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. Artificial neural network

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

  3. Deep learning

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

  4. 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

  5. Transformer (machine learning model)

    primarily in the field of natural language processing (NLP). Like recurrent neural networks (RNNs), Transformers are designed to handle ordered sequences

  6. Artificial intelligence

    the program that beat a top Go champion in 2016. Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)

  7. Encog

    Counterpropagation Neural Network (CPN) Elman Recurrent Neural Network Neuroevolution of augmenting topologies (NEAT) Feedforward Neural Network (Perceptron)

  8. History of artificial neural networks

    many-layered feedforward neural networks. Between 2009 and 2012, recurrent neural networks and deep feedforward neural networks developed in Schmidhuber's

  9. Speech recognition

    related recurrent neural networks (RNNs) and Time Delay Neural Networks(TDNN's) have demonstrated improved performance in this area. Deep Neural Networks and

  10. Language model

    models and recurrent neural network models MITLM – MIT Language Modeling toolkit. Free software NPLM – Free toolkit for feedforward neural language models