[Day 87] Registered for Stanford's XCS224W: Machine Learning with Graphs + RAG webapp with llama-index tutorial

 Hello :)
Today is Day 87!


A quick summary of today:
  • after a lot of thought and considerations I finally registered for one of Stanford's AI professional program courses - XCS224W 


Maybe 2 weeks ago I did the application for Stanford's AI professional program, and since I got accepted I have been thinking about taking one of the courses. ML with Graphs is one of primary interest to me because of its application in financial fraud, but more than that - it is a lot of money (thankfully my parents support me taking such high level education courses). Yes the quality, it is top notch, but besides Stanford's AI professional certificate program which includes the below courses (to get the certificate I need to complete 3)

I was heavily considering MITx MicroMasters in Statistics and Data Science. Online there are a lot of reviews of it being very hard, requiring a lot of hours, and this really made me interested in doing it. The MicroMasters covers:
4 courses and 1 capstone exam, and the price is significantly lower than even 1 of stanford's pro certificate courses. But the thing is, it covers topics that I have studied extensively so far, and moreover, will cover in my statistics class at uni this semester (probability and statistics). I asked in reddit, discord servers with ML students. 

This was probably half my day, and then I discussed it with my parents, and they supportively said it is okay whatever I decide. 

I thought about it, and because I know Stanford quality, I have taken the free versions of their courses, I know the quality, I will commit and trust them. Later, the MicroMasters courses are significantly cheaper, so if I decide to try them, I can.

So ~~~ ML with Graphs officially starts on the 1st of April and I am looking forward to learning about graphs, be able to chat with other students.  


Now, about the RAG react webapp with llama-index

It was in deeplearning.ai's platform and sadly it uses an OpenAI api key (though they provide one for free). To set up the app we can do the following steps:

1. Set up documents, index and query engine

2. Create a query handler

3. Set up a react frontend

And the results are:



Then, the teacher talked about advanced querying where we can use and get info from multiple data sources.
We set up the code with 2 query engines: 1) for questions about Dan Abramov, and 2) for questions about react

And when we query the queryEngine with queries about either item, we can get:

Next, we learn about agents - creating arbitrary functions and an agent knows how to use those

Example:

1. Define a simple function

2. Explain the function in a JSON structure
3. Turn the function into a tool
4. Turn the router query engine into a tool too and combine the previously created queryEngine (the one that can answer Qs about Dan Abramov and react)

5. Query and get a respone

And finally, all of the above was combined to created a RAG React webapp with llama-index

As for some other materials, I found these:

An Introduction to Statistical Learning - and I might go over it, taking notes by hand.

Deep Learning with PyTorch Step-by-Step A Beginner's Guide - maybe, if I can check out what's inside. 

XCS224U - Natural Language Understanding - all lectures and homeworks.

So there are definitely things to do. ^^


That is all for today!

See you tomorrow :) 

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