Information about Test

  1. Types of artificial neural networks

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    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

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

  3. History of artificial neural networks

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    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

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

  5. AlexNet

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    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

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

  7. Q-learning

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    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

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    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

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

  10. Visual temporal attention

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

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