Recommendations you can check.
Who uses, recommends or left fast.ai, in their own words: each a verbatim quote with a link to where it was said.
2 people
https://course.fast.ai/ is a free course on deep learning with PyTorch. It helped me get started with deep learning, though I didn’t get that far.
Her undated Bookshelf page listing books and resources that helped her (observed 2026-09-04; archived since at least 2023-01-17).
2023-01-17·jvns.ca ↗
fast.ai is a good intro, but a fair bit more effort than is necessary.
From Neel Nanda's (co-authored with Jess Smith) 'A Barebones Guide to Mechanistic Interpretability Prerequisites' on his own site.
2022-10-24·neelnanda.io ↗
Every entry is a verbatim quote and a link to the public post it came from. Nothing is paraphrased. If it is not on record somewhere public, it is not here.
The verdict is what they actually said: loved and liked are explicit; recommended means they told others to get it; uses means they only say they use it; read is a book on their public shelf with no verdict; mixed and disliked are kept too.
A dashed affiliate link, sponsored or their own label means the post disclosed a relationship. It is shown, never hidden.
The shading behind a date shows how dated a recommendation is for its kind of thing.
The shading behind a date shows how dated a recommendation is, for its kind of thing. Fully green is recent. It drains a quarter at a time as the post gets older, and is empty once it reads as dated.
What counts as old depends on the thing. A laptop praised four years ago has been replaced twice; a book loved twenty years ago is probably still loved. The shading is gone after about:
Stripes mean the post carries no date and the date shown is an estimate. No date at all, no shading.