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Mathematical Perspectives on Neural Networks

approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background;

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Mathematical Perspectives on Neural Networks

approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background;

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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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Neural Networks for Optimization and Signal Processing

A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but;

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Statistical Mechanics of Neural Networks

skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying;

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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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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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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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Computational Mechanics with Neural Networks

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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Computational Mechanics with Neural Networks

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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Introduction to Deep Learning and Neural Networks with PythonT

explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonT examples, teaching you how to use the;

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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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Neural Networks for Applied Sciences and Engineering

. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic;

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Neural Networks and Analog Computation: Beyond the Turing Limit

constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the;

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Neural Networks In Financial Engineering - Proceedings Of The Third International Conference On Neural Networks In The Capital Markets

markets. Neural networks can be harnessed for this task as they bear many mathematical similarities to the nonlinear econometric models used to;

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Stochastic Models of Neural Networks

random processes from experience, on the basis of recent developments on this subject. The mathematical frameworks on which the theory is founded;

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Neural Networks and Qualitative Physics

This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative;

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Neural Networks And Qualitative Physics

This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative;

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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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Advances in Neural Networks - ISNN 2012

The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012;

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Advances in Neural Networks - ISNN 2012

The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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Make Your Own Neural Network

understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually;

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Artificial Neural Networks

model based on biological (brain) neural networks. ANN is an adaptive system that changes its structure based on external or internal;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book;

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Parallel Architectures for Artificial Neural Networks

aspects of parallel implementation for all major artificial neural network models. The book details implementations on varoius processor;

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