Explainable machine learning models and architectures:
EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part o...
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| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ Beverly, MA
John Wiley & Sons, Inc.
2023
Hoboken, NJ Beverly, MA Scrivener Publishing 2023 |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781394185849/?ar |
| Summary: | EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. |
| Item Description: | Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on September 21, 2023) |
| Physical Description: | 1 Online-Ressource. |
| ISBN: | 9781394186570 1394186576 9781394186563 1394186568 9781394186556 139418655X 9781394185849 |
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| spelling | Explainable machine learning models and architectures edited by Suman Lata Tripathi and Mufti Mahmud Hoboken, NJ Beverly, MA John Wiley & Sons, Inc. 2023 Hoboken, NJ Beverly, MA Scrivener Publishing 2023 ©2023 1 Online-Ressource. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on September 21, 2023) EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. Machine learning Computer architecture Apprentissage automatique Ordinateurs ; Architecture Tripathi, Suman Lata HerausgeberIn edt Mahmud, Mufti HerausgeberIn edt |
| spellingShingle | Explainable machine learning models and architectures Machine learning Computer architecture Apprentissage automatique Ordinateurs ; Architecture |
| title | Explainable machine learning models and architectures |
| title_auth | Explainable machine learning models and architectures |
| title_exact_search | Explainable machine learning models and architectures |
| title_full | Explainable machine learning models and architectures edited by Suman Lata Tripathi and Mufti Mahmud |
| title_fullStr | Explainable machine learning models and architectures edited by Suman Lata Tripathi and Mufti Mahmud |
| title_full_unstemmed | Explainable machine learning models and architectures edited by Suman Lata Tripathi and Mufti Mahmud |
| title_short | Explainable machine learning models and architectures |
| title_sort | explainable machine learning models and architectures |
| topic | Machine learning Computer architecture Apprentissage automatique Ordinateurs ; Architecture |
| topic_facet | Machine learning Computer architecture Apprentissage automatique Ordinateurs ; Architecture |
| work_keys_str_mv | AT tripathisumanlata explainablemachinelearningmodelsandarchitectures AT mahmudmufti explainablemachinelearningmodelsandarchitectures |