Information about Test

  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. 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. Artificial neural network

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    Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that

  4. 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

  5. Deep learning

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

  6. 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

  7. 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"

  8. Siamese neural network

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    A siamese neural network is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable

  9. 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

  10. 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

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