You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Machine Learning & AI: Introduction, project objective, data collection, data preprocessing, feature engineering, model development, model evaluation, results and visualization, deployment, usage, contributing, license, authors, acknowledgements.
Open Source Library: About, features, installation, API documentation, examples, versioning, issues, contributing, code of conduct, license, authors, acknowledgements.
Web App: About, technologies used, system requirements, setup and installation, usage instructions, API endpoints, contribution guidelines, code of conduct, tests, deploying, changelog, license, authors, acknowledgements.
Data Science Project: Project overview, problem statement, data, exploratory data analysis, data cleaning and preprocessing, model building, model evaluation, conclusion, references, license, authors, acknowledgements.
Thank you!
What README formatting option wou want implemented in README-AI?
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
-
Hello everyone! Let's dive into a poll about README file options for the README-AI tool. Curious to learn more about what format of README people use:
Academic Research: Project overview, hypothesis, methodology, dataset, analysis, results, discussion, conclusion, future work, references, license, authors, acknowledgements.
Machine Learning & AI: Introduction, project objective, data collection, data preprocessing, feature engineering, model development, model evaluation, results and visualization, deployment, usage, contributing, license, authors, acknowledgements.
Open Source Library: About, features, installation, API documentation, examples, versioning, issues, contributing, code of conduct, license, authors, acknowledgements.
Web App: About, technologies used, system requirements, setup and installation, usage instructions, API endpoints, contribution guidelines, code of conduct, tests, deploying, changelog, license, authors, acknowledgements.
Documentation Project: Introduction, getting started, basic concepts, advanced concepts, troubleshooting guide, FAQ, glossary, best practices, contributing, license, authors, acknowledgements.
Mobile Application: Introduction, features, requirements, installation, running the application, screenshots, contributing, tests, deployment, known issues, license, authors, acknowledgements.
Project Management: Project definition, goals, scope, timeline, resources, risks, communication plan, progress tracking, change control, closure, post-project review, license, authors, acknowledgements.
Data Science Project: Project overview, problem statement, data, exploratory data analysis, data cleaning and preprocessing, model building, model evaluation, conclusion, references, license, authors, acknowledgements.
Thank you!
2 votes ·
Beta Was this translation helpful? Give feedback.
All reactions