Most of my blog posts have prerequisites stated at the beginning of the post. If you are unfamiliar with one or another, please refer to the following table for recommended readings.
I only list references that I found helpful, so it is by no means exhaustive nor objective, and you might find better references. If you do, please let me know! The table will be updated incrementally.
|Topic||Light introduction||Deep dive|
|AI alignment problem||See "AI Safety"||See "AI Safety"|
|AI safety||Bostrom |
Eliezer Yudkowski's presentation
|Leike et al.
Everitt et al.
|game theory||Leyton-Brown and Shoham *|
|Gaussian processes||David MacKay's lecture (slides and alternative upload here)||Rasmussen and Williams *|
|neural networks||3blue1brown’s series of videos|
|reinforcement learning||David Silver's lecture series||Sutton and Barto|
|LSTMs||Colah's blog post|
|Neural processes||Garnelo et al.|
|classical mechanics||Landau and Lifshitz|
|relativity||Einstein's wonderful nearly-equation-free book||Wald
Misner et al.
Penrose and Rindler
Sources with a “*” are those which I have only read partially.