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  1. Convolutional neural network

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    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. Feedforward neural network

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    recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information

  3. Siamese neural network

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    A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on

  4. 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

  5. Generative adversarial network

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    generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. GANs often suffer from a "mode collapse"

  6. Capsule neural network

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    closely mimic biological neural organization. The idea is to add structures called “capsules” to a convolutional neural network (CNN), and to reuse output

  7. MNIST database

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    convolutional neural network best performance was 0.31 percent error rate. As of August 2018, the best performance of a single convolutional neural network

  8. Recurrent neural network

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    combined with convolutional neural networks (CNNs) improved automatic image captioning. RNNs come in many variants. Basic RNNs are a network of neuron-like

  9. Dilution (neural networks)

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    currently holds the patent for the dropout technique. AlexNet Convolutional neural network § Dropout The patent is most likely not valid due to previous

  10. AI accelerator

    aimlexchange.com/search/wiki/page/AI_accelerator

    Classification Using Binary Convolutional Neural Networks". arXiv:1603.05279 [cs.CV]. Khari Johnson (May 23, 2018). "Intel unveils Nervana Neural Net L-1000 for accelerated

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