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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]
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 is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period, centered around Yann LeCun. They were designed for reading small grayscale images of handwritten digits and letters, and were used in ATM for reading cheques.
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.Their creation was inspired by biological neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]
One of its two networks has "fast weights" or "dynamic links" (1981). [15] [16] [17] A slow neural network learns by gradient descent to generate keys and values for computing the weight changes of the fast neural network which computes answers to queries. [14] This was later shown to be equivalent to the unnormalized linear Transformer. [18] [19]
In 1980, Fukushima published the neocognitron, [2] [3] the original deep convolutional neural network (CNN) architecture. [4] [5] Fukushima proposed several supervised and unsupervised learning algorithms to train the parameters of a deep neocognitron such that it could learn internal representations of incoming data.
Convolutional Deep Belief Networks on CIFAR-10 [6] 21.1 August, 2010 Maxout Networks [7] 9.38: February 13, 2013: Wide Residual Networks [8] 4.0: May 23, 2016: Neural Architecture Search with Reinforcement Learning [9] 3.65: November 4, 2016: Fractional Max-Pooling [10] 3.47: December 18, 2014: Densely Connected Convolutional Networks [11] 3.46 ...
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 ...