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Handbook Of Machine Learning For Computational Optimization Applications And Case Studies Demystifying Technologies For Computational Excellence 1st Edition Vishal Jain Editor

  • SKU: BELL-34528212
Handbook Of Machine Learning For Computational Optimization Applications And Case Studies Demystifying Technologies For Computational Excellence 1st Edition Vishal Jain Editor
$ 31.00 $ 45.00 (-31%)

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Handbook Of Machine Learning For Computational Optimization Applications And Case Studies Demystifying Technologies For Computational Excellence 1st Edition Vishal Jain Editor instant download after payment.

Publisher: CRC Press
File Extension: PDF
File size: 16.12 MB
Pages: 280
Author: Vishal Jain (editor), Sapna Juneja (editor), Abhinav Juneja (editor), Ramani Kannan (editor)
ISBN: 9780367685423, 0367685426
Language: English
Year: 2021
Edition: 1

Product desciption

Handbook Of Machine Learning For Computational Optimization Applications And Case Studies Demystifying Technologies For Computational Excellence 1st Edition Vishal Jain Editor by Vishal Jain (editor), Sapna Juneja (editor), Abhinav Juneja (editor), Ramani Kannan (editor) 9780367685423, 0367685426 instant download after payment.

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques.

This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making.

Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.

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