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The Relevance of the Time Domain to Neural Network Models

integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural;

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Neural Network Learning

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised;

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Neural Network Learning

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised;

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Neural Information Processing

follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred;

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Neural Networks Modeling and Control

Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design;

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Deep Learning for Natural Language Processing

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain;

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Model Induction from Data

recaptured using artificial neural networks. The book also demonstrates how artificial neural networks can be used to generate numerical operators on;

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Model Induction from Data

recaptured using artificial neural networks. The book also demonstrates how artificial neural networks can be used to generate numerical operators;

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Introduction to the Theory of Neural Computation

Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides;

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Introduction to the Theory of Neural Computation

Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides;

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Scaling and Particulate Fouling in Membrane Filtration Systems

network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge incapsulators by applying the;

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Neural Networks and Psychopathology

Research on connectionist models is one of the most exciting areas in cognitive science, and neural network models of psychopathology have;

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Neural Networks and Psychopathology

Research on connectionist models is one of the most exciting areas in cognitive science, and neural network models of psychopathology have;

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Neural Network Models of Conditioning and Action

challenging phenomena - and neural network models provide a natural framework for representing and analysing such interactions - all the articles;

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Neural Network Models of Conditioning and Action

challenging phenomena - and neural network models provide a natural framework for representing and analysing such interactions - all the articles;

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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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Nonlinear Economic Models Crosssectional, Time Series and Neural Network Applications

of exchange rate dynamics and jump processes, and artificial neural network and genetic algorithm models of financial markets. Attention is given to;

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Advanced Deep Learning with Python

behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular;

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Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals

time-frequency domain, and most notably the Stockwell Transform for the feature extraction process and to identify signatures. For the;

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Neural Network Methods in Natural Language Processing

computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary;

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Neural Network Methods in Natural Language Processing

computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary;

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Artificial Neural Network Applications for Software Reliability Prediction

This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction;

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Building Neural Networks

This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural;

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Practical Neural Network Recipes In C++

This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming;

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Semi-empirical Neural Network Modeling and Digital Twins Development

Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network;

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Neural Network Models

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way;

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PyTorch Recipes

Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new;

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