Favorite Repositories - bounswe/bounswe2020group1 GitHub Wiki

react

(by Barış Alhan)

The ReadMe part of the react repository is short and descriptive, with the examples of usage. Instead of a wiki page, the project has a webpage of itself. The folder structure is clean and self-explanatory which enables hundreds of developers to work together.

spaCy

(by Onur Kılıçoğlu)

spaCy is an industrial strength natural language processing module for Python. It has a good readme page which describes all the available features of the module and how to install the module pretty well. The “Where to ask questions?” Section under the readme page is well thought and links to each part of the documentation helps a lot. The labels used for the issues categorizes the issues in a really good way.

Libra

(by Murat Ekici)

Libra Core implements a decentralized, programmable database which provides a financial infrastructure that can empower billions of people. It has many components and each component has it's readme page. In each components readme there is the description of corresponding component and the links to the algorithms they used. It has a complex structure in total but components makes it really easy to understand, codebase has really modular. Also each function in the codebase has its well written description. Issues are well written also. I recently read all the repository and it uses most of the good features on github.

chess-alpha-zero

(by Asena Karolin Özdemir)

chess-alpha-zero is a reinforcement learning project for chess by Alpha Go methods. I really liked this repository, because the README page is explanatory, and shows each step of the project, so that one can follow the development process. Even some parts of the games that are played by the AI model in the repository are included in this section, so that one can observe how the model learned and improved itself after each training step. It is also explained in detail how to setup and use the model.

Manim

(by Ömer Ak)

Manim is a mathematical animation engine. It is used to create precise animations programmatically. It runs on Python 3.7. I think, this is a good repository because its readme page is pretty short and easy to understand. The page includes how to install the engine, how to run it and what types of features it has. This engine needs some other programs to be able to run and links to install these programs can also be found in the readme page.

Atlas

(by Yağız Çolak)

Atlas has a purpose to manage dimensional time series data for near real-time operational insight. It is a backend developed by Netflix, and it features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. Atlas was built because the existing systems Netflix was using for operational intelligence were not able to cope with the increase in metrics they were seeing as they expanded their operations in the cloud. I believe Wiki and Documentation pages of this repository is very neat and understandable. Also all of the issues are thoroughly tagged by the contributors.

App Ideas

(by Buse Kabakoğlu)

App ideas is a repository to provide people some ideas for developing applications. The structure of the wiki page is well-designed so that the application ideas are classified as Beginner, Intermediate and Advanced with links provided for the ideas. After choosing a project, you are directed to the explanation page of the project. There are requirements, bonus features and useful resources and links for the application. Also, there is an example project attached at the end. In the main wiki page, there is a video link for how to use this repository. I am very impressed by the design and easiness of this repository.

Mapsme/Omim

(by Barış Mutlu)

Mapsme is an open source cross-platform offline maps application, built on top of crowd-sourced OpenStreetMap data. It was publicly released for iOS and Android. I like the idea that people can their maps without internet connection. I think its so useful for the travelers who want to visit abroad but don't want to spend money for internet connection. Also its open source and still being developed. Try it and see the benefits..

Scikit-learn

(by Ali Batır)

Scikit-learn is a python module for machine learning built on the SciPy, which is library for scientific computation.Simple and efficient tools for predictive data analysis.Accessible to everybody, and reusable in various contexts.The Readme is very clear and useful to install and handle the dependency problem encountered by developers.Also this repository has a very good coding style guide and contribution guidelines.

TensorFlow

(by Mehmet Çelimli)

TensorFlow is a open-source library to train machine learning models. Labels are well defined and clear in the repository. Piece of code and tables are included in the README. Moreover, the license is specified.

freeCodeCamp

(by Ufuk Karagöz)

freeCodeCamp is an open-source codebase and curriculum. I use this repository because the README page is easily understandable and each step for progress is detailed. The wiki page is empty, but they show every detail on the README page.