Neuromath + ML (linear algebra + concepts, regression, optimization, curve fitting, stats, circular stats, graph theory) tutorial
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- jupyter-notebook, python
- Domain
- documentation, machine-learning
Research direction
Start by reviewing the repository's existing tutorials and the broad topic list in this issue. Before implementation, narrow the scope and define which notebooks should cover vectorization, machine learning concepts, regression, optimization, curve fitting, statistics, circular statistics, and graph theory; done means the agreed tutorial material is complete and runnable.
Written by the indexing model from the issue text.
Description
- Tricks for vectorizing
- "What is machine learning?"
- Dominant language
- Jupyter Notebook
- Stars
- 17
- Forks
- 4
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
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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