[TODO] More optimizers
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 30/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- julia
- Domain
- machine-learning
Research direction
Start by locating the existing SGD and Adam optimizer implementations in MXNet.jl and read the linked update references, especially the Lasagne and Keras implementations. Determine how another optimizer such as RMSProp should fit the package's existing interface, then verify that the new optimizer works consistently with SGD and Adam.
Written by the indexing model from the issue text.
Description
Currently we have SGD and Adam. Should be relatively simple to add more, like RMSProp, etc. Some references:
- Dominant language
- No language data
- Stars
- 370
- Forks
- 63
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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