Repository Research
🔍Author: Orhun Görkem
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. It has several advantages such as:
- Building and training ML models easily using intuitive high-level API's.
- Available infrastructure to train and deploy models in the cloud, in the browser, or on-device.
- A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.
- Readme page simply explains how to build and use the software with a brief example. This definitely does not intimidate the users with some complicated procedure before starting.
- Every section (or item) has colored labels that represent the content or current status, which seems energetic visually and provides efficient reading.
- Readme page is neat and brief, generally redirecting to other pages with links for additional details.
Author: Kıymet Akdemir
VMAF is a perceptual video assessment algorithm developed by Netflix. Video quality evaluation is a pretty difficult job in comparison to image quality evaluation but VMAF provides good results in perceptual quality assessment. You can read an overview blog from here.
- Readme page is clear and well structured.
- There are many ways to contribute and they are explained in detail in
Contributing.mdfile. - Vmaf has many different usages such as Python library, C library, using with Matlab or FFmpeg. Usages part in readme page directs user to a detailed document about how to build vmaf and an example to run which is very helpful to inexperienced users.
- There is a link of Google form to report if you think algorithm results are inconsistent with perceptual quality of your videos. You can upload your video samples and describe the problem. I think this feedback method is very useful to improve an algorithm.
Author: Burak Onur Duman
- vid2vid is a Pytorch implementation for high-resolution photorealistic video-to-video translation.
- By using the first frame of the video and the data representing the facial expressions and the head's pose, it can reconstruct the video in a much more efficient manner compared to the other methods. It can also transform the head movements of a person to another person's face by gathering head movement data from one person and using another person's head as the frame input.
- You can check this video which shortly explains the algorithm.
- The readme file in the repository describes the project in a clear and understandable way. There are also several useful links to related content.
- Instructions on how to use the library are very detailed.
- There are great output examples provided in the readme section.
Author: Ufuk Arslan
Flutter is a software development kit created by Google. It allows developers to build natively compiled applications for mobile, web, and desktop from a single codebase. Flutter’s widgets use Dart platform which removes the issue of dealing with differences between platforms and makes your application compatible with iOS, Android, web, and desktop. Flutter uses something called Stateful Hot Reload which allows developers to play around with the app without waiting for long compilation times. In addition to those, with a great and flexible UI and being free to use, Flutter has become one of the most used cross-platform mobile development frameworks among developers.
- The readme file is very informative and well designed. You can easily find the links to necessary information like installation and documentation pages. It explains the main features clearly, which helped me to understand what Flutter actually offers.
- There are lots of labels, each to represent a different aspect of an issue. They are mostly commented on and color-coded for sorting.
- The wiki page is very organized with notable sections, headers, subsections, etc.
Author: Sevde Sarıkaya
DeepFaceLab is an open-source deepfake system created by iperov for face swapping with more than 5.7k forks and 25.5k stars in Github. One can de-age the face, replace the head, manipulate lips in a video by using DeepFaceLab. It requires no comprehensive understanding of deep learning framework.
- The readme file is perfect!
- It includes different samples and It shows the differences clearly.
- It shows the improvement of DeepFaceLab year by year.
- There are tutorials, releases, supplementary materials and communication groups!
- There is an article which explains the project deeply.
- There are 18 contributors.
- There are different ways of helping the project.
Author: İrem Zeynep Alagöz
Markdown Here is a Google Chrome, Safari, Firefox, Opera, and Thunderbird extension that allows you to write emails in Markdown language. You can render your email before sending it. Markdown Here provides a variety of features such as:
- Entirely customizable styling.
- Supporting Google Group, Evernote, and WordPress besides emails.
- Adding code blocks and syntax highlighting if you specify the programming language you are using.
- Sending TeX Mathematical formula in your email.
- Creating tables easily.
If you are wondering what else you can do with Markdown, you can read the cheatsheet or follow other wiki pages.
- Readme page is well organized. Pictures used made the page more descriptive. Besides, the table of contents part provides fast access to the requested information. There are also links for setup and more details.
- There are informative wiki pages for necessary sections. The homepage has questions and links to these wiki pages to guide us. Each page contains detailed explanations and helpful links.
- There are useful cheatsheets for Markdown and Markdown Here. Each topic is explained with examples and very well summarized. Hence, these cheatsheets speed up the learning process.
- Repo also includes a troubleshooting section which might help solve users' common problems.
Author: Bekir Keldal
finmarketpy is a Python based library that enables users to analyze market data and also to backtest trading strategies using a simple to use API. It was firstly introduced as pythalesians. Since the merge of pythalesians and finmarketpy in September 2016, it has continued as finmarketpy.
The library allows investors to:
- Use the prebuilt templates for backtesting trading strategies
- Detect seasonality of the strategies
- Analyze historical returns
- Use in built calculator for risk weighting using volatility targeting
It has a really well-constructed and comprehensive readme section. It includes detailed installation guide for users, sample analyses and models that investors can create via the library and a coding log which contains almost every important step of the project since 2015. There is a planned features section for the future contributors as well. Some useful articles and links are also provided for users in readme.
Author: Halil Baydar
Eureka is a Java library, which's created by Netflix. It is a representational state transfer-based service, which is being used to facilitate api-gateway for load balancing among microservices and provides a communication line to microservices without any connection point. Thanks to keeping the list of all services, any API gateway, i.e. Netflix/Zuul API gateway, can control the traffics between clients and servers without knowing the ports of services, the number of services, etc.
It can be also used
- For Cassandra deployments to take instances out of traffic for maintenance.
- For Memcached caching services to identify the list of nodes in the ring.
- For carrying other additional application-specific metadata about services for various other reasons.
Importing this library to a microservice project is just a piece of cake because it is supported by Maven and Gradle project automatization tools. In addition, it is documented perfectly for each of its versions, you may not need any other resource in order to use this library. And its documentation was cut into leading sections, due to that you do not lose yourself in its docs. It has 26 issues which cover all exception and errors you may encounter.
Author: Emre Yılmaz
lichess.org is a open-source chess platform which has millions of users. They mean it when they say platform is open-source. Their git-hub repository is well-structured and easily accesiable by anybody. Even who plays chess and has a little bit of programming experience, can get a use of their platform by using their apis. And even their databases are open to everyone, too.
- I made a chess-related website, so main reason i love this repository is that it gives everything you need to learn if you are up to work anything related chess.
- It is open-source in any sense. Even their databases are open-source and anybody can access data of millions of chess games and puzzles.
Author: Kutay Saran
LGTM is a code analysis platform for identifying vulnerabilities and preventing them from reaching production.It automatically runs 1600+ standard analyses contributed by researchers from the Semmle Security Research Team and a customer community, including Microsoft, Google, Uber and Mozilla.LGTM's automatic code review for pull requests only notifies you of new (and fixed!) alerts. This way, we can detect critical problems early and fix them before they’re merged!
- LGTM analyzes every commit of our project, so we can see how our alerts have changed over time.
- We can view the alerts for the latest commit of our project, smartly prioritized based the project history and alert severity.
- We can focus on the results that are most important for us thanks to the extensive filters.
