Information about Test

  1. Multilayer perceptron

    artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed

  2. Machine learning

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

  3. Feature scaling

    (e.g., support vector machines, logistic regression, and artificial neural networks).[citation needed] The general method of calculation is to determine

  4. Region Based Convolutional Neural Networks

    Region Based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection. The

  5. Neural network (disambiguation)

    Large scale brain networks, biological neural networks on a larger scale Convolutional neural network, a class of deep neural networks, most commonly applied

  6. 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

  7. Outline of machine learning

    method Artificial neural network Feedforward neural network Extreme learning machine Convolutional neural network Recurrent neural network Long short-term

  8. Neocognitron

    pattern recognition tasks, and served as the inspiration for convolutional neural networks. The neocognitron was inspired by the model proposed by Hubel

  9. Feature learning

    are learned using labeled input data. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. In unsupervised

  10. Eye tracking

    2017 constructed a Deep Integrated Neural Network (DINN) out of a Deep Neural Network and a convolutional neural network. The goal was to use deep learning