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. Siamese neural network

    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

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

  5. Generative adversarial network

    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

    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

    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

    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)

    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

    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