Explainable Machine Learning Models and Architectu res

Name: Explainable Machine Learning Models and Architectu res
Brand: John Wiley & Sons Inc
SKU: 9781394185849
Price: 66043 HUF
Availability: InStock

Szerző: Mufti Mahmud

Nyelv:

Kötés: Kemény kötésű

Kiadó: John Wiley & Sons Inc

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EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardwa...

Információk a könyvről

Szerző

Mufti Mahmud

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2024

oldal

272

EAN

9781394185849

Enbook ID

43729701

Kiadó

John Wiley & Sons Inc

Súly

531

Teljes leírás

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.

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Explainable Machine Learning Models and Architectu res

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