Information about Test

  1. AlexNet

    aimlexchange.com/search/wiki/page/AlexNet

    AlexNet is the name of a convolutional neural network, designed by Alex Krizhevsky, and published with Ilya Sutskever and Krizhevsky's PhD advisor Geoffrey

  2. Unsupervised learning

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    detection Local Outlier Factor Neural Networks Autoencoders Deep Belief Nets Hebbian Learning Generative adversarial networks Self-organizing map Approaches

  3. Transformer (machine learning model)

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    computation. Autoregressive convolutional neural networks can handle long-range dependencies through dilated convolutions such that the path length is

  4. Multilayer perceptron

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    artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to refer to any feedforward ANN, sometimes strictly to refer to networks composed

  5. Spiking neural network

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    media Play media Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal

  6. Kunihiko Fukushima

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    1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima proposed several supervised and

  7. Neural machine translation

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    decoder, that is used to predict words in the target language. Convolutional Neural Networks (Convnets) are in principle somewhat better for long continuous

  8. SpaCy

    aimlexchange.com/search/wiki/page/SpaCy

    available as a separate open-source Python library. It features convolutional neural network models for part-of-speech tagging, dependency parsing and named

  9. Neural architecture search

    aimlexchange.com/search/wiki/page/Neural_architecture_search

    Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine

  10. Machine learning in video games

    aimlexchange.com/search/wiki/page/Machine_learning_in_video_games

    and run on. Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn

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