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We've just published a lot of original, visual, and intuitive explanations of concepts to introduce people to large language models.
It's available for free with no sign-up needed and it includes text articles, some video explanations, and code examples/notebooks as well. And we're available to answer your questions in a dedicated Discord channel.
You can find it here: https://llm.university/
Having written https://jalammar.github.io/illustrated-transformer/, I've been thinking about these topics and how best to communicate them for half a decade. But this project is extra special to me because I got to collaborate on it with two of who I think of as some of the best ML educators out there. Luis Serrano of https://www.youtube.com/@SerranoAcademy and Meor Amer, author of "A Visual Introduction to Deep Learning" https://kdimensions.gumroad.com/l/visualdl
We're planning to roll out more content to it (let us know what concepts interest you). But as of now, it has the following structure (With some links for highlighted articles for you to audit):
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Module 1: What are Large Language Models
- Text Embeddings (https://docs.cohere.com/docs/text-embeddings)
- Similarity between words and sentences (https://docs.cohere.com/docs/similarity-between-words-and-sentences)
- The attention mechanism
- Transformer models (https://docs.cohere.com/docs/transformer-models HN Discussion: https://news.ycombinator.com/item?id=35576918)
- Semantic search
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Module 2: Text representation
- Classification models (https://docs.cohere.com/docs/classification-models)
- Classification Evaluation metrics (https://docs.cohere.com/docs/evaluation-metrics)
- Classification / Embedding API endpoints
- Semantic search
- Text clustering
- Topic modeling (goes over clustering Ask HN posts https://docs.cohere.com/docs/clustering-hacker-news-posts)
- Multilingual semantic search
- Multilingual sentiment analysis
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Module 3: Text generation
- Prompt engineering (https://docs.cohere.com/docs/model-prompting)
- Use case ideation
- Chaining prompts
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A lot of the content originates from common questions we get from users of the LLMs we serve at Cohere. So the focus is more on application of LLMs than theory or training LLMs.
Hope you enjoy it, open to all feedback and suggestions!
I spent a few weeks looking at the top HN posts of all time. This included exploration, clustering, creating visualizations, and zooming in on what (to me personally) seems like some of the best discussions on here.
Three things in this post:
1- The interesting groups of HN posts
2- The interactive visualizations that you can explore in your browser
3- The data from this exploration -- this includes CSV of the titles as well as the text embeddings of 3,000 Ask HN articles.
Blog post about this whole process here: [1]
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1- The interesting groups of HN posts
From the exploration, Ask HN proved the most interesting. These are the top four groups of topics I found insightful. Each group contains about 400 posts.
- Life experiences and advice threads [2]
- Technical and personal development [3]
- Software career insights, advice, and discussions [4]
- General content recommendations (blogs/podcasts) [5]
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2- The interactive visualizations that you can explore in your browser
- Top 10,000 Hacker News articles of all time [6]
- Top 3,000 posts in Ask HN [7]
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3- The data from this exploration
CSV file of top 3K Ask HN posts: [8]
The sentence embeddings of the titles of those posts: [9]
This is a colab notebook containing the code examples (including loading these two data files): [10]
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If you've ever wanted to get into language models, this is a good place to start. Happy to answer any questions