Looking to break into ML and I kind of lose track of what I've been doing so I figured I'd just document everything here.
- Finished tidying up a repo with notes that I'd taken down on Karpathy's course. Currently we have part 1,2 and 3 in there.
- Read up a bit on the paper that he mentioned - A Neural Probablistic Model which mentions the use of a real-number vector to represent words. This is currently used extensively in NLP and is known as word embeddings but back then I'm sure it must have been a novel idea.
- Played around with the new Next Auth Kysely integration and Resend and wrote a quick article here - Started working on a small tool as part of Buildspace s4 to help people prep for interviews using GPT-4 and some other models called Prep With AI which uses a bunch of the different things that I wrote about
I wasn't able to do as much as I wanted due to reservice commitments but I did manage to get a few things done.
Discovered Andrej Karpathy's Zero to Hero course and plan to start working through it through August. So far I've finished up with his intro to neural networks and I built a basic binary classifier which has ~42% accuracy using a custom neural network I coded in vanila python. Finished up with the first 2 chapters of his course and I'm really enjoying it so far.
Finally figured out how to deploy langchain on AWS lambda and spent my entire weekend trying to automate a 20 min task with aws sdk
June has just started and my plan now is to work on more applications of LLMs. I believe that using LLMs to augment my learning will help tremendously when it comes to generating new insights and finding interesting angles to explore.
The plan is to build a local LLM using gpt to be able to query and discover new insights about my previous notes and chats. I tried implementing a basic clone with memory and embeddings here but ended up getting side tracked with other ideas.
I also started experimenting with Open AI Functions and built out a simple classifier using Yake and GPT that was able to classify places that I had been to before using my reviews and other metadata ( Link )
I've managed to finish up Part 1 of Fast AI's course and boy have I learnt a lot about machine learning in general. The course seems to cover a lot more of traditional machine learning techniques and there's a lot which I'll definitely need to revisit. You can read my notes here FastAI Part 1