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Learning rate

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Since it influences to what extent newly acquired information overrides old information, it metaphorically represents the speed at which a machine…

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Overview

Machine learningStatisticsTuning parameterOptimization algorithmLoss function
15 related topics

Learning rate schedule

The formula for factoring in the momentumKerasFloor
3 related topics

Adaptive learning rate

Adaptive
1 related topics

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Learning rate schedule

Adaptive learning rate

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Learning rate

Nodes23
Edges22
Triples34
Avg. degree1.91
Density0.086957
Components1

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Learning rate

Top relations

related to Further reading · 17
Learning rate → Advances, Aurélien, Convex Analysis, Global Optimization, Gradient Descent, Géron, Hands-On Machine Learning, ISBN, Kluwer, Learning Rate Adaptation, Magoulas, O'Reilly, Plagianakos, Scikit-Learn, Stochastic Gradient Descent, TensorFlow, Vrahatis
related to Adaptive learning rate · 7
Learning rate → Adadelta, Adagrad, Adam, Keras, RMSprop, The, To
related to Learning rate schedule · 4
Learning rate → Decay, Initial, There, This
is a · 1
Learning rate → tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function

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Important terminology

learning rate adaptive gradient optimization step decay descent machine parameter function minima momentum schedules iteration often minimum local schedule also

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SubjectPredicateObjectConfidenceSrc
Learning rateis atuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function0.90text
Keras.Time-based learning schedules alter the learning rate depending on the learning rate of the previous time iterationinstance ofThe formula for factoring in the momentum is more complex than for decay but is most often built in with deep learning libraries0.80text
Adagradinstance ofthere are many different types of adaptive gradient descent algorithms0.80text
Adadeltainstance ofthere are many different types of adaptive gradient descent algorithms0.80text
RMSpropinstance ofthere are many different types of adaptive gradient descent algorithms0.80text
and Adam which are generally built into deep learning libraries such as Kerasinstance ofthere are many different types of adaptive gradient descent algorithms0.80text
Learning raterelated to Adaptive learning rateThe0.60section
Learning raterelated to Adaptive learning rateTo0.60section
Learning raterelated to Adaptive learning rateAdagrad0.60section
Learning raterelated to Adaptive learning rateAdadelta0.60section
Learning raterelated to Adaptive learning rateRMSprop0.60section
Learning raterelated to Adaptive learning rateAdam0.60section

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