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Deep learning

In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to…

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Overview

Interpretations

History

Neural networks

Hardware

Applications

Relation to human cognitive and brain development

Commercial activity

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Deep learning

Nodes348
Edges347
Triples213
Avg. degree1.99
Density0.005747
Components1

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Deep learning

Top relations

related to Hardware · 19
Deep learning → AI, AI-specific, AlexNet, AlphaZero, By, Cerebras Systems, CPUs, CS-2, Deep, Google Cloud Platform, GPUs, Huawei, NPUs, OpenAI, Since, Special, TPU, Wafer Scale Engine, WSE-2
related to Deep backward stochastic differential equation method · 12
Deep learning → Backward, BSDE, By, Deep, Deep BSDE, In, Monte Carlo, PDEs, Physics-informed, PINNs, Specifically, This
related to Automatic speech recognition · 11
Deep learning → American English, Its, Large-scale, LSTM, LSTM RNNs, More, PER, The, This, TIMIT, Very Deep Learning
related to Cyber threat · 11
Deep learning → ANN, ANNs, Another, As, By, For, In, One, Such, The, TinEye
related to Natural language processing · 9
Deep learning → Deep, LSTM, Neural, Other, PCFG, Recursive, RNN, Using, Word
related to Materials science · 8
Deep learning → AI, GNoME, Google DeepMind, In November, Lawrence Berkeley National Laboratory, Materials Project, The, This
related to Theory · 8
Deep learning → Deep, Does, However, If, In, Learning, The Guardian's, What
related to Data collection ethics · 7
Deep learning → Amazon Mechanical Turk, CAPTCHAs, Facebook, Google, It, Rainer Mühlhoff, The
related to Deep learning revolution · 7
Deep learning → Although CNNs, CNNs, GPU, GPUs, Later, NNs, The
related to Relation to human cognitive and brain development · 7
Deep learning → Deep, In, Like, On, Other, These, This

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Important terminology

deep learning neural networks layers data training network recognition image models speech used tasks machine human methods generative backpropagation layer

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Deep learningpart ofstate-of-the-art systems in various disciplines0.85text
classificationinstance offocuses on utilizing multilayered neural networks to perform tasks0.80text
regressioninstance offocuses on utilizing multilayered neural networks to perform tasks0.80text
and representation learninginstance offocuses on utilizing multilayered neural networks to perform tasks0.80text
convolutional neural networksinstance ofOverviewMost modern deep learning models are based on multi-layered neural networks0.80text
transformersinstance ofOverviewMost modern deep learning models are based on multi-layered neural networks0.80text
although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networksinstance ofOverviewMost modern deep learning models are based on multi-layered neural networks0.80text
deep Boltzmann machines.Fundamentallyinstance ofOverviewMost modern deep learning models are based on multi-layered neural networks0.80text
deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a progressively more abstractinstance ofOverviewMost modern deep learning models are based on multi-layered neural networks0.80text
composite representationinstance ofOverviewMost modern deep learning models are based on multi-layered neural networks0.80text
linesinstance ofThe first representational layer may attempt to identify basic shapes0.80text
circlesinstance ofThe first representational layer may attempt to identify basic shapes0.80text

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