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Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
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  • Title: Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
  • Author(s) Mariette Awad, Rahul Khanna
  • Publisher: Apress OPEN; eBook (Creative Commons Licensed)
  • License(s): CC BY 4.0
  • Hardcover/Paperback: 268 pages
  • eBook: PDF and ePub
  • Language: English
  • ISBN-10: 1430259892
  • ISBN-13: 978-1430259893 (Print) 978-1430259909 (Online)
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Book 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.

Authors 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.

About the Authors
  • Rahul Khanna is a platform architect at Intel Corporation involved in development of energy-efficient algorithms.
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