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Going further

Going further

If you arrived from the previous entry, here are the resources that did not fit into the three itineraries: deeper, more specific or more niche, but with the same criteria as always — free and well regarded. None of this is compulsory: it is a menu, not a checklist.

Free reference books

Specialising by area

Reinforcement learning

NLP and LLMs

MLOps and production

  • Made With ML — Design, development and deployment of ML systems, with a focus on good practice.
  • MLOps Zoomcamp (DataTalks.Club) — A free course with cohorts, projects and a very active community.
  • Full Stack Deep Learning — The pioneering course on taking models to production. No longer updated frequently, but the material remains useful.

Ethics, justice and safety

Tip

This subsection is the hinge with the third entry of the chapter. “AI ethics” as taught in these courses is above all an ethics of implementation —how to audit a model, how to measure a bias—; questions about the political economy, the infrastructure and the geography of the field are taken up there.

More fundamentals (if you want to shore things up)

Blogs that are pure gold

  • Jay Alammar — His “The Illustrated Transformer” and “The Illustrated GPT-2” posts are the visual explanation of LLMs.
  • Lilian Weng (Lil’Log) — Technical, exhaustive posts on agents, diffusion, RLHF and more. Among the best writing in the field.
  • Distill — Interactive articles of rare beauty. It stopped publishing in 2021, but the archive is still a goldmine.
  • colah.github.io — Few posts, all memorable (start with “Understanding LSTMs”).
  • Andrej Karpathy (blog) — Don’t miss “A Recipe for Training Neural Networks”: practical advice distilled from years of craft.
  • Chip Huyen — One of the sharpest voices on ML systems; her blog and Stanford notes are public.

YouTube for papers and news

Keeping a finger on the pulse

Communities

Extending the itineraries

If you took Route A (understanding without programming): Nielsen’s Neural Networks and Deep Learning → Jay Alammar’s The Illustrated Transformer → fairmlbook → Two Minute Papers or Import AI to keep up.

If you took Route B (building things): The Missing Semester → An Introduction to Statistical Learning → MLOps Zoomcamp or Made With ML → the OpenAI Cookbook and the Anthropic courses for your day-to-day with LLMs.

If you took Route C (solid foundations): Probabilistic Machine Learning or Understanding Deep Learning → Sutton and Barto with Silver’s course (if RL grabs you) → Lilian Weng → arXiv and Hugging Face Papers for reading research. Optional: BlueDot if AI safety interests you.

One piece of advice for starting to read papers

Don’t start with the bare PDF. First look for a post or video explaining it (the blogs and channels above help a great deal), read the abstract, figures and conclusions, and only then get into the body. If you want a formal method, S. Keshav’s classic How to Read a Paper proposes the “three passes” and takes fifteen minutes to read.

One thing remains to be said about all this material, and it is what Route D was pointing at. The courses, books and blogs in these two entries came almost entirely out of a handful of universities and companies in the global North. That does not invalidate them —again: it is the best that is freely available— but it does decide in advance which questions count as interesting. The next entry is the other half of the itinerary.

Note: links may change over time; if one doesn’t open, search for the name of the resource.

Continue with Thinking AI from the Global South
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