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of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications;
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industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear;
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This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered;
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deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be;
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Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning;
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the condition monitoring of electrical equipment. With chapters discussing the fundamentals of machine learning and data analytics, the book;
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major issue in today's digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to;
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Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;
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Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;
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information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail;
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information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail;
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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization;
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organized in topical sections on information and customer analytics; visual data science; machine and deep learning; big data analytics;
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This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The;
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introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics;
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the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type;
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This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world;
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, big data analytics, smart computing, smart cities, Internet of things (IoT), data communication, cloud computing, machine learning algorithms;
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networks such as CNNs, RNNs, and LSTM with the help of practical examples Deploy a trained deep learning network on real-world data Who this;
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; Deep Learning based Biomedical Data Analytics; NLP based Learning from Unstructured Data; Biomedical Data Modelling and Prediction.;
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informatics, business data analytics, data from Internet of Things and deep learning applications.;
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Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer;
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and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural;
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insights from your data. You'll explore the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial;
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rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that artificial intelligence is included;
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rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that artificial intelligence is included;
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Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore;
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