{"product_id":"9781430259909","title":"Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers","description":"\u003cp\u003e    \u003cem\u003e\u003c\/em\u003e\u003c\/p\u003e\u003cp\u003eMachine 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. \u003cem\u003eEfficient Learning Machines\u003c\/em\u003e explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. \u003c\/p\u003e\u003cp\u003eMariette 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 \u003cem\u003eEfficient Learning Machines\u003c\/em\u003e 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.\u003c\/p\u003e\u003cp\u003eAdvances 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.\u003c\/p\u003e\u003cp\u003eNature 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e         What you’ll learn\u003cp\u003e\u003cem\u003eEfficient Learning Machines\u003c\/em\u003e systematically guides readers to an understanding and practical mastery of the following techniques:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ethe machine learning techniques most commonly used to solve complex real-world problems\u003c\/li\u003e\n\u003cli\u003erecent improvements to classification and regression techniques\u003c\/li\u003e\n\u003cli\u003ethe application of bio-inspired techniques to real-life problems\u003c\/li\u003e\n\u003cli\u003enew deep learning techniques that exploit advances in computing performance and storage\u003c\/li\u003e\n\u003cli\u003emachine learning techniques for solving multi-objective optimization problems with nondominated methods that minimize distance to the Pareto front\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003e\u003cem\u003eEfficient Learning Machines \u003c\/em\u003eequips engineers, students of engineering, and system designers with the knowledge and guidance to design and create new and more efficient machine learning systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eTable of Contents\u003cp\u003eChapter 1. Machine Learning\u003c\/p\u003e\u003cp\u003eChapter 2. Machine Learning and Knowledge Discovery\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 3. Support Vector Machines for Classification\u003c\/p\u003e\u003cp\u003eChapter 4. Support Vector Regression\u003c\/p\u003e\u003cp\u003eChapter 5. Hidden Markov Model\u003c\/p\u003e\u003cp\u003eChapter 6. Bio-Inspired Computing: Swarm Intelligence\u003c\/p\u003e\u003cp\u003eChapter 7. Deep Neural Networks\u003c\/p\u003e\u003cp\u003eChapter 8. Cortical Algorithms\u003c\/p\u003e\u003cp\u003eChapter 9. Deep Learning\u003c\/p\u003e\u003cp\u003eChapter 10. Multiobjective Optimization\u003c\/p\u003e\u003cp\u003eChapter 11. Machine Learning in Action: Examples\u003c\/p\u003e","brand":"Apress","offers":[{"title":"Default Title","offer_id":47122967429360,"sku":"9781430259909","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781430259909_p0.jpg?v=1763750380","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781430259909","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}