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  2. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]

  3. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto in 2012.

  4. LeNet - Wikipedia

    en.wikipedia.org/wiki/LeNet

    LeNet-5 architecture (overview). LeNet is a series of convolutional neural network structure proposed by LeCun et al. [1] The earliest version, LeNet-1, was trained in 1989.In general, when "LeNet" is referred to without a number, it refers to LeNet-5 (1998), the most well-known version.

  5. Convolutional layer - Wikipedia

    en.wikipedia.org/wiki/Convolutional_layer

    In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry.

  6. Neural machine translation - Wikipedia

    en.wikipedia.org/wiki/Neural_machine_translation

    In 2013 and 2014, end-to-end neural machine translation had their breakthrough with Kalchbrenner & Blunsom using a convolutional neural network (CNN) for encoding the source [18] and both Cho et al. and Sutskever et al. using a recurrent neural network (RNN) instead.

  7. Region Based Convolutional Neural Networks - Wikipedia

    en.wikipedia.org/wiki/Region_Based_Convolutional...

    Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. [1] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or ...

  8. Q-learning - Wikipedia

    en.wikipedia.org/wiki/Q-learning

    The DeepMind system used a deep convolutional neural network, with layers of tiled convolutional filters to mimic the effects of receptive fields. Reinforcement learning is unstable or divergent when a nonlinear function approximator such as a neural network is used to represent Q.

  9. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.