tensorflow / tensorflow/probability
Categorical autoregressive distribution with LSTM example/tutorial
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Description
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked examples/lstm.py file and the surrounding TensorFlow Probability distribution APIs. The issue does not identify an implementation entry point, tests, or a defined acceptance criterion; completion would require establishing the intended categorical autoregressive distribution and producing the requested LSTM example or tutorial.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100