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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. 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

  4. Generative adversarial network

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    generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. GAN applications have increased rapidly

  5. Siamese neural network

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    A twin neural network (sometimes called a Siamese Network, though this term is frowned upon) is an artificial neural network that uses the same weights

  6. Dropout (neural networks)

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    visible) in a neural network. AlexNet Convolutional neural network § Dropout [1], "System and method for addressing overfitting in a neural network"  Hinton

  7. 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

  8. 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

  9. AI accelerator

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    Deep Convolutional Neural Networks Using Specialized Hardware" (PDF). Microsoft Research. "A Survey of FPGA-based Accelerators for Convolutional Neural Networks"

  10. 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

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