Dive into the Popularity of Top Machine Learning and Deep Learning GitHub Projects
GitHub, a popular platform for software development, hosts an extensive collection of online folders known as repositories. Each repository contains all the files related to a single project and can be made public or private. In a recent study, the author analysed the most popular Machine Learning and Deep Learning repositories on GitHub, collecting data on 2,000 repositories using Python scripts and the PyGitHub library.
The study revealed that the majority of the most popular repositories were created between 2016 and 2020. The author used the number of stars as a metric to determine popularity and identified the top 25 individuals and top 25 organizations based on the number of followers and stars respectively, collecting data on their public repositories on GitHub.
GitHub repositories offer a range of features that make them indispensable for modern software development. Version control is one such feature, with GitHub repositories using Git to track every change to files, allowing users to revert to previous versions and providing a complete history of project evolution. Developers can create branches to work on new features or fixes independently, then merge their changes back into the main project via pull requests, ensuring smooth collaboration and code review.
Pull requests facilitate code review and discussion, allowing team members to propose changes, comment on code, and approve modifications before they are integrated into the main codebase. Each repository includes tools for tracking bugs, feature requests, and tasks through Issues, while Boards and Projects help organize and prioritize work, supporting agile workflows.
GitHub Actions allow users to automate workflows directly within the repository, such as building, testing, and deploying code, by triggering custom actions on events like commits or pull requests. This boosts productivity and reduces manual errors. GitHub Pages enable users to host static websites directly from a repository, ideal for documentation, project pages, or personal portfolios.
Package Management is another key feature, allowing users to publish, share, and reuse software packages privately or publicly using GitHub’s secure package hosting for efficient dependency management. Built-in tools scan for vulnerabilities and improve the security of the software supply chain, helping teams maintain secure and reliable codebases. Collaboration Tools like Discussions, Wikis, and centralized documentation hosting make it easy for distributed teams to communicate, share knowledge, and maintain transparency.
GitHub is widely used for open-source development, private and commercial projects, education and learning, portfolio showcase, Continuous Integration/Continuous Deployment (CI/CD), package distribution, and more. GitHub offers centralized code and documentation hosting, with tools to manage and release code from millions of repositories. Each team member can access the repository, view past versions, and work on the same project simultaneously, no matter where they are.
The study also provided insights into the most popular programming languages used in the collected repositories, with Python being the most popular. Figure 9 provides a look into the top 5 programming languages that these organizations use. The study also showed a large number of individuals located in the United States and China, and several individuals scattered throughout countries in Europe. Figure 6 presents the mean latitude and longitude values for all of the individuals who provided valid location details.
In conclusion, GitHub repositories are a cornerstone of modern software development, providing a comprehensive suite of tools for collaboration, automation, and secure code management—catering to both individual developers and large enterprises. The study provides valuable insights into the most popular repositories, programming languages, and locations of contributors in the Machine Learning and Deep Learning domains.
Data-and-cloud-computing played a crucial role in the aforementioned study, as Python scripts and the PyGitHub library were used to collect data from GitHub repositories. The study focused on the most popular Machine Learning and Deep Learning repositories, revealing that many of these repositories were created between 2016 and 2020.
Science and technology intertwined in the study, as the author utilized GitHub repositories for analyzing machine learning and deep learning projects, showcasing how these platforms are instrumental in contemporary science and technology advancements.
