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Simulating Neural Networks with Mathematica

of neural network architecture. *Contains exercises, suggested projects, and supplementary reading lists with each chapter. *Includes Mathematica;

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Network Models for Control and Processing

experimentation and physical mechanisms with computer-assisted and hardware models of biological neural networks and neurons; improving processes for;

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Dynamical Systems with Applications using Mathematica (R)

This book provides an introduction to the theory of dynamical systems with the aid of the Mathematica® computer algebra package. The;

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Supervised and Unsupervised Pattern Recognition

research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy;

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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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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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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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Primer Of Nmr Theory

Presents the theory of NMR enhanced with Mathematica (c) notebooks Provides short, focused chapters with brief explanations;

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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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Neural-Network Simulation of Strongly Correlated Quantum Systems

solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance;

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Neural-Network Simulation of Strongly Correlated Quantum Systems

solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance;

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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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Advanced Models of Neural Networks

with neural networks and dynamical systems theory.;

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Operator Calculus On Graphs

communications, neural networks, electrical circuits, transportation, and the world wide web. Examples are put forward in Mathematica throughout the book;

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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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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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Complex-valued Neural Networks

In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum;

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