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. Neural network

    A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus

  3. Generative adversarial network

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

  4. Deep learning

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

  5. Transformer (machine learning model)

    computation. Autoregressive convolutional neural networks can handle long-range dependencies through dilated convolutions such that the path length is

  6. PyTorch

    processing units (GPU) Deep neural networks built on a tape-based autodiff system Facebook operated both PyTorch and Convolutional Architecture for Fast Feature

  7. Movidius

    uses the Myriad 2. Vision processing unit MPSoC Coprocessor Convolutional neural network Newenham, Pamela. "Sean Mitchell and David Moloney, Movidius"

  8. Encog

    provided to help model and train neural networks. Encog has been in active development since 2008. ADALINE Neural Network Adaptive Resonance Theory 1 (ART1)

  9. Machine learning in video games

    and run on. Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn

  10. Machine learning

    Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. "Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations"