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Embedded machine learning with microcontrollersapplications on STM32 development boards /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Embedded machine learning with microcontrollersby Cem Ünsalan, Berkan Höke, Eren Atmaca.
其他題名:	applications on STM32 development boards /
其他題名:	STM32
作者:	Ünsalan, Cem.
其他作者:	Höke, Berkan.
出版者:	Cham :Springer International Publishing :2025.
面頁冊數:	xiv, 403 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Microcontrollers.
電子資源:	https://doi.org/10.1007/978-3-031-70912-8
ISBN:	9783031709128$q(electronic bk.)

Embedded machine learning with microcontrollersapplications 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$q(electronic bk.)

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

284049
Microcontrollers.

LC Class. No.: TJ223.P76

Dewey Class. No.: 006.22

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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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