🔴 Welcome contributors!

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture. Deep Learning Simplified is an Open-source repository, containing beginner to advance level deep learning projects for the contributors, who are willing to start their journey in Deep Learning.

Structure of the Projects 📝

This repository consists of various machine learning projects, and all of the projects must follow a certain template. I wish the contributors will take care of this while contributing in this repository.

Dataset - This folder stores the dataset used in this project. If the Dataset is not being able to uploaded in this folder due to the large size, then put a README.md file inside the Dataset folder and put the link of the collected dataset in it. That'll work!

Images - This folder is used to store the images generated during the data analysis, data visualization, data segmentation of the project.

Model - This folder would have your project file (that is .ipynb file) be it analysis or prediction. Other than project file, it should also have a '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.

🧮 Workflow

• 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.
• Project admin will evaluate your PR and provide remarks accordingly. If it satisfies all the criterias, your PR will be merged and your contributions will be counted.

❄️Open Source Programs!

SSOC 2022

SSOC 2023

SWOC 2023

CodePeak 2023

SWOC 2024

🤔 New to Open Source programs/events!

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

🏆 Achievements of this Project Repo 🎉

:one: Recognized as the "🥇 TOP PROJECT ADMIN" for Social Summer of Code, for the year 2022. :two: Recognized as the "🥇 TOP PROJECT ADMIN" for Social Winter of Code, for the year 2023. :three: Recognized as the "🥇 TOP PROJECT ADMIN" for Social Summer of Code, for the year 2023.

✔Project Admin

Abhishek Sharma

✨Top Contributors

Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀

⭐Give this Project a Star

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

🎉 🎊 😃 Happy Contributing 😃 🎊 🎉

📬 Contact

If you want to contact me, you can reach me through social handles.

© 2023 Abhishek Sharma