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gptkbp:instance_of
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gptkb:neural_networks
|
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gptkbp:analyzes
|
Weight Matrix
Activation Maps
Loss Curves
|
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gptkbp:architectural_style
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gptkb:Feedforward_Neural_Network
gptkb:neural_networks
|
|
gptkbp:can_overfit
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gptkb:True
|
|
gptkbp:coat_of_arms
|
Hidden Layer
Input Layer
Output Layer
|
|
gptkbp:developed_by
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gptkb:Yoshua_Bengio
gptkb:Geoffrey_R._Hinton
gptkb:Yann_Le_Cun
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|
gptkbp:engine
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gptkb:Retail
Automotive
Finance
Healthcare
|
|
gptkbp:has_limitations
|
Computational Power
Vanishing Gradient Problem
Sensitive to Hyperparameters
Requires Feature Engineering
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|
https://www.w3.org/2000/01/rdf-schema#label
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Traditional Neural Networks
|
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gptkbp:initiated_by
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Sigmoid Function
Re LU Function
Tanh Function
|
|
gptkbp:is_compared_to
|
gptkb:Support_Vector_Machines
gptkb:Decision_Trees
gptkb:Deep_Learning
|
|
gptkbp:is_enhanced_by
|
gptkb:Dropout
L2 Regularization
Early Stopping
|
|
gptkbp:is_evaluated_by
|
Accuracy
Loss Function
Confusion Matrix
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Python
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
Biological Neural Networks
Cognitive Science
|
|
gptkbp:is_part_of
|
gptkb:Artificial_Intelligence
gptkb:machine_learning
|
|
gptkbp:is_trained_in
|
Backpropagation
Gradient Descent
|
|
gptkbp:is_used_for
|
gptkb:Pattern_Recognition
Time Series Prediction
Function Approximation
|
|
gptkbp:is_used_in
|
gptkb:Natural_Language_Processing
gptkb:speeches
Image Recognition
Game Playing
|
|
gptkbp:requires_data
|
Large Datasets
Labeled Data
|
|
gptkbp:bfsParent
|
gptkb:Deep_Boltzmann_Machines
gptkb:Bayesian_Neural_Networks
|
|
gptkbp:bfsLayer
|
5
|