Description: Efficient Learning Machines Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Theories, Concepts, and Applications for Engineers and System Designers Author(s): Mariette Awad, Rahul Khanna Format: Paperback Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Germany Imprint: APress ISBN-13: 9781430259893, 978-1430259893 Synopsis 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.
Price: 27.83 GBP
Location: Aldershot
End Time: 2025-02-04T09:08:43.000Z
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Book Title: Efficient Learning Machines
Number of Pages: 268 Pages
Publication Name: Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
Language: English
Publisher: Springer-Verlag Berlin AND Heidelberg Gmbh & Co. KG
Item Height: 254 mm
Subject: Computer Science
Publication Year: 2015
Type: Textbook
Item Weight: 5091 g
Author: Mariette Awad, Rahul Khanna
Item Width: 178 mm
Format: Paperback
