Sequences, Time Series and Prediction
GPTKB entity
Statements (23)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Deep Learning Course Module
|
| gptkbp:availableOn |
Yes
|
| gptkbp:focusesOn |
gptkb:Natural_Language_Processing
Recurrent Neural Networks Time Series Prediction Gated Recurrent Units (GRU) Long Short-Term Memory (LSTM) |
| gptkbp:hasAssignments |
Yes
|
| gptkbp:hasQuizzes |
Yes
|
| gptkbp:hasVideoLectures |
Yes
|
| https://www.w3.org/2000/01/rdf-schema#label |
Sequences, Time Series and Prediction
|
| gptkbp:language |
English
|
| gptkbp:notableFaculty |
gptkb:Andrew_Ng
|
| gptkbp:offeredBy |
gptkb:Coursera
|
| gptkbp:partOf |
gptkb:Deep_Learning_Specialization
|
| gptkbp:targetAudience |
Data scientists
Deep learning practitioners |
| gptkbp:teaches |
How to apply deep learning to audio, text, and other sequence data
How to build sequence models How to use LSTMs and GRUs How to use RNNs for time series data |
| gptkbp:bfsParent |
gptkb:TensorFlow_Developer_Professional_Certificate
|
| gptkbp:bfsLayer |
8
|