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Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep;
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Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start;
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describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio;
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explores the cutting edge of deep learning studies. A subfield of machine learning, deep learning differs from conventional machine learning;
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This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars;
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learning methodologies on evolving accurate and precise early detection and medical diagnosis systems. Information presented in an;
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The role of deep learning for the analysis and learning of massive amounts of data from all aspects of daily-life has dramatically changed;
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If you are an active user of statistics to examine trends in your business, then using machine learning is your next best step. It uses;
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This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the;
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everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies;
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, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to;
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Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based;
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This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm;
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This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm;
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This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the;
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This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has;
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learning; deep learning and its applications; document analysis; intelligent information systems; swarm intelligence.;
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, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones;
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begins by considering KM practices, techniques, and methodologies in e-learning. These chapters explain how knowledge capture, retention;
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DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial;
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This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition;
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Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key Features Learn deep;
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Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic;
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This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise;
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This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise;
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with mobile and web-based learning environments. Blended Learning: Concepts, Methodologies, Tools, and Applications explores emerging trends;
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deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches;
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