Artificial Intelligence (AI) is rapidly transforming the world we live in. AI allows computer systems to perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI is a complex field, but it is becoming increasingly accessible to developers of all skill levels.

World of AI is an open-source repository by CognitiveLab, containing beginner to expert level AI-based projects for the contributors, who are willing to start their journey in Artificial Intelligence and Deep Learning.

Sure, here's the updated project structure with the categories before the folder structures:

This repository consists of various AI-based projects, and all of the projects must follow a certain template. We wish the contributors will take care of this while contributing to this repository.

We have 3 categories of projects:

AI projects need to be complete projects that can be put out into the world and used by people. They should have a user interface, preferably using MERN, FARM, Gradio, or Streamlit. AI projects will be a good learning experience and preferably use libraries like Langchain to work with LLMs. Some examples of AI projects include:

• A chatbot fine-tuned with your own data for specific needs (e.g., trying to chat with a PDF)
• Detecting fake news using deep learning
• Image classification using transfer learning

For each AI project, we have the following folder structure:

• Project Name 📁 - This folder is named after your project and must be in kebab-case. It should contain all the project assets.
  • Streamlit 🚀 - This folder is used to store the Streamlit app that uses the trained model. The README.md in this folder should have the instructions to run the Streamlit app.
  • Readme - Follow the following template

DL projects are intermediate-level projects and need not be production-ready, but having a demo using Streamlit would be nice. These projects will include image processing, audio processing, and other deep learning-related projects. Some examples of DL projects include:

• Object detection using YOLO
• Speech recognition using deep learning
• Generative adversarial network (GAN) for image generation

For each DL project, we have the following folder structure:

• Project Name 📁 - This folder is named after your project and must be in kebab-case. It should contain all the project assets.
  • Dataset 📁 - This folder stores the dataset used in this project. If the dataset is too large to upload, create a README.md file inside the Dataset folder and provide a link to the dataset.
  • Images 📷 - This folder is used to store the images generated during the data analysis, data visualization, data segmentation of the project.
  • Model 🤖 - This folder contains your project file (i.e., .ipynb file) for analysis or prediction. Other than the project file, it should also have a README.md file using this template and a requirements.txt file which would be enclosed with all needed add-ons and libraries that are included in the project.
  • Streamlit 🚀 - This folder is used to store the Streamlit app that uses the trained model. The README.md in this folder should have the instructions to run the Streamlit app.
  • Readme - Follow the following template

ML projects are beginner-friendly and will mainly constitute datasets from Kaggle. These projects can include:

• Regression problems
• Classification problems
• Clustering problems
• Time-series forecasting problems

For each ML project, we have the following folder structure:

• Project Name 📁 - This folder is named after your project and must be in kebab-case. It should contain all the project assets.
  • Dataset 📁 - This folder stores the dataset used in this project. If the dataset is too large to upload, create a README.md file inside the Dataset folder and provide a link to the dataset.
  • Images 📷 - This folder is used to store the images generated during the data analysis, data visualization, data segmentation of the project.
  • Model 🤖 - This folder contains your project file (i.e., .ipynb file) for analysis or prediction. Other than the project file, it should also have a README.md file using this template and a requirements.txt file which would be enclosed with all needed add-ons and libraries that are included in
  • Readme - Follow the following template 'README.md' using this template and 'requirements.txt' file which would be enclosed with all needed add-ons and libraries that are included in the project.
    
    Please follow the Code of Conduct and Contributing Guidelines while contributing in this project repository.

• Go through the project repository and the README to get an idea about this repository.
• Check out the existing issues present there in the Issues section.
• Comment out in the issue, you wanna work on.
• Wait for the issue to be assigned to you. Once it's assigned to you, start working on it.
• Fork the repository.
• Clone your forked repository using terminal or gitbash. Also you can simply use the web version of GitHub to add your files.
• Make changes to the cloned repository.
• Add, Commit and Push.
• Then in Github, in your cloned repository find the option to make a pull request.

Here are few articles which will help you to get an idea on how you start contributing in open source projects, You can refer to the following articles on the basics of Git and Github.

• Watch this video to get started, if you have no clue about open source
• Forking a Repo
• Cloning a Repo
• How to create a Pull Request
• Getting started with Git and GitHub

Girl Script Summer of Code

Adithya S Kolavi 💻

SK MIRAJ 💻

Thanks to these wonderful people for their contributions!

If you found this project helpful or you learned something from the source code and want to thank the developers, consider giving the repo a star. It means a lot to us! ⭐

If you liked working on this project, do ⭐ and share this repository.

© 2023 CognitiveLab

This project is licensed under the MIT License

If you want to contact us, you can reach us at [email protected] or [email protected].

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