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gptkbp:instance_of
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gptkb:neural_networks
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gptkbp:analyzes
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Activation Maps
Neural Network Diagrams
Weight Visualization
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gptkbp:architectural_style
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Layered Structure
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gptkbp:can
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Multiple Hidden Layers
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gptkbp:can_be_combined_with
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gptkb:neural_networks
gptkb:Recurrent_Neural_Networks
|
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gptkbp:can_be_parallelized_by
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Data Parallelism
Model Parallelism
|
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gptkbp:can_be_used_for
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gptkb:Natural_Language_Processing
Image Recognition
Time Series Prediction
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gptkbp:can_be_used_to
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Gradient Descent
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gptkbp:consists_of
|
Hidden Layer
Input Layer
Output Layer
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gptkbp:has_limitations
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Vanishing Gradient Problem
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https://www.w3.org/2000/01/rdf-schema#label
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Feedforward Neural Network
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gptkbp:initiated_by
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Non-linear Function
|
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gptkbp:input_output
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Predictions
|
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gptkbp:is_applied_in
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Classification Problems
Regression Problems
|
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gptkbp:is_characterized_by
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Feedforward Connections
|
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gptkbp:is_enhanced_by
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gptkb:Hyperparameter_Tuning
gptkb:Dropout
|
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gptkbp:is_evaluated_by
|
gptkb:test_subjects
Cross-Validation
F1 Score
Loss Function
Accuracy Metric
Precision Metric
Recall Metric
|
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gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Py_Torch
|
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gptkbp:is_influenced_by
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Learning Rate
Batch Size
Number of Epochs
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gptkbp:is_not_recurrent
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gptkb:True
|
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gptkbp:is_optimized_for
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gptkb:Adam_Optimizer
|
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gptkbp:is_popular_in
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gptkb:Deep_Learning
|
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gptkbp:is_related_to
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gptkb:Artificial_Intelligence
gptkb:Pattern_Recognition
gptkb:machine_learning
|
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gptkbp:is_trained_in
|
Training Set
|
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gptkbp:is_used_for
|
Dimensionality Reduction
Feature Extraction
|
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gptkbp:is_used_in
|
gptkb:health_services
gptkb:speeches
Recommendation Systems
Supervised Learning
Game AI
Financial Forecasting
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gptkbp:requires
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Training Data
|
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gptkbp:sensitivity
|
Overfitting
|
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gptkbp:training
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Backpropagation
|
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gptkbp:bfsParent
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gptkb:Artificial_Neural_Networks
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gptkbp:bfsLayer
|
3
|