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An implementation of ML and DL algorithms from scratch in python using nothing but numpy.

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ML-DL-implementation

An implementation of ML and DL algorithms from scratch in python using nothing but numpy.

Matplotlib might be used in some places for visualisation.

Why this repository?

This repository gives beginners and newcomers in the field of AI and ML a chance to understand the inner workings of popular learning algorithms by presenting them with a simple to analyze the implementation of ML and DL algorithms in pure python using only numpy as a backend for linear algebraic computations for the sake of efficiency.

The goal of this repository is not to create the most efficient implementation but the most transparent one, so that anyone with little knowledge of the field can contribute and learn.

Contributing to the repository

Follow the following steps to get started with contributing to the repository.

  • Clone the project to you local environment. Use git clone https://github.com/RoboticsClubIITJ/ML-DL-implementation/ to get a local copy of the source code in your environment.

  • Install dependencies: You can use pip to install the dependendies on your computer. To install use pip install -r requirements.txt

  • Installation: use python setup.py develop if you want to setup for development or python setup.py install if you only want to try and test out the repository.

  • Make changes, work on a existing issue or create one. Once assigned you can start working on the issue.

  • While you are working please make sure you follow standard programming guidelines. When you send us a PR, your code will be checked for PEP8 formatting and soon some tests will be added so that your code does not break already existing code. Use tools like flake8 to check your code for correct formatting.

Finding "Issues" for the Choosen Repository

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  • Now, many Issues will be visible, with their suitable labels for different purposes.

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  • According to the conveinence, the Issue can be choosen by clicking on their title. In order to provide characteristics of an Issue, well-defined labels are supported with it. Click on "Labels" icon , on the left side of search bar.

"Good First Issues"

  • The "Labels" icon will lead to definitions of each label, associated with Issues of Repository. In addition to this, Number of pull requests and open changes are also mentioned on the right side of definitions.

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An implementation of ML and DL algorithms from scratch in python using nothing but numpy.

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  • Jupyter Notebook 61.6%
  • Python 38.4%