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. Deep learning

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

  4. Transformer (machine learning model)

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

  5. 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)

  6. Movidius

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

  7. AI accelerator

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

  8. PyTorch

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

  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. History of artificial neural networks

    artificial neural networks (ANN) began with Warren McCulloch and Walter Pitts (1943) who created a computational model for neural networks based on algorithms