Information about Test

  1. 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

    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

    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

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

  5. Siamese neural network

    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)

    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

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

  8. Spiking neural network

    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

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

  10. Recurrent neural network

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