CA :Apress :2025.
面頁冊數:	xx, 326 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	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

Tiny machine learning quickstartmachine learning for Arduino microcontrollers /
LDR:02901nmm a2200337 a 4500 001 678256
003 DE-He213
005 20250416130214.0
006 m d
007 cr nn 008maaau
008 251021s2025 cau s 0 eng d
020 $a 9798868812941$q(electronic bk.)
020 $a 9798868812934$q(paper)
024 7 $a 10.1007/979-8-8688-1294-1 $2 doi
035 $a 979-8-8688-1294-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UB $2 bicssc
072 7 $a COM067000 $2 bisacsh
072 7 $a UBM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S163 2025
100 1 $a Salerno, Simone. $3 991696
245 1 0 $a Tiny machine learning quickstart $h [electronic resource] : $b machine learning for Arduino microcontrollers / $c by Simone Salerno.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xx, 326 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Maker innovations series, $x 2948-2550
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.
650 0 $a Machine learning. $3 188639
650 0 $a Arduino (Programmable controller) $3 670955
650 1 4 $a Maker. $3 913111
650 2 4 $a Machine Learning. $3 833608
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Maker innovations series. $3 956498
856 4 0 $u https://doi.org/10.1007/979-8-8688-1294-1
950 $a Professional and Applied Computing (SpringerNature-12059)