Deep neural networks machine learning

• [DOCX File]Title

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  More recent advances in deep learning techniques as applied to speech include the use of locally-connected or convolutional deep neural networks (CNN) [30][9][10][31] and of temporally (deep) recurrent versions of neural networks (RNN) [14][15][8][6], also considerably outperforming the early neural networks with convolution in time [36] and ...

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• [DOCX File]NIST

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  A research scientist or machine learning practitioner wants to train a deep neural network from a large (>>1TB) corpus of data (typically imagery, video, audio, or text). Such training procedures often require customization of the neural network architecture, learning criteria, and dataset pre-processing.

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• [DOCX File]Author Guidelines for 8

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  of the log-posterior probabilities generated by the softmax output layer of a shallow neural network [18]. The method of building deep stacking networks is a more general, recursive way of creating tandem-like features as the overall network built gets deeper and deeper [10][11][20][33].

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• [DOC File]1 - FAU

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  Detailed topics include introduction to machine learning algorithms, perceptron learning, and multi-layer neural networks, and deep neural network structures and learning algorithms. The lectures will include practical sessions dedicated to the implementation and programming of deep learning framework. 7.

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• [DOCX File]Table of Figures - Virginia Tech

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  Deep Learning is a subfield of Machine Learning that is focused around creating a multitude of neural networks that are able to learn from data and predict outcomes. Deep Learning is a field that has, in recent years, become increasingly applicable in the workforce due to its many useful applications.

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