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Joint Models of Neural and Behavioral Data

book also discusses other joint modeling approaches and future directions. Joint Models of Neural and Behavioral Data will be;

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Joint Training for Neural Machine Translation

monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based;

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Behavioral and Neural Genetics of Zebrafish

Behavioral and Neural Genetics of Zebrafish assembles the state-of-the-art methodologies and current concepts pertinent to their;

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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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Behavior Analysis with Machine Learning Using R

, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even;

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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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Behavioral Modelling and Predistortion of Wideband Wireless Transmitters

Covers theoretical and practical aspects related to the behavioral modelling and predistortion of wireless transmitters and power;

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Neural Network Models of Cognition

This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their;

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Neural Information Processing

from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based;

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Introduction to Deep Learning for Healthcare

This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that;

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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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Correlated Data Analysis

formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear;

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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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Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers

modeling (including neural Volterra series modeling, neural network modeling, X-parameter modeling), nonlinear analysis methods, and power;

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Data-Driven Computational Neuroscience

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain;

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Tinnitus and Hyperacusis

hyperacusis. The author then proposes two parallel neural pathways underlying these conditions and provides a basis for connecting animal to human;

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How to Build a Brain

provided to explain a wide range of data including single-cell recordings, neural population activity, reaction times, error rates, choice behavior;

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The Computational Brain

book to bring together computational concepts and behavioral data within a neurobiological framework. Computer models constrained by;

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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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Dynamical Biostatistical Models

Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models;

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Model Induction from Data

induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some currently popular artificial neural;

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Model Induction from Data

induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some currently popular artificial neural;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Rethinking Neural Networks

pursued? Half of the contributions deal with modelling synapto-dendritic and neural ultrastructural processes; the remainder, with laboratory;

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