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Mathematical statistics --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- factoranalyse --- grafische vormgeving --- informatica --- robots
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Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications
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Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- beeldverwerking --- ICT (informatie- en communicatietechnieken) --- IR (information retrieval) --- grafische vormgeving --- multimedia --- informatiesystemen --- computernetwerken
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Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- ICT (informatie- en communicatietechnieken) --- grafische vormgeving --- informatica --- multimedia --- informatiesystemen --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)
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This is a new, revised and updated edition of the original book by Isaac Amidror. It presents the most comprehensive and methodical work on the theory of the moiré phenomenon, providing a full general-purpose and application-independent exposition of this fascinating effect. Based on the Fourier theory, it leads the reader through the various phenomena which occur in the superposition of repetitive layers, both in the image and in the spectral domains. The first chapters of the book present the basic theory which covers the superposition of monochrome, periodic layers. In later chapters the theory is extended to the even more interesting cases of polychromatic moirés and moirés between repetitive, non-periodic layers. Throughout the whole text the book favours a pictorial, intuitive approach which is supported by mathematics, and the discussion is accompanied by a large number of figures and illustrative examples, some of which are visually attractive and even spectacular. This book is intended for students, scientists, engineers and any readers who wish to widen their knowledge of the moiré effect. It also offers a beautiful demonstration of the Fourier theory and its relationship with other fields of mathematics and science. The prerequisite mathematical background is limited to an elementary familiarity with calculus and with the Fourier theory.
Harmonic analysis. Fourier analysis --- Mathematics --- Electronics and optics of solids --- Computer. Automation --- beeldverwerking --- Fourieranalyse --- toegepaste wiskunde --- optica
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Neuropathology --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- beeldverwerking --- neurologie --- grafische vormgeving --- signaalverwerking
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