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...

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 networks for sequential data processing. Unlike feedforward neural networks, which process...

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

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...

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 neural circuitry...

Need paper. In neural machine translation, the seq2seq method developed in the early 2010s uses two neural networks. An encoder network encodes an input...

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

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

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

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

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

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...

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

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

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

suggests that a new approach to machine learning should be explored, and is currently working on a unique neural network that has characteristics more similar...

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

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

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

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

Association rule learning algorithms Apriori algorithm Eclat algorithm Artificial neural network Feedforward neural network Extreme learning machine Convolutional...

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

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

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

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