As a mentor to many aspiring ML engineers, I have seen that it’s not easy to find trustworthy resources to learn Generative AI yet. One of the best platforms to learn anything in AI for free is GitHub. So, today, I’m going to walk you through the best repositories I recommend to every fresher to master Generative AI.
Best GitHub Repositories to Master Generative AI
Below is a curated list of three repositories that form a complete roadmap. This is the guide I wish I had when I started. It will take you from understanding the nuts and bolts to building production-ready Generative AI applications.
Andrej Karpathy’s nanoGPT: Build Your Own GPT from Scratch
Before you can effectively use powerful tools, you need to understand the engine. Andrej Karpathy, a founding member of OpenAI and former Director of AI at Tesla, is a legend when it comes to teaching complex concepts from first principles. His nanoGPT repository is a masterpiece of simplicity.
This repository teaches you how to build a Generative Pre-trained Transformer (GPT), the architecture behind models like ChatGPT, from scratch, in plain Python and PyTorch. You won’t just be importing a library and calling a function. You’ll be implementing the attention mechanism, building the transformer blocks, and training the model on a small dataset.
Don’t just clone the repo and run it. Open a blank notebook and try to code it line by line yourself, following his video lecture. Find this GitHub repository here.
Microsoft’s generative-ai-for-beginners: Your University-Grade Course
Sometimes you need a syllabus. You need a structured path that takes you from lesson one to the final project. Microsoft’s Applied AI team has built exactly that with this incredible, free, and open-source course.
This is a 21-lesson, comprehensive course that covers the entire Generative AI landscape. You’ll learn about everything from prompt engineering and Large Language Models (LLMs) to building your own text and image generation apps. It even includes lessons on building a GenAI startup. It’s a full-fledged curriculum from one of the world’s leading AI companies.
Do share this repository with your friend. Working through the projects in this repository with someone else will keep you accountable and provide a valuable sounding board when you get stuck. Find this GitHub repository here.
Hugging Face’s diffusers: Master Image Generation
Generative AI isn’t just about text. The world has been captivated by AI-generated art, and the technology behind it is Diffusion Models. Hugging Face is the undisputed hub for the machine learning community, and their diffusers library is the industry standard for image, audio, and 3D generation.
This library abstracts away the insane complexity of diffusion models, allowing you to generate stunning visuals in just a few lines of code. But more importantly, it’s designed to be explored. You can easily peek under the hood, swap out components like schedulers and UNets, and see how they impact the final image.
Once you’ve generated an image, don’t stop there. Change one parameter at a time to understand:
- What happens if you increase the num_inference_steps?
- What if you switch from a DPM++ scheduler to an Euler scheduler?
This experimentation builds an intuitive understanding of how these powerful models work. Find this GitHub repository here.
Summary
Here are my three favourite GitHub repositories that form a comprehensive roadmap to master Generative AI:
I hope you liked this article on the best repositories I recommend to every fresher to master Generative AI. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
