From-Scratch

Do you have what it takes to implement that module you love using from scratch? Give it a try and make sure you meet the below criteria

• Ensure that your version implements at least 80% of the original functionality
• Make your code as efficient as you can make it, and learn as you watch others make it more efficient than you can!
• Do not use any functionality from the module you are trying to implement from

Here is how it works?

1. Upload your implementation of an existing module that you think you are capable of implementing. Note: it can be anything e.g it can be something as simple as np.mean or as complex as a sklearn.LogisticRegression or Neural Network if you are daring. This is to ensure everyone on any skill level can participate
2. Provide a short description of the function you are implementing and state the source
3. State the limitation of your implementation against the original implementation i.e what functionality that is in the original implementation that you are unable to implement
4. if possible in your code, leave tasks that other programmers below your skill levels can try to tackle. e.g converting a for loop to list comprehension, finding a way to remove duplicate codes etc. Enables a gradual learning curve
5. Try as much as possible to contain your implementation in a single file. If not possible, all the files should be contained in a single folder and the name of the folder should be the name of the implementation.
6. Your code should contain your linkedin details to give others who wish to improve your implementation connect with you
7. Comment your code!!!

How to contribute to existing custom implementation

• If you see an implementation you think you can improve on without breaking the code, go right ahead and create a pull request! (even if it is a typo or grammatical error!)
• Try as you can to see if you can detect a bug in an existing implementation and hurry to create an issue! (or a pull request if you can solve it!)

NOTE

• Only the author of the custom implementation will be made contributors so that they can handle requests and solve issues made on their implementation and other authors implementations they think they are capable of handling. So go on and implement something!

The benefit of Participating and becoming a contributor

• Learn how to work and collaborate on Github
• Gain a deeper understanding of your favourite module and an even deeper appreciation of the developers
• Increase your Python skills
• Build your portfolio

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A special thanks to Mr Stephen Odaibo founder of RETINA-AI for his daring us to achieve, to push ourselves beyond our limit and Prof. Kris Sankaran Professor of Statistics at the University of Wisconsin-Madison for making the intricacies of Machine Learning algorithms plain to our eyes. You made DSN Bootcamp 2021 a truly amazing learning experience. We the finalist of DSN Bootcamp Finalist truly thank you.

Contributors:

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