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Stochastic gradient descent

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an…

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Background

Iterative method

History

Notable applications

Extensions and variants

Approximations in continuous time

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Stochastic gradient descent

Nodes100
Edges99
Triples81
Avg. degree1.98
Density0.02
Components1

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Stochastic gradient descent

Top relations

related to history · 12
Stochastic gradient descent → As, Backpropagation, Building, Frank Rosenblatt, Herbert Robbins, In, Jack Kiefer, Jacob Wolfowitz, Later, SGD, Soon, Sutton Monro
has application · 9
Stochastic gradient descent → ADALINE, Full Waveform Inversion, FWI, Geophysics, Its, L-BFGS, Stochastic, Vowpal Wabbit, When
related to Momentum · 9
Stochastic gradient descent → Boris Polyak's, Delta, Further, Hinton, ML, Rumelhart, Soviet, Stochastic, Williams
related to Extensions and variants · 8
Stochastic gradient descent → In, MacQueen, Many, Practical, Setting, SGD, Spall, Such
related to Linear regression · 7
Stochastic gradient descent → In, Note, SGD, Suppose, That, The, This
related to AdaGrad · 6
Stochastic gradient descent → AdaGrad, Examples, Gj, Informally, It, This
related to Averaging · 5
Stochastic gradient descent → Averaged, Polyak, Ruppert, That, When
related to Implicit updates (ISGD) · 5
Stochastic gradient descent → As, Fast, It, The, This
related to Iterative method · 5
Stochastic gradient descent → As, If, In, Several, Typical
related to Approximations in continuous time · 2
Stochastic gradient descent → For, ODE

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Important terminology

gradient displaystyle stochastic descent learning rate eta function algorithm optimization method training momentum parameter sgd nabla sum frac approximation used

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Adadeltainstance ofmany improvements and branches of Adam were then developed0.80text
Adagradinstance ofmany improvements and branches of Adam were then developed0.80text
AdamWinstance ofmany improvements and branches of Adam were then developed0.80text
and Adamax.Within machine learninginstance ofmany improvements and branches of Adam were then developed0.80text
approaches to optimization in 2023 are dominated by Adam-derived optimizersinstance ofmany improvements and branches of Adam were then developed0.80text
TensorFlowinstance ofmany improvements and branches of Adam were then developed0.80text
PyTorchinstance ofmany improvements and branches of Adam were then developed0.80text
by far the most popular machine learning librariesinstance ofmany improvements and branches of Adam were then developed0.80text
as of 2023 largely only include Adam-derived optimizersinstance ofmany improvements and branches of Adam were then developed0.80text
as well as predecessors to Adam such as RMSpropinstance ofmany improvements and branches of Adam were then developed0.80text
classic SGDinstance ofmany improvements and branches of Adam were then developed0.80text
Stochastic gradient descenthas applicationStochastic0.60section

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