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Neural Networks with R

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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Advanced Deep Learning with R

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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Deep Learning with R for Beginners

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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Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults

conventional techniques of fault analysis. The authors provide insight into artificial neural networks and their applications, with illustrations, for;

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Fuzzy Logic and Neural Networks

In this title, fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic;

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R Deep Learning Projects

neural networks with helpful tips, tricks, and best practices Book DescriptionR is a popular programming language used by statisticians and;

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Computational Intelligence

the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB(R) exercises and applications in each chapter;

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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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Simulating Neural Networks with Mathematica

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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Deep Learning from Scratch Building with Python from First Principles

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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Complex-Valued Neural Networks

This book covers the hot topics and applications surrounding complex-valued neural networks. It demonstrates advanced theories with a wide;

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Fundamentals Neural NetworksUnited State

An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind;

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Complex-valued Neural Networks

-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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Blind Equalization in Neural Networks

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

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

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Exploring Neural Networks with C#

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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Neural Network Programming with Python

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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Applied Neural Networks for Signal Processing

The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas. Applied Neural;

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Advanced Models of Neural Networks

with neural networks and dynamical systems theory.;

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Exploring Neural Networks with C#

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

/>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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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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Artificial Neural Networks in Hydrology

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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Neural Network Programming with Java

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

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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