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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]
Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. [6] [7] [8] The Normalization layer adjusts the output data from previous layers to achieve a regular distribution. This results in improved scalability and model training.
Discontinuous activation functions, [5] noncompact domains, [11] [25] certifiable networks, [26] random neural networks, [27] and alternative network architectures and topologies. [ 11 ] [ 28 ] The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded ...
Video: as the width of the network increases, the output distribution simplifies, ultimately converging to a Neural network Gaussian process in the infinite width limit. Artificial neural networks are a class of models used in machine learning, and inspired by biological neural networks. They are the core component of modern deep learning ...
The Network in Network architecture (2013) [9] was an earlier CNN. It changed the AlexNet architecture by adding 1x1 convolutions, and using a global average pooling after the last convolution. The key architectural principle of VGG models is the consistent use of small convolutional filters throughout the network.
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.
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.
Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern