Statements (63)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:API
gptkb:television_channel gptkb:Deep_Learning |
| gptkbp:allows |
vanishing gradient problem
exploding gradient problem |
| gptkbp:analyzes |
activation functions
hidden state representations weight matrices |
| gptkbp:applies_to |
speech recognition
video analysis image captioning music generation |
| gptkbp:can_be_used_with |
transformers
attention mechanisms CN Ns |
| gptkbp:characteristics |
gptkb:Magic:_The_Gathering_set
feedback connections |
| gptkbp:developed_by |
gptkb:Yoshua_Bengio
gptkb:David_Rumelhart gptkb:Geoffrey_R._Hinton |
| gptkbp:has_programs |
language translation
sentiment analysis chatbots anomaly detection stock price prediction |
| gptkbp:has_style |
gptkb:GRU
gptkb:LSTM |
| https://www.w3.org/2000/01/rdf-schema#label |
Recurrent Neural Networks
|
| gptkbp:is_characterized_by |
dynamic behavior
temporal dependencies variable input lengths |
| gptkbp:is_evaluated_by |
gptkb:municipality
F1 score accuracy precision loss function |
| gptkbp:is_influenced_by |
regularization techniques
gradient descent dropout batch normalization |
| gptkbp:is_part_of |
deep learning
|
| gptkbp:is_related_to |
gptkb:Artificial_Intelligence
gptkb:television_channel gptkb:software_framework |
| gptkbp:is_used_for |
natural language processing
time series analysis sequence prediction |
| gptkbp:is_used_in |
gptkb:robot
healthcare finance gaming social media analysis |
| gptkbp:requires |
backpropagation through time
|
| gptkbp:training |
gptkb:Adam_optimizer
large datasets time series data text corpora audio data stochastic gradient descent RM Sprop sequential data |