Information about Test

  1. Outline of machine learning

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    method Artificial neural network Feedforward neural network Extreme learning machine Convolutional neural network Recurrent neural network Long short-term

  2. Keyword spotting

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    and garbage model K-best hypothesis Iterative Viterbi decoding Convolutional neural network on Mel-frequency cepstrum coefficients Keyword spotting in document

  3. Feature scaling

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    (e.g., support vector machines, logistic regression, and artificial neural networks)[citation needed]. The general method of calculation is to determine

  4. Apache SINGA

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    models. Workload: we use a deep convolutional neural network, ResNet-50 as the application. ResNet-50 has 50 convolution layers for image classification

  5. Artificial intelligence

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    deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks

  6. WaveNet

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    sounding audio. WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal

  7. Extreme learning machine

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    Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning

  8. Jürgen Schmidhuber

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    his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN

  9. Backpropagation

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    training feedforward neural networks for supervised learning. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for

  10. Reverse image search

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    system. The pipeline uses Apache Hadoop, the open-source Caffe convolutional neural network framework, Cascading for batch processing, PinLater for messaging

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