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Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, by Mariette Awad, Rahul Khanna
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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.
What you’ll learn
Efficient Learning Machines systematically guides readers to an understanding and practical mastery of the following techniques:
- the machine learning techniques most commonly used to solve complex real-world problems
- recent improvements to classification and regression techniques
- the application of bio-inspired techniques to real-life problems
- new deep learning techniques that exploit advances in computing performance and storage
- machine learning techniques for solving multi-objective optimization problems with nondominated methods that minimize distance to the Pareto front
Who this book is for
Efficient Learning Machines equips engineers, students of engineering, and system designers with the knowledge and guidance to design and create new and more efficient machine learning systems.
- Sales Rank: #1781533 in eBooks
- Published on: 2015-04-30
- Released on: 2015-04-30
- Format: Kindle eBook
About the Author
Rahul Khanna is a platform architect at Intel Corporation involved in development of energy-efficient algorithms. Over the past 17 years he has worked on server system software technologies, including platform automation, power/thermal optimization techniques, reliability, optimization, and predictive methodologies. He has authored numerous technical papers and book chapters in the areas related to energy optimization, platform wireless interconnects, sensor networks, interconnect reliability, predictive modeling, motion estimation, and security. He holds 27 patents. He is the co-inventor of the Intel IBIST methodology for High-Speed interconnect testing. His research interests include machine learning-based power/thermal optimization algorithms, narrow-channel high-speed wireless interconnects, and information retrieval in dense sensor networks. Rahul is member of IEEE and the recipient of three Intel Achievement Awards for his contributions in areas related to advancements of platform technologies. He is the author of A Vision for Platform Autonomy: Robust Frameworks for Systems.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Good Introduction
By Ray
Good introduction to ML and Algorithms. The book is more geared towards understanding the concepts, rather than real world implementations, eventually you have to look at other books for deep dive.
0 of 0 people found the following review helpful.
For knowledge refresh
By Jian Xu
Good coverage with depth. Need some background to read. Good to have with kindle unlimited. You can skip as chapters don't have strong dependency.
2 of 3 people found the following review helpful.
Four Stars
By Rodrigo Sandoval
Good introductory text.
See all 3 customer reviews...
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