Statements (57)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:LSTM
gptkb:Long_Short-Term_Memory_Network |
| gptkbp:analyzes |
accuracy curves
loss curves model architecture diagrams |
| gptkbp:applies_to |
time series data
|
| gptkbp:can_be_used_with |
dropout layers
|
| gptkbp:composed_of |
multiple LSTM layers
|
| gptkbp:enhances |
feature extraction
|
| https://www.w3.org/2000/01/rdf-schema#label |
Stacked LSTM
|
| gptkbp:improves |
model capacity
|
| gptkbp:is_challenged_by |
vanishing gradient problem
exploding gradient problem |
| gptkbp:is_characterized_by |
long-term dependencies
cell states gated mechanisms hidden states |
| gptkbp:is_evaluated_by |
gptkb:municipality
performance metrics F1 score confusion matrix cross-validation precision train-test split |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
gptkb:Keras gptkb:Py_Torch gptkb:Theano MX Net |
| gptkbp:is_optimized_for |
gradient clipping
|
| gptkbp:is_part_of |
deep learning
|
| gptkbp:is_popular_in |
machine learning community
|
| gptkbp:is_related_to |
gptkb:GRU
gptkb:CNN Bidirectional LSTM |
| gptkbp:is_tested_for |
real-world applications
benchmark datasets |
| gptkbp:is_used_for |
financial forecasting
speech recognition video analysis sequence prediction |
| gptkbp:is_used_in |
gptkb:robot
natural language processing climate modeling text generation anomaly detection image captioning music generation game AI healthcare data analysis |
| gptkbp:requires |
large datasets
|
| gptkbp:suitable_for |
vanilla RNN
simple LSTM |
| gptkbp:training |
backpropagation through time
|
| gptkbp:tuning |
hyperparameter optimization
|