Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of ma...
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| Formato: | Online |
| Idioma: | inglês |
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Apress
2021
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| Acesso em linha: | 27338 |
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| _version_ | 1869519326961205248 |
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| author | Mariette Awad Rahul Khanna |
| author_browse | Mariette Awad Rahul Khanna |
| author_facet | Mariette Awad Rahul Khanna |
| author_sort | Mariette Awad |
| collection | Directory of Open Access Books |
| description | Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning. |
| format | Online |
| id | doab-20.500.12854ir-45904 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Apress |
| publisherStr | Apress |
| record_format | ojs |
| spelling | doab-20.500.12854ir-459042023-12-20T18:40:45Z Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers Mariette Awad Rahul Khanna QA75.5-76.95 bic Book Industry Communication::U Computing & information technology::UY Computer science Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning. 2021-02-11T12:10:43Z 2021-02-11T12:10:43Z 2018-07-20 16:51:38 2015 book 27338 0 9781430259893 9781430259909 https://directory.doabooks.org/handle/20.500.12854/45904 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.apress.com/gb/book/9781430259893?wt_mc=ThirdParty.SpringerLink.3.EPR653.About_eBook#otherversion=9781430259909 https://link.springer.com/book/10.1007/978-1-4302-5990-9 Apress https://doi.org/10.1007/978-1-4302-5990-9 https://doi.org/10.1007/978-1-4302-5990-9 63d766d6-865d-4f92-bc33-b46fceac0c6a adcbbdbf-4704-4285-802a-68b3b9a5aa61 9781430259893 9781430259909 268 Intel open access |
| spellingShingle | QA75.5-76.95 bic Book Industry Communication::U Computing & information technology::UY Computer science Mariette Awad Rahul Khanna Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers |
| title | Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers |
| title_full | Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers |
| title_fullStr | Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers |
| title_full_unstemmed | Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers |
| title_short | Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers |
| title_sort | efficient learning machines theories concepts and applications for engineers and system designers |
| topic | QA75.5-76.95 bic Book Industry Communication::U Computing & information technology::UY Computer science |
| topic_facet | QA75.5-76.95 bic Book Industry Communication::U Computing & information technology::UY Computer science |
| url | 27338 |
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