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Give a short description of the basic overview of the steps you have taken/will need to take to finish your project.

When you first take a look at a GitHub repository you see a brief description of a project, maybe an image/logo.
Is it a computer vision project? Reinforcement learning? How/why is it interesting? Where can it be used going forwards?

Descriptions should be short and sweet, needing little to no "domain knowledge" to interpret.
Try creating one within the README.md file!

Give a brief overview of who works on the project.
You can talk about the people themselves, or just their roles within the project.

You can also give an outline of how people can contribute to your project.
An example of this would be outlining your recommended Git workflow (covered in the workshop slides), or how to install the software you need to use run project.
For extra credit talk about hardware requirements (i.e. graphics card specs are needed, RAM and CPU specs) and how can change.

Do something to purposefully create a Git conflict, and get someone else in your team to fix it!
Do help them out though... 🤣

You've done a lot of work on your project and you definitely have achieved a lot.
Now it is time to explain how you've done that.
If there are any major issues you've experienced along the way (which could help others) mention them here (even if you're not completely finished solving them).

Give a brief overview of the libraries, frameworks and key concepts you've used.
Briefly describe what each one does.

Here you may also describe any decisions you have made.
An example of this would be justifying why PyTorch was used over TensorFlow.

Feel free to just do a number of dot points like below:

• PyTorch - Pythonic Neural Network library for Deep Learning
• PyTorch-Lightning - A standard way to write PyTorch code (for research projects)
• Apache Spark - Preprocess large amounts of data efficiently
• AllenNLP - Experimental library for research into Natural Language Processing (used instead of Hugging Face Transformers for extra flexibility and modularity)

Your short and sweet description got the readers interested, but now you need to go into more depth about what you're doing.
Explain what you're doing in the full amount of depth.
It can be technical, but should still stay easy enough to read!

Words are fine, but sometimes pictures are easier to interpret/understand.
Try adding a few into the readme file!

You can add in diagrams, memes to lighten the mood, emojis 🤖, model outputs or anything else.
It'll look good, trust me 😅!