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  1. Convolutional neural network

    aimlexchange.com/search/wiki/page/Convolutional_neural_network

    In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery

  2. Artificial neural network

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    Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal

  3. Deep learning

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    architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields

  4. Neural network

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    A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus

  5. Transformer (machine learning model)

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    primarily in the field of natural language processing (NLP). Like recurrent neural networks (RNNs), Transformers are designed to handle ordered sequences

  6. Artificial intelligence

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    the program that beat a top Go champion in 2016. Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)

  7. Encog

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    Counterpropagation Neural Network (CPN) Elman Recurrent Neural Network Neuroevolution of augmenting topologies (NEAT) Feedforward Neural Network (Perceptron)

  8. History of artificial neural networks

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    many-layered feedforward neural networks. Between 2009 and 2012, recurrent neural networks and deep feedforward neural networks developed in Schmidhuber's

  9. Speech recognition

    aimlexchange.com/search/wiki/page/Speech_recognition

    related recurrent neural networks (RNNs) and Time Delay Neural Networks(TDNN's) have demonstrated improved performance in this area. Deep Neural Networks and

  10. Language model

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    models and recurrent neural network models MITLM – MIT Language Modeling toolkit. Free software NPLM – Free toolkit for feedforward neural language models

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