Information about Test

  1. Types of artificial neural networks

    types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate

  2. Kunihiko Fukushima

    1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima proposed several supervised and

  3. 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

  4. Universal approximation theorem

    sets of functions, such as the Convolutional neural network architecture , radial basis-functions , or neural networks with specific properties. Most

  5. AlexNet

    AlexNet is the name of a convolutional neural network (CNN), designed by Alex Krizhevsky, and published with Ilya Sutskever and Krizhevsky's doctoral advisor

  6. Unsupervised learning

    detection Local Outlier Factor Neural Networks Autoencoders Deep Belief Nets Hebbian Learning Generative adversarial networks Self-organizing map Approaches

  7. Q-learning

    human levels. The DeepMind system used a deep convolutional neural network, with layers of tiled convolutional filters to mimic the effects of receptive fields

  8. Inceptionv3

    Inceptionv3 is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for Googlenet. It is

  9. Neural machine translation

    information leading to over-translation and under-translation. Convolutional Neural Networks (Convnets) are in principle somewhat better for long continuous

  10. Visual temporal attention

    video analytics tasks, such as human action recognition. In convolutional neural network-based systems, the prioritization introduced by the attention