Information about Test

  1. Outline of machine learning

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

  2. Keyword spotting

    and garbage model K-best hypothesis Iterative Viterbi decoding Convolutional neural network on Mel-frequency cepstrum coefficients Keyword spotting in document

  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. Apache SINGA

    models. Workload: we use a deep convolutional neural network, ResNet-50 as the application. ResNet-50 has 50 convolution layers for image classification

  5. Artificial intelligence

    deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks

  6. WaveNet

    sounding audio. WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal

  7. Extreme learning machine

    Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning

  8. Jürgen Schmidhuber

    his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN

  9. Backpropagation

    training feedforward neural networks for supervised learning. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for

  10. Reverse image search

    system. The pipeline uses Apache Hadoop, the open-source Caffe convolutional neural network framework, Cascading for batch processing, PinLater for messaging