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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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...
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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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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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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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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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memory, reasoning tasks in differentiable neural computers, and neural Turing machines. The attention network was designed to identify the highest correlations...
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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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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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Translate gradually replaced the older statistical machine translation approach with the newer neural-networks-based approach that included a seq2seq model...
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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...
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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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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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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...
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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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AI accelerator (redirect from Hardware accelerated neural network)
accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine vision. Typical applications include...
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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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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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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...
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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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The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations...
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Tensor Processing Unit (section Pixel Neural Core)
application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using...
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particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice...
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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....
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Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data...
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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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perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance....
134 KB (14,773 words) - 04:53, 2 July 2024