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Animal Learning and Cognition

of classical conditioning, Part II describes neural networks of operant conditioning, and animal communication, Part III discusses spatial and;

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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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Connectionist Models in Cognitive Psychology

neural network models in a clear and accessible style, with an emphasis on the relationship between the models and relevant experimental data;

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Connectionist Models in Cognitive Psychology

neural network models in a clear and accessible style, with an emphasis on the relationship between the models and relevant experimental data;

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Artificial Neural Networks and Machine Learning ICANN 2020

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks;

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Artificial Neural Networks and Machine Learning ICANN 2020

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks;

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Computational Models of Brain and Behavior

methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models;

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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 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

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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Introduction to the Theory of Neural Computation

coverage of neural network applications in a variety of problems of both theoretical and practical interest.;

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Introduction to the Theory of Neural Computation

coverage of neural network applications in a variety of problems of both theoretical and practical interest.;

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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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Brits Speak Out

This work introduces concepts and algorithms in optimization theory for neural network research, covers the main neural network models and;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Introduction to Neural and Cognitive Modeling

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions;

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Introduction to Neural and Cognitive Modeling

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions;

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PyTorch Recipes

code. You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as;

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Semi-empirical Neural Network Modeling and Digital Twins Development

solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single;

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Building Neural Networks

network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author;

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Network Models for Control and Processing

related fields. These cover: control processes, modelling features and operations of biological neural networks and neurons, simulation;

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Mastering PyTorch

PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and;

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Neural Networks Modeling and Control

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and;

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Practical Neural Network Recipes In C++

experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network;

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