Tech Yourself AI & Data
If you’re diving into AI, data, or machine learning, it can feel overwhelming fast. There’s no shortage of tutorials, online courses, or endless “Top 100 Resources” lists. You need to answer:
- Which topics should you actually learn first, and why?
- Which books, lectures, or tools are worth your time?
- And how do you avoid getting lost in the noise?
This section is my attempt to answer those questions. Think of it as a curated starting point, resources I wish I had when I began, plus notes and reflections from my own learning journey.
| Subject | Why study? | Resources |
| Docker | “It works on my machine” is not a deployment strategy. Containers make sure it works everywhere. | DataTalksClub – Project of the week – Docker Learn Docker and its fundamentals. By the end of the week, you will have the knowledge and skills to create Docker images and run containers. |
| RAG | RAG makes your model less “once upon a time” and more “according to the database…” | Full Stack Retrieval Community This guide explains retrieval in context-aware apps and how to refine it. AI Engineering (O’Reilly) – Chapter 6: RAG & Agents Clear, well-structured explanations of the core theory behind RAG. |
| Computer Architecture | Code doesn’t run on fairy dust. It runs on logic gates, caches, and pipelines. Architecture is where the magic actually happens. | UC Berkeley CS 61C (“Great Ideas in Computer Architecture”) A YouTube Playlist that captures the entire core lecture series |
Personal Takeaways
This is my library of working notes: ideas distilled from the books I read, the courses I take, and the articles that make me stop and think. It isn’t a perfect archive. It’s a living notebook. I try to capture what’s useful, why it matters, and how I’m applying it in the real world.
Browse the latest entries below, or jump straight to Books, Courses, or Interesting Reads. If a note helps you, share it with someone who would benefit.
- Libro: El dilema del prisionero (Poundstone) [Spanish]
My notes and takeaways in Spanish.
