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in neural networks with R, to implement them in your applications * Build smart systems using the power of deep learning * Real-world case studies;
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branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will;
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Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the;
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conventional techniques of fault analysis. The authors provide insight into artificial neural networks and their applications, with illustrations, for;
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In this title, fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic;
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neural networks with helpful tips, tricks, and best practices Book DescriptionR is a popular programming language used by statisticians and;
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the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB(R) exercises and applications in each chapter;
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Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book;
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implement and experiment with neural network architectures. *Addresses a major topic related to neural networks in each chapter, or a specific type;
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With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;
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This book covers the hot topics and applications surrounding complex-valued neural networks. It demonstrates advanced theories with a wide;
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An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind;
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-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as;
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The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms;
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Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;
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Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;
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Networks with C# presents the important properties of neural networks-while keeping the complex mathematics to a minimum. Explaining how to build;
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network works is also explored. You are then guided on how to implement neural networks with Neupy, another Python library.The following topics;
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The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas. Applied Neural;
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with neural networks and dynamical systems theory.;
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Networks with C# presents the important properties of neural networks while keeping the complex mathematics to a minimum. Explaining how to build;
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/>The Essence of Neural Networks is designed to be a first course on neural networks for undergraduate students, with the mathematics contained;
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This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other;
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This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other;
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dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a;
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This book is an exploration of neural networks and how to implement them in Java. First, the reader is guided so as to understand what;
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Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to;
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