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Machine Learning Models and Architectures for Biomedical Signal Processing
Engleski
363 b
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Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamentals concepts of machine learning techniques for bioinformatics in an interactive way. It investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and Field programmable gate arrays (FPGAs) or any hybrid system. This book will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques. Covers the hardware architecture implementation of machine learning algorithmsDiscusses the software implementation approach, efficient hardware of the machine learning application with FPGAPresents the major design challenges and research potential in machine learning techniques
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