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Core readings

by @stats385 · 7 nodes · curated

Shelf starters: textbooks, the Nature overview, and statistical perspectives every student should know.

September 24, 2026
Emergence of Simple-cell Receptive Field Properties courses.cs.washington.edu courses.cs.washington.edu
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Sparse coding / V1 inspiration paper (Olshausen & Field) — Lecture 1 background reading.

ACM Turing Award lectures (Hinton, LeCun, …) amturing.acm.org amturing.acm.org
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Video lectures by Turing awardees — pair with the Nature article for a narrative arc.

History and Development of Neural Networks sciencedirect.com sciencedirect.com
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Historical survey of neural nets — useful context before modern architecture papers.

An Overview from the Statistical Perspective arxiv.org arxiv.org
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Course-listed statistical overview of deep learning — bridges ML practice and stats language.

Dive into Deep Learning (d2l.ai) d2l.ai d2l.ai
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Interactive tutorials with code — excellent companion when you need implementations alongside theory.

Deep Learning — LeCun, Bengio & Hinton (Nature 2015) nature.com nature.com
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The three Turing awardees’ Nature overview — best short intro to why deep learning worked.

Deep Learning Book (Goodfellow, Bengio, Courville) deeplearningbook.org deeplearningbook.org
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Canonical textbook. Use as background reference; skim architecture & optimization chapters early.