In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used...

Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision. Their purpose is either...

Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are...

convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network...

their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert. Each of these...

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions...

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

Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series...

conventional machine learning models used for these applications. The prior knowledge of general physical laws acts in the training of neural networks (NNs)...

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

Neural network (machine learning), a network of mathematical neurons used in computation Neural network or Neural Networks may also refer to: Neural Networks...

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

learning, and autoencoders. After the rise of deep learning, most large-scale unsupervised learning have been done by training general-purpose neural...

Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an...

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

leveraging information from the hidden layers of recurrent neural networks. Recurrent neural networks favor more recent information contained in words at the...

incremental learning. Examples of incremental algorithms include decision trees (IDE4, ID5R and gaenari), decision rules, artificial neural networks (RBF networks...

In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up...

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence...

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

The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations...

A Boltzmann machine is a type of stochastic neural network invented by Geoffrey Hinton and Terry Sejnowski in 1985. Boltzmann machines can be seen as...

Oriol; Le, Quoc V (2014). "Sequence to Sequence Learning with Neural Networks". Advances in Neural Information Processing Systems. 27. Curran Associates...

The Switching Neural Network approach was developed in the 1990s to overcome the drawbacks of the most commonly used machine learning methods. In particular...

Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample...

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

Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model...