> For the complete documentation index, see [llms.txt](https://ai.saikatkumardey.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai.saikatkumardey.com/best-llm-resources-on-the-internet.md).

# Best LLM Resources on the internet

### Talks

* [The Amazing AI Super Tutor for Students and Teachers | Sal Khan | TED](https://www.youtube.com/watch?v=hJP5GqnTrNo)

### Lecture Videos

* [Let's build GPT: from scratch, in code, spelled out](https://www.youtube.com/watch?v=kCc8FmEb1nY) by Andrej Karpathy
* [GPT-4 - How does it work, and how do I build apps with it? - CS50 Tech Talk](https://www.youtube.com/watch?v=vw-KWfKwvTQ)
* Large Language Models from scratch: [part 1](https://www.youtube.com/watch?v=lnA9DMvHtfI) + [part 2](https://www.youtube.com/watch?v=YDiSFS-yHwk)

### Courses

* [CS 324 - Large Language Models](https://stanford-cs324.github.io/winter2022/)
* [NLP Course by Huggingface](https://huggingface.co/learn/nlp-course/chapter1/1?fw=pt) : a really great course for understanding transformers, which is the backbone for LLMs.
* [COS 597G: Understanding Large Language Models](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/)
* [CSCI 601.771: Self-supervised Statistical Models](https://self-supervised.cs.jhu.edu/fa2022/#schedule)

### Blogs

* <https://jalammar.github.io> => intuitive posts on language models & transformers. Great visuals.