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Explainable Machine Learning Models and Architectures

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Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
Explainable Machine Learning Models and Architectures delves into the development and application of machine learning models that prioritise transparency and interpretability. The book explores various techniques and architectures that enable users to understand and trust the decisions made by complex algorithms. It combines theoretical insights with practical case studies, making it valuable for both researchers and practitioners in the computing and technology fields.
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Format: Hardback
$39099
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

This book may appeal to you if you are fascinated by the inner workings of machine learning models and crave a deeper understanding of how they can be made more transparent and comprehensible. It's ideal for those passionate about demystifying complex algorithms and enhancing their application in technology and computing.

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Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

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 requires 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, which further leads to more IC area. Trade-offs between power consumption, delay, and IC area are always a concern for 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 significant opportunities for improvements in terms of accuracy, speed, computational powers, and overall power consumption.

This book deals with 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.

Book Details

INFORMATION

ISBN: 9781394185849

Publisher: John Wiley & Sons Inc

Format: Hardback

Date Published: 06 September 2023

Country: United States

Imprint: Wiley-Scrivener

Contributors:

  • Edited by Suman Lata Tripathi
  • Edited by Mufti Mahmud
  • Edited by Suman Lata Tripathi

Audience: Professional and scholarly

DIMENSIONS

Weight: 531g

Pages: 272

Collections

Computing & Technology Education & Reference Non-Fiction

About the Author

Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.

Mufti Mahmud, PhD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees.

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