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Handbook of machine learning for computational optimization : applications and case studies / edited by Vishal Jain, Sapna Juneja, Abhinav Juneja, and Ramani Kannan.

Contributor(s): Material type: TextTextSeries: Demystifying technologies for computational excellence: moving towards society 5.0Edition: First editionDescription: xiv, 280 pages : illustrationsISBN:
  • 9780367685423
  • 9780367685454
Subject(s): DDC classification:
  • 006.31 23 HAN
Summary: "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 which 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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Holdings
Item type Current library Home library Collection Call number Status Date due Barcode
Book Book Dept. of Futures Studies General Stacks Dept. of Futures Studies Non-fiction 006.31 HAN (Browse shelf(Opens below)) Available DFS4479

Includes bibliographical references and index.

"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 which 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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