tensorflow / tensorflow/probability
Tutorial on Variational Inference
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- Dominant language
- Jupyter Notebook
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Description
The current tutorials cover a majority of MCMC. Could we get one for variational inference? The edward tutorial on Supervised Learning shows how to run inference using Kullback-Leibler divergence. It would be great if you could provide a similar port over at TFP.
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 existing TFP tutorials and the referenced Edward tutorial on Supervised Learning. Use those as the basis for a comparable variational-inference tutorial using Kullback-Leibler divergence; done means a complete tutorial is added and demonstrates the requested inference workflow.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, tensorflow
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100