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gptkbp:instance_of
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gptkb:Artificial_Intelligence
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gptkbp:bfsLayer
|
6
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gptkbp:bfsParent
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gptkb:Keras_Tuner
|
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gptkbp:applies_to
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gptkb:software_framework
deep learning models
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gptkbp:based_on
|
bandit-based optimization
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gptkbp:can_be_used_with
|
other optimization techniques
|
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gptkbp:developed_by
|
Nicolas P. Joulin
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Hyperband
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gptkbp:improves
|
random search
|
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gptkbp:introduced
|
gptkb:2016
|
|
gptkbp:is_compared_to
|
Successive Halving
|
|
gptkbp:is_designed_for
|
efficient hyperparameter tuning
|
|
gptkbp:is_effective_against
|
resource allocation
|
|
gptkbp:is_evaluated_by
|
cross-validation
|
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gptkbp:is_implemented_in
|
gptkb:Library
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|
gptkbp:is_optimized_for
|
neural architecture search
|
|
gptkbp:is_part_of
|
gptkb:Auto_ML
|
|
gptkbp:is_popular_in
|
data science community
|
|
gptkbp:is_related_to
|
Bayesian optimization
|
|
gptkbp:is_used_for
|
hyperparameter optimization
|
|
gptkbp:is_used_in
|
automated machine learning frameworks
|
|
gptkbp:requires
|
computational resources
|
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gptkbp:suitable_for
|
large search spaces
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gptkbp:utilizes
|
early stopping
|