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Neural Networks for Complete Beginners

. The back-propagation algorithm, which is used for training artificial neural networks, is discussed. The book also guides you through the;

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Practical Deep Learning

Deep Learning for Complete Beginners: A Python-Based Introduction is for complete beginners in machine learning. It introduces fundamental;

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Advanced Models of Neural Networks

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by;

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Principles Of Artificial Neural Networks (3rd Edition)

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different;

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Learning in Energy-Efficient Neuromorphic Computing - Algorithm and Architecture Co-Design

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural;

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Principles Of Artificial Neural Networks (2nd Edition)

The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering;

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Neural Networks with Keras Cookbook

Implement neural network architectures by building them from scratch for multiple real-world applications. Key Features From scratch, build;

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New Developments In Artificial Neural Networks Research

in chromatography and spectroscopy; heterogeneous neural networks and tuning differential evolution for artificial neural networks.;

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Focus on Artificial Neural Networks

of pharamceutical microemulsions; massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images;

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Introduction to Neural Networks for Java, Second Edition

"Introduction to Neural Networks for Java" introduces the Java programmer to the world of Neural Networks and Artificial Intelligence;

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Evolutionary Machine Learning Techniques

methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning;

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Evolutionary Machine Learning Techniques

methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning;

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Simulating Neural Networks with Mathematica

will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural;

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Spatially Explicit Hyperparameter Optimization for Neural Networks

depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the;

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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

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are;

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Neural Networks with R

to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background;

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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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Blind Equalization in Neural Networks

simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists.;

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Neural Networks

at ways of implementing a neural network, including electronic and optical systems This book is suitable for undergraduates from Computer;

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Information-Theoretic Aspects of Neural Networks

neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric;

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Progress in Neural Networks

This is a study of the use of neural networks for machine vision. It is part of a series which reviews research in natural and synthetic;

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Principles Of Artificial Neural Networks

course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers;

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Neural Networks for Applied Sciences and Engineering

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering;

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Learning from Data

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a;

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