Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural...

functions. Artificial neural networks are used to solve artificial intelligence problems. In the context of biology, a neural network is a population of biological...

structure and functions of biological neural networks. A neural network consists of connected units or nodes called artificial neurons, which loosely model...

many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used...

of these technologies.[non-primary source needed] History of artificial neural networks History of knowledge representation and reasoning History of natural...

Biological neural networks are studied to understand the organization and functioning of nervous systems. Closely related are artificial neural networks, machine...

physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse...

In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the...

networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance...

Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights...

training and convergence of deep neural networks with hundreds of layers, and is a common motif in deep neural networks, such as transformer models (e.g...

Fast Artificial Neural Network (FANN) is cross-platform programming library for developing multilayer feedforward artificial neural networks (ANNs). It...

Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes...

known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that...

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where...

analogy with biology (removal of components of an organism), and is particularly used in the analysis of artificial neural networks by analogy with ablative...

NETtalk is an artificial neural network that learns to pronounce written English text by supervised learning. It takes English text as input, and produces...

with previous AI winters. In 2012, a University of Toronto research team used artificial neural networks and deep learning techniques to lower the error...

Generative adversarial networks (GANs) are an influential generative modeling technique. GANs consist of two neural networks—the generator and the...

An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary...

integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics...

some alternative definitions of artificial neural networks, and have shown them to be equivalent, that is, neural networks under one definition realizes...

artificial intelligence History of artificial neural networks McCulloch, Warren S.; Pitts, Walter (1943-12-01). "A logical calculus of the ideas immanent in...

In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers...

learning, cellular neural networks (CNN) or cellular nonlinear networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference...

field of artificial intelligence, specifically artificial neural networks. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence...

University of Edinburgh. Each one developed its own style of research. Earlier approaches based on cybernetics or artificial neural networks were abandoned...

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of...

multilayered procedure is equivalent to the Artificial Neural Network with polynomial activation function of neurons. Therefore, the algorithm with such...

top of other libraries). Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks. OpenNN...