Every once in a while I ran into some interesting resources and I never know where to put them. Somehow bookmarks don’t seem to work for resources that I use less frequently but I never know where to find them when I need them, so I decided to list them in this blog. That way, other can find them.
Data Science
A cool end-to-end course showing both local and remote DS life cycle steps : https://madewithml.com/
(Spanish) A bit outdated but still useful Aprende con Alf
People Analytics
Fundamentals of People Analytics with Applications in R
Some details on how to do regression modelling applied to People Analytics
Finance
This is a must for those working in Finance
Deep Learning
Must you must know:
- Deep Learning
- Understanding Deep Learning
- Really useful cheatsheets https://stanford.edu/~shervine/teaching/
- Some cool visualizations that help better understand the concept:
LLMs
I can really recommend Sebastian Raschka’s magazine to deep dive into concepts and architectures. But there are many other resources available out there:
- Foundations of LLMs
- Applied LLMs has quite some resources you can explore
- How to scale them
- BentoML’s take on inference
- Anthropic Academy
- Exercises from the Book Hands on Large Language Models