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gptkbp:instance_of
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gptkb:Artificial_Intelligence
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gptkbp:applies_to
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gptkb:microprocessor
financial modeling
natural language processing
image classification
convex functions
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gptkbp:based_on
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momentum concept
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gptkbp:benefits
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overfitting reduction
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gptkbp:can_be_used_with
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adaptive learning rates
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gptkbp:developed_by
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gptkb:Yurii_Nesterov
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https://www.w3.org/2000/01/rdf-schema#label
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Nesterov Accelerated Gradient
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gptkbp:improves
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gradient descent
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gptkbp:is_a
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first-order optimization method
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gptkbp:is_adopted_by
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data scientists
machine learning practitioners
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gptkbp:is_analyzed_in
|
research papers
|
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gptkbp:is_characterized_by
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adaptive step size
lookahead gradient
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gptkbp:is_compared_to
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gptkb:Adam_optimizer
gptkb:Nesterov's_method
RM Sprop
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gptkbp:is_considered_as
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a variant of momentum
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gptkbp:is_described_as
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theoretical analysis
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gptkbp:is_effective_against
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large-scale optimization
|
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gptkbp:is_evaluated_by
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benchmark tests
loss function
|
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gptkbp:is_explored_in
|
academic literature
optimization research
|
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gptkbp:is_implemented_in
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gptkb:Graphics_Processing_Unit
gptkb:Keras
gptkb:scikit-learn
gptkb:Py_Torch
|
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gptkbp:is_influenced_by
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classical mechanics
|
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gptkbp:is_known_for
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faster convergence
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gptkbp:is_optimized_for
|
non-convex functions
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gptkbp:is_part_of
|
optimization techniques
|
|
gptkbp:is_recognized_by
|
state-of-the-art method
|
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gptkbp:is_related_to
|
stochastic gradient descent
|
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gptkbp:is_studied_in
|
numerical optimization
|
|
gptkbp:is_supported_by
|
theoretical proofs
|
|
gptkbp:is_used_for
|
training models
|
|
gptkbp:is_used_in
|
deep learning
reinforcement learning
time series forecasting
|
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gptkbp:is_utilized_in
|
computer vision
parameter tuning
|
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gptkbp:provides
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momentum
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gptkbp:suitable_for
|
standard gradient descent
|
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gptkbp:used_in
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gptkb:software_framework
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gptkbp:bfsParent
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gptkb:CNTK
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gptkbp:bfsLayer
|
4
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