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Some artificial neural networks are adaptive systems and are used for example to model populations and environments, which constantly change. Neural networks can be hardware- (neurons are represented by physical components) or software-based (computer models), and can use a variety of topologies and learning algorithms.
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 ]
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models . While individual neurons are simple, many of them together in a network can perform complex tasks.
Artificial neural networks is included in the JEL classification codes as JEL: C45 Wikimedia Commons has media related to Artificial neural networks . The main article for this category is Artificial neural networks .
A probabilistic neural network (PNN) [1] is a feedforward neural network, which is widely used in classification and pattern recognition problems.In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function.
The research achieved great success and aroused the interest of scholars in the study of neural networks. While the architecture of the best performing neural networks today are not the same as that of LeNet, the network was the starting point for a large number of neural network architectures, and also brought inspiration to the field.
Rethinking Recurrent Neural Networks and other Improvements for Image Classification [17] 1.64 July 31, 2020 AutoAugment: Learning Augmentation Policies from Data [18] 1.48 May 24, 2018 A Survey on Neural Architecture Search [19] 1.33 May 4, 2019 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism [20] 1.00 Nov 16, 2018