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Full Description
This textbook introduces basic embedded machine learning methods by exploring practical applications on STM32 development boards. Covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers, microprocessor systems, and embedded systems. Following the learning by doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples that will provide 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 will be 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 course slides are available for readers and instructors, and a solutions manual is available to instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists.
Contents
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.
