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LSTM Recurrent Neural Networks for Signature Verification

The author investigated the application of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) to the task of signature;

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Recurrent Neural Networks

scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized;

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Deep Learning for Natural Language Processing

Explore convolutional and recurrent neural networks and long short-term memory networks Book DescriptionApplying deep learning approaches to;

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Neural Networks with Keras Cookbook

multiple neural network architectures such as CNN, RNN, LSTM in Keras Discover tips and tricks for designing a robust neural network to solve real;

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Sign on the Dotted Line

verification. In particular "biometrics," a branch of identification that deals with verifying physical or behavioural characteristics of human beings;

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PyTorch Recipes

algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you;

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Python Deep Learning

-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent;

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Recurrent Neural Networks for Temporal Data Processing

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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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Advanced Deep Learning with Python

LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs;

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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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Codeless Deep Learning with KNIME

you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting;

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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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Recurrent Neural Networks for Prediction

Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and;

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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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Neural Information Processing

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

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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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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous;

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Simulating Neural Networks with Mathematica

will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural;

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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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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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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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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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Deep Neural Networks in a Mathematical Framework

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools;

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Network Models for Control and Processing

experimentation and physical mechanisms with computer-assisted and hardware models of biological neural networks and neurons; improving processes for;

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