Embedded machine learning with microcontrollers: applications on arduino boards
Ünsalan, Cem
Embedded machine learning with microcontrollers: applications on arduino boards - Cham Springer 2025 - xiii, 371 p.
Table of contents:
Front Matter
Pages i-xiii
Download chapter PDF
Introduction
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 1-6
Hardware to Be Used in the Book
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 7-15
Software to Be Used in the Book
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 17-23
Data Acquisition from Sensors
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 25-49
Introduction to Machine Learning
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 51-81
Classification
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 83-127
Regression
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 129-163
Clustering
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 165-182
The TensorFlow Platform and Keras API
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 183-197
Fundamentals of Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 199-231
Multilayer Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 233-253
Embedding the Neural Network Model to the Microcontroller
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 255-284
Convolutional Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 285-321
Recurrence in Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 323-357
[https://link.springer.com/book/10.1007/978-3-031-69421-9]
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
(https://link.springer.com/book/10.1007/978-3-031-69421-9)
9783031694202
Data science
Network---analysis
006.31 / UNS
Embedded machine learning with microcontrollers: applications on arduino boards - Cham Springer 2025 - xiii, 371 p.
Table of contents:
Front Matter
Pages i-xiii
Download chapter PDF
Introduction
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 1-6
Hardware to Be Used in the Book
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 7-15
Software to Be Used in the Book
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 17-23
Data Acquisition from Sensors
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 25-49
Introduction to Machine Learning
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 51-81
Classification
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 83-127
Regression
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 129-163
Clustering
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 165-182
The TensorFlow Platform and Keras API
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 183-197
Fundamentals of Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 199-231
Multilayer Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 233-253
Embedding the Neural Network Model to the Microcontroller
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 255-284
Convolutional Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 285-321
Recurrence in Neural Networks
Cem Ünsalan, Berkan Höke, Eren Atmaca
Pages 323-357
[https://link.springer.com/book/10.1007/978-3-031-69421-9]
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
(https://link.springer.com/book/10.1007/978-3-031-69421-9)
9783031694202
Data science
Network---analysis
006.31 / UNS