Reference & Resources
Below are some incredible resources that I recommend to read or watch if you are interested in learning more about the topics that I covered.
Some of them are great blog posts or videos that I was taught during my time at learning. Some of them are great tools or libraries that I use in my daily work.
Explanations & Guides
Transformer Model Explaination
A great YouTube video clearly explains the concepts of the transformer architecture.
Let's build GPT: from scratch, in code, spelled out.
Andrej Karpathy's famous video tutorial on how to build a GPT model from scratch.
Another video guide to Transformer model by Jay Alammar.
A clear and easy implementation of Andrej's video contents by Mat Miller.
A great blog post that explains the Transformer model in detail.
An implementation by Harvard that explains the Transformer model in detail.
Handwrite the Transformer model code based on paper 《Attention is all you need》.
A great visualization of how the LLM model orchestrates.
GPT — Intuitively and Exhaustively Explained
Exploring the architecture of OpenAI’s Generative Pre-trained Transformers.
A Step-by-Step Math Example
Ecosystem
A platform for sharing opensource models and datasets.
A platform for building, training, and deploying AI models in the cloud.
A platform for video creators to generate AI-powered videos.
A vector DB for similarity search.
A PyTorch extension for Metal Performance Shaders for MAC.