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Recurrent Neural Networks

This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from;

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Blind Equalization in Neural Networks

in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and;

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Recurrent Neural Networks for Temporal Data Processing

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Deep Learning from Scratch Building with Python from First Principles

neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural;

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Neural Network Programming with Java

implement these in Java. Recurrent neural networks, which are believed to have memory, are discussed in detail. These work in such a way that the;

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Neural Information Processing

networks, recurrent neural networks, and spiking neural networks.;

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Advances in Neural Networks - ISNN 2016

cover many topics of neural network-related research including signal and image processing; dynamical behaviors of recurrent neural networks;

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Engineering Applications of Neural Networks

machine, machine learning applications, predictive models, fuzzy and recommender systems, recurrent neural networks, spiking neural networks.;

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Statistical Mechanics of Neural Networks

chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have;

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Recurrent Neural Networks for Prediction

presenting the latest research work the authors demonstrate how real--time recurrent neural networks (RNNs) can be implemented to expand the range;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Handbook on Neural Information Processing

applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks;

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Simulating Neural Networks with Mathematica

with Neural Networks Feedback and Recurrent Networks Adaptive Resonance Theory Genetic Algorithms 020156629XB04062001;

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Evolutionary Algorithms and Neural Networks

feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the;

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Supervised Sequence Labelling with Recurrent Neural Networks

, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences;

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Deep Neural Networks in a Mathematical Framework

, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive;

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Deep Learning and Practice with MindSpore

(CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud;

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Deep Learning and Practice with MindSpore

(CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud;

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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;

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Neural Network Methods in Natural Language Processing

, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These;

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Neural Network Methods in Natural Language Processing

, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These;

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Introduction to Graph Neural Networks

networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed;

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Introduction to Graph Neural Networks

between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural;

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Advanced Deep Learning with Python

neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as;

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