Information about Test

  1. Bootstrap aggregating

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    procedures" (Breiman, 1996), which include, for example, artificial neural networks, classification and regression trees, and subset selection in linear

  2. CNN (disambiguation)

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    Capone-N-Noreaga (C-N-N), a hip hop duo Cellular neural network, a parallel computing paradigm Convolutional neural network, a multilayer perceptron variation Condoms

  3. Bias–variance tradeoff

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    when increasing the width of a neural network. This means that it is not necessary to control the size of a neural network to control variance. This does

  4. Dermatoscopy

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    public image collections such as HAM10000 enabled application of convolutional neural networks. The latter approach has now shown experimental evidence of

  5. 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

  6. Traffic-sign recognition

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    help. A convolutional neural network can be trained to take in these predefined traffic signs and 'learn' using Deep Learning techniques. The neural net in

  7. Transformer (machine learning model)

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    in the field of natural language processing (NLP). Like recurrent neural networks (RNNs), Transformers are designed to handle sequential data, such as

  8. Receptive field

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    also used in the context of artificial neural networks, most often in relation to convolutional neural networks (CNNs). When used in this sense, the term

  9. 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

  10. Tsetlin machine

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    and more efficient primitives compared to more ordinary artificial neural networks, but while the method may be faster it has a steep drop in signal-to-noise

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