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Mathematical Aspects of Spin Glasses and Neural Networks

Aimed at graduates and potential researchers, this is a comprehensive introduction to the mathematical aspects of spin glasses and neural;

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Mathematical Aspects of Spin Glasses and Neural Networks

Aimed at graduates and potential researchers, this is a comprehensive introduction to the mathematical aspects of spin glasses and neural;

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Spin Glasses and Complexity

applications, from airline scheduling to neural networks. Spin Glasses and Complexity offers the most concise, engaging, and accessible introduction to;

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Statistical Physics of Spin Glasses and Information Processing

Spin glasses are magnetic materials. Statistical mechanics, a subfield of physics, has been a powerful tool to theoretically analyse;

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Perspectives On Spin Glasses

Presenting and developing the theory of spin glasses as a prototype for complex systems, this book is a rigorous and up-to-date;

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Principal Component Neural Networks

of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the;

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Deep Neural Networks in a Mathematical Framework

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools;

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Mathematical Perspectives on Neural Networks

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests;

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Mathematical Perspectives on Neural Networks

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests;

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Information-Theoretic Aspects of Neural Networks

Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory;

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Fuzzy Neural Intelligent Systems

Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development;

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Statistical Mechanics of Neural Networks

chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have;

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Advances in Neural Network Research

competition has migrated away from an emphasis on neural networks. IJCNN has embraced the proliferation of spin-off and related fields (see the topic;

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Neural Networks Theory

This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory;

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Artificial Neural Networks in Biological and Environmental Analysis

, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and;

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Artificial Neural Networks in Biological and Environmental Analysis

, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and;

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Deep Learning from Scratch Building with Python from First Principles

provides: Extremely clear and thorough mental models-accompanied by working code examples and mathematical explanations-for understanding neural;

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Differential Equations and Applications: Recent Advances

Systems, Mathematical Modelling and Qualitative Theory. Different aspects such as Existence, Stability, Controllability, Viscosity and Numerical;

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Parallel Architectures for Artificial Neural Networks

This excellent reference for all those involved in neural networks research and application presents, in a single text, the necessary;

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Neural Networks for Optimization and Signal Processing

also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer;

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Wavelet Neural Networks

reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural;

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Modeling Neural Development

An important collection showing how computational and mathematical modeling can be used to study the complexities of neural development;

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Introduction to Deep Learning and Neural Networks with PythonT

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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Neural Networks and Analog Computation: Beyond the Turing Limit

The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer;

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Deep Learning Architectures

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both;

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Sensory Neural Networks

book also uses nonlinear mathematical techniques to analyze temporal and spatial behavior of models presented within the text. Sensory Neural;

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Revival: Sensory Neural Networks (1991)

book also uses nonlinear mathematical techniques to analyze temporal and spatial behavior of models presented within the text. Sensory Neural;

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