Core readings
Shelf starters: textbooks, the Nature overview, and statistical perspectives every student should know.
Sparse coding / V1 inspiration paper (Olshausen & Field) — Lecture 1 background reading.
Video lectures by Turing awardees — pair with the Nature article for a narrative arc.
Historical survey of neural nets — useful context before modern architecture papers.
Course-listed statistical overview of deep learning — bridges ML practice and stats language.
Interactive tutorials with code — excellent companion when you need implementations alongside theory.
The three Turing awardees’ Nature overview — best short intro to why deep learning worked.
Canonical textbook. Use as background reference; skim architecture & optimization chapters early.
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