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gptkbp:instance_of
|
gptkb:Google
|
|
gptkbp:can_be_adjusted_with
|
adaptive learning rates
|
|
gptkbp:can_be_combined_with
|
momentum
Nesterov momentum
|
|
gptkbp:can_be_sensitive_to
|
initialization of parameters
|
|
gptkbp:can_be_used_in
|
online learning
|
|
gptkbp:can_be_used_to
|
reduce overfitting
improve model generalization
optimize convex functions
train deep networks
|
|
gptkbp:can_be_used_with
|
mini-batch training
|
|
gptkbp:can_converge_to
|
local minima
|
|
gptkbp:can_lead_to
|
faster convergence
|
|
gptkbp:has
|
learning rate
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
SGD optimizer
|
|
gptkbp:is
|
stochastic gradient descent
first-order optimization algorithm
|
|
gptkbp:is_a_foundation_for
|
gptkb:machine_learning
|
|
gptkbp:is_a_fundamental_technique_in
|
gptkb:Artificial_Intelligence
|
|
gptkbp:is_a_key_component_of
|
reinforcement learning
|
|
gptkbp:is_a_standard_method_in
|
statistical learning theory
|
|
gptkbp:is_affected_by
|
batch size
data shuffling
|
|
gptkbp:is_effective_against
|
large-scale problems
|
|
gptkbp:is_enhanced_by
|
learning rate decay
|
|
gptkbp:is_essential_for
|
training pipelines
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Py_Torch
regularization techniques
backpropagation
|
|
gptkbp:is_known_for
|
simplicity
high variance in updates
|
|
gptkbp:is_less_effective_with
|
noisy gradients
|
|
gptkbp:is_often_benchmarked_against
|
other optimization algorithms
|
|
gptkbp:is_often_compared_to
|
gptkb:Adam_optimizer
|
|
gptkbp:is_often_discussed_in
|
optimization research
|
|
gptkbp:is_often_the_default_choice_for
|
many frameworks
|
|
gptkbp:is_often_used_in
|
gptkb:academic_research
computer vision
natural language processing
real-time applications
cross-validation
|
|
gptkbp:is_popular_for
|
training generative models
|
|
gptkbp:is_recommended_for
|
large datasets
|
|
gptkbp:is_used_for
|
training neural networks
|
|
gptkbp:is_used_in
|
support vector machines
|
|
gptkbp:minimizes
|
loss function
|
|
gptkbp:modifications
|
learning rate schedules
|
|
gptkbp:requires
|
hyperparameter tuning
|
|
gptkbp:sensitivity
|
learning rate
|
|
gptkbp:suitable_for
|
non-convex optimization problems
|
|
gptkbp:type_of
|
gradient descent
|
|
gptkbp:updates
|
model parameters
|
|
gptkbp:used_in
|
gptkb:machine_learning
deep learning
|
|
gptkbp:bfsParent
|
gptkb:Keras_Sequential_models
|
|
gptkbp:bfsLayer
|
6
|