Results 271 to 280 of about 312,321 (283)
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2009
Recurrent neural networks are networks that feed the outputs from neurons to other adjacent neurons, to themselves, or to neurons on preceding network layers. Two of the most popular recurrent neural networks are the Hopfield and the Bidirectional Associative Memory (BAM) networks.
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Recurrent neural networks are networks that feed the outputs from neurons to other adjacent neurons, to themselves, or to neurons on preceding network layers. Two of the most popular recurrent neural networks are the Hopfield and the Bidirectional Associative Memory (BAM) networks.
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2020
A feedforward fully-connected neural network cannot be used successfully for modeling sequences of data. A few basic reasons are the following: they cannot handle variable-length input sequences, do not share parameters, cannot track long-term dependencies, and cannot maintain information about the order of input data.
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A feedforward fully-connected neural network cannot be used successfully for modeling sequences of data. A few basic reasons are the following: they cannot handle variable-length input sequences, do not share parameters, cannot track long-term dependencies, and cannot maintain information about the order of input data.
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2017
Recurrent Neural Networks (RNNs) in essence are neural networks that employ recurrence, which is basically using information from a previous forward pass over the neural network.
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Recurrent Neural Networks (RNNs) in essence are neural networks that employ recurrence, which is basically using information from a previous forward pass over the neural network.
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2018
In Chapter 9, we looked at how convolutional neural networks (CNNs) improve upon the traditional neural network architecture for image classification. Although CNNs perform very well for image classification in which image translation and rotation are taken care of, they do not necessarily help in identifying temporal patterns.
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In Chapter 9, we looked at how convolutional neural networks (CNNs) improve upon the traditional neural network architecture for image classification. Although CNNs perform very well for image classification in which image translation and rotation are taken care of, they do not necessarily help in identifying temporal patterns.
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2019
In the previous chapter, CNNs provided a way for neural networks to learn a hierarchy of weights, resembling that of n-gram classification on the text. This approach proved to be very effective for sentiment analysis, or more broadly text classification.
Uday Kamath, John Liu, James Whitaker
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In the previous chapter, CNNs provided a way for neural networks to learn a hierarchy of weights, resembling that of n-gram classification on the text. This approach proved to be very effective for sentiment analysis, or more broadly text classification.
Uday Kamath, John Liu, James Whitaker
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1999
From the Publisher: With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design and Applications reflects the tremendous, worldwide interest in and virtually unlimited potential of RNNs ...
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From the Publisher: With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design and Applications reflects the tremendous, worldwide interest in and virtually unlimited potential of RNNs ...
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1995
Neural networks have attracted much attention lately as a powerful tool of automatic learning. Of particular interest is the class of recurrent networks which allow for loops and cycles and thus give rise to dynamical systems, to flexible behavior, and to computation. This paper reviews the recent findings that mathematically quantify the computational
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Neural networks have attracted much attention lately as a powerful tool of automatic learning. Of particular interest is the class of recurrent networks which allow for loops and cycles and thus give rise to dynamical systems, to flexible behavior, and to computation. This paper reviews the recent findings that mathematically quantify the computational
openaire +1 more source