Information about Test

  1. Multilayer perceptron

    aimlexchange.com/search/wiki/page/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

    aimlexchange.com/search/wiki/page/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

    aimlexchange.com/search/wiki/page/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

    aimlexchange.com/search/wiki/page/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)

    aimlexchange.com/search/wiki/page/Neural_network_%28disambiguation%29

    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

    aimlexchange.com/search/wiki/page/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

    aimlexchange.com/search/wiki/page/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

    aimlexchange.com/search/wiki/page/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

    aimlexchange.com/search/wiki/page/Feature_learning

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

  10. Eye tracking

    aimlexchange.com/search/wiki/page/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

Contents