Cognitive computing and deep learning

This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based t

Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday de

This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy is

How deep learning -- from Google Translate to driverless cars to personal cognitive assistants -- is changing our lives and transforming every sector of the economy.The deep learning revolution has br

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex p

A comprehensive guide to learning technologies that unlock the value in big dataCognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and der

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent ope

這本書融合了大數據理論與智能雲(物聯網)的新技術。數據分析師和計算機科學家必須學會如何有效地使用雲和物聯網來發現新的知識，進而才能明智地做出重要決策。本書旨在縮短這些學習方向之間的差距,並鼓勵數據科學家和雲計算科學家之間的相互學習與合作。書中將大數據集成理論、雲設計原則、物聯網傳感、機器學習、數據分析、Hadoop和Spark編程等融為一體，目標是在物聯網傳感、機器學習和分析系統的支持下，完成有效

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database m

Deep learning is a committed approach to learning. It is a process of constructing and interpreting new knowledge in light of prior cognitive structures and experiences, which can be applied in new, u

Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that

Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that

Educator and neuroscientist Dr. Jared Cooney Horvath reveals why digital tools in school consistently undermine learning--and what parents, teachers, and schools can do to push back with purpose. Our children are struggling. Schools, once alive with deep learning born of human connection, are now dominated by screens and digital tools. The result is unmistakable: falling performance, fractured attention, and the slow erosion of rigorous thought. For the first time in the history of standardized cognitive measurement, children are consistently scoring lower on key measures of cognitive development. We were told that classroom technology was progress. It wasn't. In The Digital Delusion, neuroscientist and educator Dr. Jared Cooney Horvath shows how the widespread use of laptops, tablets, and educational software in classrooms is quietly undermining how children learn, think, and develop. Drawing on decades of neuroscience and education research, Horvath dismantles the core myths driving

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deplo

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fail