Books recommended by AI researchers

Explore the broader reading of Demis Hassabis, Andrej Karpathy, Peter Norvig, Sebastian Raschka, Yoshua Bengio and Pieter Abbeel. These books cover many subjects; each has a documented recommendation.

Reviewed researcher membershipAll book subjectsRecommendation evidence linked
26Evidence records
6People
25Books

Start here

For readers exploring computing, mathematics, model interpretation, minds, and AI safety through researchers’ recommendations. These five editorial starting choices are not a technical curriculum or a reading order endorsed by the recommenders.

Editorial starting points from From The Greats. Level, prerequisites, learning goals and reading order are our guidance, not statements attributed to the recommenders. Inspect their actual recommendation evidence below.

Structure and Interpretation of Computer Programs

Editorial guidance

Explore how programs express ideas through a recommendation from Peter Norvig.

Level: Computing foundations

Prerequisites: Editorial suggestion: some programming experience and comfort with symbolic expressions.

Learning goal: Develop a deeper understanding of abstraction and the structure of computer programs.

Caveats: A classic computing text rather than a modern machine-learning or LLM implementation guide. Norvig’s recommendation does not endorse this editorial sequence.

Recommended by Peter Norvig — recommendation evidence.

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Mathematics for Machine Learning

Editorial guidance

Refresh mathematical foundations through Pieter Abbeel’s recommendation.

Level: Mathematical prerequisites

Prerequisites: Editorial suggestion: some prior algebra and calculus; use additional explanations and exercises when needed.

Learning goal: Connect linear algebra, calculus, probability, and optimization to machine-learning methods.

Caveats: The recommendation is an undated author-hosted endorsement of the 2020 work. It does not establish endorsement of every later online PDF revision.

Recommended by Pieter Abbeel — recommendation evidence.

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Interpretable Machine Learning

Editorial guidance

Explore how models and predictions can be explained through Sebastian Raschka’s review.

Level: Model interpretation

Prerequisites: Editorial suggestion: familiarity with supervised learning and common predictive models.

Learning goal: Compare approaches to interpreting models and their predictions.

Caveats: Raschka’s review dates to August 2020 and concerns the print and online versions available then. The online book continues to change; the review does not endorse every later revision.

Recommended by Sebastian Raschka — recommendation evidence.

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Godel, Escher, Bach

Editorial guidance

Explore formal systems, minds, and computation through a recommendation from Demis Hassabis.

Level: Conceptual exploration

Prerequisites: Editorial suggestion: curiosity about logic and computation; no model-training setup is needed.

Learning goal: Explore connections between formal systems, self-reference, and ideas about intelligence.

Caveats: A cross-disciplinary conceptual book rather than a machine-learning textbook. Its place here is an editorial starting choice, not evidence of a technical learning sequence.

Recommended by Demis Hassabis — recommendation evidence.

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Human Compatible

Editorial guidance

Explore arguments about beneficial AI through Yoshua Bengio’s recommendation.

Level: AI safety context

Prerequisites: Editorial suggestion: no programming prerequisite; familiarity with basic AI ideas may help.

Learning goal: Examine Stuart Russell’s arguments about AI objectives, human preferences, and safety.

Caveats: Bengio’s recommendation is an undated publisher-hosted endorsement. This is conceptual AI safety reading, not a coding tutorial or a complete account of current AI research.

Recommended by Yoshua Bengio — recommendation evidence.

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Featured people

Explore a selection of people with documented recommendations in this collection.

Portrait unavailable for Andrej Karpathy

Andrej Karpathy

AI researcher and software engineering leader

8 recommendation records across 8 books

Featured books

Start with these books, then open their profiles to see who recommended them and why.

Featured recommendation evidence

A preview of documented recommendations. Mention-only and citation-only records are separate context; documented citations classified as recommendations are included.

Cover unavailable for Deep Learning

Deep Learning

Documented citation: Suggested as more advanced technical reading.

The concluding learning-resources passage directs advanced readers to the book then being written. This 2015 guidance predates the final 2016 edition; it does not claim he endorsed a later revision or every book claim.

Documented citation · Recommended
Evidence details What a Deep Neural Network thinks about your #selfie

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

Andrej Karpathy · 2015-10-25 · accessed 2026-10-02

article · Original written source

The concluding learning-resources passage directs advanced readers to the book then being written. This 2015 guidance predates the final 2016 edition; it does not claim he endorsed a later revision or every book claim.

Open source
Portrait unavailable for Andrej KarpathyAndrej Karpathy
Cover unavailable for Deep Learning

Deep Learning

Documented citation: Explicitly recommends the upcoming Deep Learning manuscript after ML/statistical foundations.

Existing catalog work. Recommendation predates 2016 publication; do not assert later-edition endorsement.

Documented citation · Recommended
Evidence details How would your curriculum for a machine learning beginner look like?

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

Sebastian Raschka · undated · accessed 2026-10-05

article · Historical personal study-plan recommendations

Explicitly recommends the upcoming Deep Learning manuscript after ML/statistical foundations.

Open source
Portrait unavailable for Sebastian RaschkaSebastian Raschka
Cover of Godel, Escher, Bach

Godel, Escher, Bach

Why this author is included

Douglas R. Hofstadter · Pulitzer-winning cognitive scientist

Tying together Godel's incompleteness theory with mathematics, with Escher's drawings, and Bach's fugues. And showing they're all related in some way.

Hassabis described the book while discussing works that influenced how he thinks about minds and intelligence.

Exact quote verified · Recommended
Evidence details Demis Hassabis: The interview

We captured the recommendation language from the source.

YouTube · timestamp 22:56 · accessed 2026-06-17

video · YouTube interview clip

Timestamped source link discovered from a public recommendation index and retained as the original video source URL.

Open source
Demis Hassabis at the Royal SocietyDemis Hassabis
AmazonAudibleBookshop.orgAffiliate disclosure: We may earn a commission when you buy through some links. Recommendations are included because of the source, not because of the retailer. Details
Cover of Surely You're Joking, Mr. Feynman!

Surely You're Joking, Mr. Feynman!

Why this author is included

Richard Feynman · Nobel physicist

Ralph Leighton · Co-author

Documented citation: I really recommend to any students watching this to read those books.

The article reports Hassabis naming Richard Feynman's memoir as influential during his student years and recommending Feynman's books to students in a 2024 Nobel Prize interview context.

Documented citation · Recommended
Evidence details When Google AI CEO Demis Hassabis shared the book that influenced him most as a student and said: I really recommend to every student to read these books

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

The Times of India · 2026-03-18 · accessed 2026-06-20

article · Reported Nobel interview recommendation

The article reports that Hassabis said Feynman's memoir influenced him as a student and quoted him recommending those books to students during a Nobel Prize interview in Stockholm.

Open source
Demis Hassabis at the Royal SocietyDemis Hassabis
AmazonAudibleBookshop.orgAffiliate disclosure: We may earn a commission when you buy through some links. Recommendations are included because of the source, not because of the retailer. Details
Cover unavailable for Contact

Contact

Documented citation: In his own ranked science-fiction reviews, Karpathy says he liked the book substantially more than its movie adaptation.

Personal science-fiction reading recommendation; not a technical AI study assignment.

Documented citation · Recommended
Evidence details Books

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

Andrej Karpathy · undated; accessed 2026-10-05 · accessed 2026-10-05

article · Personal ranked science-fiction reviews

In his own ranked science-fiction reviews, Karpathy says he liked the book substantially more than its movie adaptation.

Open source
Portrait unavailable for Andrej KarpathyAndrej Karpathy
AudibleRetailer link
Cover unavailable for Exhalation

Exhalation

Documented citation: In his own ranked science-fiction reviews, Karpathy explicitly identifies the collection as required reading.

Personal science-fiction reading recommendation; not a technical AI study assignment.

Documented citation · Recommended
Evidence details Books

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

Andrej Karpathy · undated; accessed 2026-10-05 · accessed 2026-10-05

article · Personal ranked science-fiction reviews

In his own ranked science-fiction reviews, Karpathy explicitly identifies the collection as required reading.

Open source
Portrait unavailable for Andrej KarpathyAndrej Karpathy
AudibleRetailer link
Cover unavailable for Neural Networks and Deep Learning

Neural Networks and Deep Learning

Documented citation: Suggested as a starting resource for beginners learning neural networks.

In the conclusion of his 2015 article, Karpathy points beginners to Michael Nielsen’s tutorials. This is historical guidance about that work, not an endorsement of every later revision or a modern software setup.

Documented citation · Recommended
Evidence details What a Deep Neural Network thinks about your #selfie

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

Andrej Karpathy · 2015-10-25 · accessed 2026-10-05

article · Original written source

The conclusion directs beginners to Michael Nielsen’s tutorials and links neuralnetworksanddeeplearning.com. Nielsen’s own companion repository identifies the linked work as his book Neural Networks and Deep Learning. Recommendation wording here is a paraphrase, not a direct quote.

Open source
Portrait unavailable for Andrej KarpathyAndrej Karpathy
Cover unavailable for Nexus

Nexus

Documented citation: In his own ranked science-fiction reviews, Karpathy describes its world-building as highly enjoyable.

Personal science-fiction reading recommendation; not a technical AI study assignment.

Documented citation · Recommended
Evidence details Books

This source documents the book relationship, but the displayed text is a catalog summary rather than a verbatim quote.

Andrej Karpathy · undated; accessed 2026-10-05 · accessed 2026-10-05

article · Personal ranked science-fiction reviews

In his own ranked science-fiction reviews, Karpathy describes its world-building as highly enjoyable.

Open source
Portrait unavailable for Andrej KarpathyAndrej Karpathy
AudibleRetailer link

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