國立虎尾科技大學 |

Embedded machine learning with microcontrollers = applications on STM32 development boards /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Embedded machine learning with microcontrollers/ by Cem Ünsalan, Berkan Höke, Eren Atmaca.
Reminder of title:	applications on STM32 development boards /
remainder title:	STM32
Author:	Ünsalan, Cem.
other author:	Höke, Berkan.
Published:	Cham :Springer International Publishing : : 2025.,
Description:	xiv, 403 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Microcontrollers. -
Online resource:	https://doi.org/10.1007/978-3-031-70912-8
ISBN:	9783031709128

Embedded machine learning with microcontrollers = applications on STM32 development boards /
Ünsalan, Cem.

Embedded machine learning with microcontrollersapplications on STM32 development boards /[electronic resource] :STM32by Cem Ünsalan, Berkan Höke, Eren Atmaca. - Cham :Springer International Publishing :2025. - xiv, 403 p. :ill., digital ;24 cm.

Introduction -- Hardware to Be Used in the Book -- Software to Be Used in the Book -- Data Acquisition From Sensors -- Introduction to Machine Learning -- Classification -- Regression -- Clustering -- The Tensorflow Platform and Keras API -- Fundamentals of Neural Networks -- Embedding the Neural Network Model to the Microcontroller -- Multi-layer Neural Networks -- Convolutional Neural Networks -- Recurrent Neural Networks -- ARM CMSIS NN Software Library -- Appendix. STM32 Board Pin Usage Tables.

This textbook introduces basic and advanced embedded machine learning methods by exploring practical applications on STM32 development boards. By covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers and embedded machine learning systems. Following the learning-by-doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples, providing them with the design and implementation skills needed for a competitive job market. By utilizing a programming environment that enables students to reach and modify low-level microcontroller properties, the material allows for more control of the developed system. Students are guided in implementing machine learning methods to be deployed and tested on microcontrollers throughout the book, with the theory behind the implemented methods also emphasized. Sample codes and real-world projects are available for readers and instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists. Teaches the embedded system design skills needed for today's job market; Thoroughly explains each concept and provides illustrated examples and projects; Includes sample codes and course slides and a solutions manual.

ISBN: 9783031709128

Standard No.: 10.1007/978-3-031-70912-8doiSubjects--Topical Terms:

596424
Microcontrollers.

LC Class. No.: TJ223.P76

Dewey Class. No.: 006.22

Embedded machine learning with microcontrollers = applications on STM32 development boards /
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505 0 $a Introduction -- Hardware to Be Used in the Book -- Software to Be Used in the Book -- Data Acquisition From Sensors -- Introduction to Machine Learning -- Classification -- Regression -- Clustering -- The Tensorflow Platform and Keras API -- Fundamentals of Neural Networks -- Embedding the Neural Network Model to the Microcontroller -- Multi-layer Neural Networks -- Convolutional Neural Networks -- Recurrent Neural Networks -- ARM CMSIS NN Software Library -- Appendix. STM32 Board Pin Usage Tables.
520 $a This textbook introduces basic and advanced embedded machine learning methods by exploring practical applications on STM32 development boards. By covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers and embedded machine learning systems. Following the learning-by-doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples, providing them with the design and implementation skills needed for a competitive job market. By utilizing a programming environment that enables students to reach and modify low-level microcontroller properties, the material allows for more control of the developed system. Students are guided in implementing machine learning methods to be deployed and tested on microcontrollers throughout the book, with the theory behind the implemented methods also emphasized. Sample codes and real-world projects are available for readers and instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists. Teaches the embedded system design skills needed for today's job market; Thoroughly explains each concept and provides illustrated examples and projects; Includes sample codes and course slides and a solutions manual.
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