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...
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Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...
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Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are...
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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization...
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synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used...
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Recurrent neural networks (RNNs) are a class of artificial neural networks for sequential data processing. Unlike feedforward neural networks, which process...
85 KB (9,882 words) - 11:45, 17 August 2024
Neural network (machine learning), a network of mathematical neurons used in computation Neural network or Neural Networks may also refer to: Neural Networks...
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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...
28 KB (3,646 words) - 04:05, 26 July 2024
include hyperparameter optimization, meta-learning and neural architecture search. In a typical machine learning application, practitioners have a set of...
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Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by neural circuitry...
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Need paper. In neural machine translation, the seq2seq method developed in the early 2010s uses two neural networks. An encoder network encodes an input...
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Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence...
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A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was...
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Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that...
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Oriol; Le, Quoc V (2014). "Sequence to Sequence Learning with Neural Networks". Advances in Neural Information Processing Systems. 27. Curran Associates...
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AI accelerator (redirect from Hardware accelerated neural network)
to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. Typical applications...
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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...
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In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the...
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deep learning", certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. A convolutional neural network...
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Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an...
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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...
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learning, and autoencoders. After the rise of deep learning, most large-scale unsupervised learning have been done by training general-purpose neural...
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The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations...
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Association rule learning algorithms Apriori algorithm Eclat algorithm Artificial neural network Feedforward neural network Extreme learning machine Convolutional...
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perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance....
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research in quantum neural networks involves combining classical artificial neural network models (which are widely used in machine learning for the important...
21 KB (2,542 words) - 11:51, 25 May 2024
ISBN 978-0898716597. Schmidhuber, Jürgen (2015). "Deep learning in neural networks: An overview". Neural Networks. 61: 85–117. arXiv:1404.7828. Bibcode:2014arXiv1404...
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incremental learning. Examples of incremental algorithms include decision trees (IDE4, ID5R and gaenari), decision rules, artificial neural networks (RBF networks...
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types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate...
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The Switching Neural Network approach was developed in the 1990s to overcome the drawbacks of the most commonly used machine learning methods. In particular...
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