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. Artificial neural network

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

  4. 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

  5. Generative adversarial network

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

  6. Siamese neural network

    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

  7. AI accelerator

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

  8. 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

  9. Deep learning

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

  10. 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