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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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Learning from Data

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

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Principal Component Analysis Networks and Algorithms

algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses;

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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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Statistics and Neural Networks

Recent years have seen a growing awareness of the interface between statistical research and recent advances in neural computing and;

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Handbook Of Chemometrics And Qualimetrics

as neural networks, genetic algorithms and latent variable based methods.;

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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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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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Neural Networks And Their Applications

, and genetic and neural algorithms. This book demonstrates the vigour and strength of the subject in solving hard problems and as such will be;

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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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Hands-On Neural Networks

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions;

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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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Focus on Artificial Neural Networks

artificial neural networks in environmental sciences and chemical engineering; application of artificial neural networks in the development;

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Applied Neural Networks and Soft Computing

Applied Neural Networks and Soft Computing examines the relation between neural networks and soft computing. Neural network is a system;

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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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Simulating Neural Networks with Mathematica

This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers;

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A Statistical Approach to Neural Networks for Pattern Recognition

An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural;

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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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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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Statistics and Data Science

, data visualization, bioinformatics, machine learning, neural networks, statistics, and probability.;

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Neural Networks with R

with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial;

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Neural Networks for Complete Beginners

learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace 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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