國立虎尾科技大學 |

Embedded machine learning with microcontrollers = applications on arduino boards /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Embedded machine learning with microcontrollers/ by Cem Ünsalan, Berkan Höke, Eren Atmaca.
其他題名:	applications on arduino boards /
作者:	Ünsalan, Cem.
其他作者:	Atmaca, Eren.
出版者:	Cham :Springer International Publishing : : 2025.,
面頁冊數:	xiii, 371 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Processor Architectures. -
電子資源:	https://doi.org/10.1007/978-3-031-69421-9
ISBN:	9783031694219

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

Embedded machine learning with microcontrollersapplications on arduino boards /[electronic resource] :by Cem Ünsalan, Berkan Höke, Eren Atmaca. - Cham :Springer International Publishing :2025. - xiii, 371 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 -- Training the Multi-layer Neural Network on the Microcontroller -- Convolution Neural Networks -- Recurrence in Neural Networks.

This textbook introduces basic and advanced embedded machine learning methods by exploring practical applications on Arduino 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 microcontroller properties easily, the material allows for fast implementation 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 embedded machine learning system design skills needed for today's job market; Thoroughly explains each concept and provides illustrated examples and projects; Includes sample codes, course slides, and a solutions manual.

ISBN: 9783031694219

Standard No.: 10.1007/978-3-031-69421-9doiSubjects--Topical Terms:

669787
Processor Architectures.

LC Class. No.: TJ223.P76 / U573 2025

Dewey Class. No.: 006.22

Embedded machine learning with microcontrollers = applications on arduino 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 -- Training the Multi-layer Neural Network on the Microcontroller -- Convolution Neural Networks -- Recurrence in Neural Networks.
520 $a This textbook introduces basic and advanced embedded machine learning methods by exploring practical applications on Arduino 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 microcontroller properties easily, the material allows for fast implementation 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 embedded machine learning system design skills needed for today's job market; Thoroughly explains each concept and provides illustrated examples and projects; Includes sample codes, course slides, and a solutions manual.
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