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...

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

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

A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization...

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

Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which...

Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that...

include hyperparameter optimization, meta-learning and neural architecture search. In a typical machine learning application, practitioners have a set of...

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

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

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

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

differentiable neural computers and neural Turing machines. It was termed intra-attention where an LSTM is augmented with a memory network as it encodes...

to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. Typical applications...

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

developments are impacting all areas of machine learning, such as text mining and clustering, time varying data, and neural networks wherein the input data is a social...

deep learning", certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. A convolutional neural network...

ISBN 978-0898716597. Schmidhuber, Jürgen (2015). "Deep learning in neural networks: An overview". Neural Networks. 61: 85–117. arXiv:1404.7828. Bibcode:2014arXiv1404...

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

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

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 neural Turing machine (NTM) is a recurrent neural network model of a Turing machine. The approach was published by Alex Graves et al. in 2014. NTMs...

A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between...

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

without explicit instructions. Advances in the field of deep learning have allowed neural networks to surpass many previous approaches in performance. ML finds...

particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice...

topological order. Recursive neural networks, sometimes abbreviated as RvNNs, have been successful, for instance, in learning sequence and tree structures...

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...

Examples include supervised neural networks, multilayer perceptrons, and dictionary learning. In unsupervised feature learning, features are learned with...