Johnson I Agbinya – författare
717 kr
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811 kr
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719 kr
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The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors.
Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications.
The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.
This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
714 kr
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584 kr
Läs direkt efter köp
811 kr
Läs direkt efter köp
719 kr
Läs direkt efter köp
The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors.
Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications.
The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.
This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
714 kr
Läs direkt efter köp
584 kr
Läs direkt efter köp
714 kr
Läs direkt efter köp
714 kr
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1 006 kr
Läs direkt efter köp
1 902 kr
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1 006 kr
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Rapid deployment and acceptance of broadband networks, including the 802.11 a/b/g, 3G cellular networks, WiMAX, and emerging 4G cellular IP networks, have sparked a growing reliance on voice over IP and the quickly emerging IP TV and Mobile TV. Providing the necessary background and technical understanding to stay abreast of and even ahead of the IP trend, IP Communications and Services for NGN explores IP development for the delivery of next generation mobile services.
Packed with detailed illustrations, this cutting-edge reference examines the primary IP protocols (IPv4 and IPv6), real-time protocols, and three major IP services (VoIP, IPTV, and Mobile TV). It clearly explains the different architectures of fixed, mobile, and wireless networks along with the major advantages and disadvantages of each. It includes coverage of the latest in:
The VoIP MarketSCTP and Vertical HandoffRSVP: Resource Reservation ProtocolMPLS: MultiProtocol Label SwitchingSIP: Session Initiation ProtocolIMS: IP Multimedia SubsystemRTSP: Real-Time Streaming ProtocolRTP: Real-Time Transport ProtocolIPTV System Architectures and IPTV System Descriptions
With a detailed listing of commonly used acronyms, along with a clear description of the role IP is likely to play in the development of next generation mobile services, this book provides educators, industry practitioners, regulators, and subscribers with the ideal starting point for developing the understanding required to deploy, train, and use IP services effectively and efficiently.
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