IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
THE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaAp IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30pressopenFor your convenience Apress has placed some of the front matter material after the index. Please use the Bookmarks and Contents at a Glance links to access them.ApressContents at a GlanceAbout the Authors.........................................................XVAbout the Technical Reviewer IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30s...........................................xvllAcknowledgments..........................................................xixI Chapter 1: Machine LearnIT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
ing..............................................1Chapter 2: Machine Learning and Knowledge Discovery.....................19I Chapter 3: Support VectoTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaAp IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30odel..........................................81Chapter 6: Bioinspired Computing: Swarm Intelligence...................105I Chapter 7: Deep Neural Networks........................................127I Chapter 8: Cortical Algorithms.........................................149I Chapter 9: Deep Learning IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30...............................................167I Chapter 10: Multiobjective Optimization................................185Chapter 11: Machine LearIT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
ning In Action: Examples.......................209Index....................................................................241VCHAPTER 1Machine LearniTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaAp IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30e, and it was by learning the inner working of nature that man became a builder of machines.—Eric Hoffer, Reflections on the Human Condition.Machine learning (ML) is a branch of artificial intelligence that systematically applies algorithms to synthesize the underlying relationships among data and i IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30nformation. For example. ML systems can be trained on automatic speech recognition systems (such as IPhone’s Slrl) to convert acoustic Information InIT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
a sequence of speech data Into semantic structure expressed In the form of a string of words.ML Is already finding widespread uses In web search, ad pTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaAp IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30g data analytics, and many more applications. ML will play a decisive role in the development of a host of user-centric Innovations.ML owes Its burgeoning adoption to Its ability to characterize underlying relationships within large arrays of data in ways that solve problems in big data analytics, b IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30ehavioral pattern recognition, and information evolution. ML systems can moreover be trained to categorize the changing conditions of a process so asIT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
to model variations in operating behavior. As bodies ol knowledge evolve under the influence of new ideas and technologies, ML systems can identify diTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaAp IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30of ML Is to generalize the training experience (or examples) and output a hypothesis that estimates the target function. The generalization attribute of ML allows the system to perform well on unseen data instances by accurately predicting the future data. Unlike other optimization problems, ML does IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30 not have a well-defined function that can be optimized. Instead, training errors serve as a catalyst to test learning errors. The process of generaliIT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
zation requires classifiers that input discrete or continuous feature vectors and output a class.The goal of ML is to predict future events or scenariTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaAp IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30g explicitly programmed" (Samuel I9.59). Tie concluded that programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort. According to Tom M. Mitchell’s definition of MT.: "A computer program Is said to learn from experience r. with IT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30 respect to some class of tasks 1 and performance measure p, if its performance at tasks in T, as measured by p, improves with experience k.“ .Alan TuIT training efficient learning machines theories, concepts, and applications for engineers and system designers awad khanna 2015 04 30
ring’s seminal paper ( l uring 1950) introduced a benchmark standard for demonstrating machine Intelligence, such that a machine has to be IntelligentTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaApTHE EXPERT’S VOICE® IN MACHINE LEARNINGMachinesTheories, Concepts, and Applications for Engineers and System DesignersMarietta Awad and Rahul KhannaApGọi ngay
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