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 the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...

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...

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

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

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

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

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

memory, reasoning tasks in differentiable neural computers, and neural Turing machines. The attention network was designed to identify the highest correlations...

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

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

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

Translate gradually replaced the older statistical machine translation approach with the newer neural-networks-based approach that included a seq2seq model...

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...

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...

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...

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

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

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was...

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

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

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

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

application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using...

Multimodal learning, in the context of machine learning, is a type of deep learning using multiple modalities of data, such as text, audio, or images....

research in quantum neural networks involves combining classical artificial neural network models (which are widely used in machine learning for the important...

Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data...

perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance....