https://github.com/Sentdex/NNfSiX

Neural Networks from Scratch in various programming languages
https://github.com/Sentdex/NNfSiX

Last synced: 14 days ago
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Neural Networks from Scratch in various programming languages

• Host: GitHub
• URL: https://github.com/Sentdex/NNfSiX
• Owner: Sentdex
• License: mit
• Created: 2020-04-16T19:23:37.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-09-24T22:01:43.000Z (about 1 year ago)
• Last Synced: 2024-10-16T09:06:07.636Z (28 days ago)
• Language: C++
• Homepage: https://nnfs.io
• Size: 951 KB
• Stars: 1,433
• Watchers: 94
• Forks: 373
• Open Issues: 43
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Neural Networks from Scratch in `X`

The idea here is to share [Neural Networks from Scratch tutorial parts](https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3) / [Neural Networks from Scratch book](https://nnfs.io) in various other programming languages, besides just Python. 

If you're following along in another language, feel free to contribute to your specific language via a pull request. If you're considering trying to use another language, you can see how others have achieved writing the neural networks from scratch in other languages here too.

# How to contribute: 

The plan is to share code from video part #s. We will be updating the Python variants, so, if you are just following the [book](https://nnfs.io), you can see which block(s) of code should be translated by checking out the Python versions.

Before submitting a PR, check to see if there's already a PR from someone for that specific part # and language.

Submission for given part number should be complete in a single file, ie. should not be split across multiple files. This makes it easier for us to maintain the repository amd easier for others to follow.

If you want to submit solutions in multiple languages, or your submission adds new files and changes files of someone else, create separate PRs for these cases.

We will be accepting PRs in the following ways:

1. Prioritizing the already-established people who have contributed to that language already.

2. Prioritizing by who submits first.