About

This is a collection of notebooks for folks who:

• want to begin an AI journey. It assumes that you are starting with next to no knowledge of Jupyter, python, or AI.
• need to fill in gaps in skills. The workbooks help illustrate topics that are frequently misunderstood.

If you have any suggestions on how to improve this, please do let me know.

Note: for best results, I suggest using Google's Colab service. There is a link at the top of each workbook to make getting started as easy as possible. All workbooks will function at the free tier. If you want to run these notebooks locally or in another service and you run into issues, please let me know, I'll do what I can to help.

Intro

The workbooks are grouped by topic and increase in difficulty as you go through each section. Each workbook is 100% stand alone. You do NOT need to complete a prior workbook to do one that interests you. This means you are free to jump around and work on these labs as you choose. However, if you do not have skills or knowledge to understand what is happening in a workbook, there are prior ones you can go back to and experiment with until you are more comfortable.

Foundations

1. Intro to Jupyter Lab workbooks
2. Intro to Python
3. Helpful Vocabulary

Embeddings

4. Space is meaning
5. Tokens
6. Embeddings

Vectorization of data

7. Text vectorization
8. Audio vectorization
9. Image vectorization
10. Vector math

Applying what we've learned

11. Sentiment analysis
12. Classification
13. Filling in the blanks
14. Apache log analysis
15. LLM problems

Diving Deeper

16. RAG
17. How transformers work in detail (very low level)
18. Vector database considerations
19. Tokenizer issues

Next Steps

20. Suggested resources for additional learning