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Neuromath + ML (linear algebra + concepts, regression, optimization, curve fitting, stats, circular stats, graph theory) tutorial

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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

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

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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