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  • Sutskever's List: Foundational ideas of modern AI
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Sutskever's List: Foundational ideas of modern AI

by Richard Heimann (Author) Format: Paperback
4.7 out of 5 stars (14)
#1 New Release in Computer Vision & Pattern Recognition

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"A perspective the field has needed. Sutskever’s List delivers it with care and historical accuracy.”
—Yanping Huang, Google

Sutskever’s List is a guided intellectual journey through the ideas that made modern AI suddenly possible. Each chapter is anchored in specific papers, books, or other sources from Sutskever’s list. The papers themselves are not the focus. Instead, the author uses them as entry points into the larger breakthroughs, arguments, interconnections, and shifts in thinking that transformed the field.

It begins with AlexNet, where data, GPUs, and training craft made neural networks impossible to dismiss, then moves to ResNet, where depth becomes a superpower rather than a liability. From there, the story accelerates through sequence models, speech systems, attention, Transformers, and hyperscale, showing how AI escaped older bottlenecks and became built to grow.

Later chapters ask whether these systems can reason, why simplicity can emerge from complexity, and what intelligence and safety mean once AI capabilities begin to feel uncanny. Reviewers praise Heimann’s “exquisitely deep, detailed, and nuanced knowledge” and the “massive amount of gold material” gathered here. Yet the book remains remarkably easy to read, turning difficult papers into a “guided initiation those papers were never designed to provide on their own.”

As you go, you’ll understand how abstract lab results have translated into real-world consequences, including shifting architectures and internal organizational politics. With lucid explanations of the core technologies of AI as defined in Sutskever’s collection of seminal papers, Heimann explores common engineering choices, evaluating the strengths and limits of deep learning without falling for hype or cynicism. Complex concepts are clarified through relevant examples, vivid anecdotes, and practical engineering insights.

Each of the core papers examined in Sutskever’s List represents a crucial steppingstone in the evolution of the AI. You’ll love how Richard Heimann combines a deep technical background with a journalistic eye, never losing sight of practical considerations and providing a stepping off point to understand where the technology goes next.

Sutskever’s List features nine chapters, an epilogue, and a practical appendix, smoothly blending technical instruction with cultural and historical context. The result is a logically flowing book that remains highly accessible, navigable, and technically deep without requiring the reader to have a specialist’s background.

What's inside

• Decoding landmark AI papers from AlexNet to transformers
• Understanding scaling laws, reasoning models, and AI safety
• Engineering patterns that scale from research to real-world systems

About the reader

For anyone interested in modern AI and deep learning. No specialist knowledge required.

About the author

Richard Heimann has honed his deep AI and machine learning expertise across technical and strategic roles in industry, academia, and government. He excels at translating complex ideas into clear, engaging insights for audiences from practitioners to policymakers.

Table of Contents

1 What did Ilya see?
2 The AlexNet moment
3 ResNet revolution
4 Deep learning accelerates
5 Attention is all you need
6 The birth of hyperscale
7 The pivot to reasoning
8 Simplicity, hidden in complexity
9 Safe superintelligence
Epilogue: The missing pieces
Appendix: Design patterns for engineers
Read more
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  1. ISBN-10
    1633434796
  2. ISBN-13
    978-1633434790
  3. Publication date
    July 28, 2026
  4. Language
    English
  5. Dimensions
    7.38 x 0.84 x 9.25 inches
  6. Print length
    336 pages
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From the Publisher

Sutskever's List header

Sutskever's List quote 1

"A clear, insightful overview of foundational papers in AI, connecting theory to practical modern applications."

Anirban Majumder, Amazon

Sutskever's List quote 2

"If you’ve ever wanted to understand the intellectual DNA of modern AI, this is the book to read."

Oliver Roskill, The Hacking Games

Sutskever's List quote 3

"Perfectly summarizes the highlights of Sutskever’s list while providing the historical and technical context."

Nicolas Bievre, Meta

Sutskever's List about the book

why this book?

Sutskever’s List helps you understand modern AI through the landmark ideas and papers that shaped it, rather than treating today’s models as a black box.

It gives engineers, leaders, and technically curious readers a clearer mental model for deep learning, attention, reasoning, and AI safety, so they can make better sense of where the field is going.

The benefit is a practical perspective: you come away better able to evaluate AI claims, connect current tools to their research roots, and think more strategically about what comes next.

about manning

about Manning

Manning helps developers and tech professionals stay ahead in a fast-moving industry with expert-led books, videos, and projects. Learning never stops, but it’s hard to keep up, so we focus on content that’s practical, clear, and trusted. As an independent publisher, we adapt quickly, from pioneering early-access books to offering DRM-free eBooks. Our series, like "In Action" and "In a Month of Lunches", reflect a commitment to making complex topics accessible.

Deep Learning with Python, Third Edition
AI Agents in Action
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Causal AI
Customer Reviews
4.2 out of 5 stars 42
4.0 out of 5 stars 51
4.5 out of 5 stars 613
4.5 out of 5 stars 36
4.8 out of 5 stars 7
4.4 out of 5 stars 14
Level of proficiency Intermediate Intermediate Intermediate Intermediate Intermediate Advanced
About the reader For readers with intermediate Python skills. For intermediate Python programmers. Readers need intermediate Python skills and some knowledge of machine learning. For data scientists and ML engineers. For data scientists and data analysts. For data scientists and machine learning engineers.
Special features Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant.
Pages 648 344 368 456 232 520

Editorial Reviews

About the Author

Richard Heimann has honed his deep AI and machine learning expertise across technical and strategic roles in industry, academia, and government. His work spans neural networks, generative AI, and transformative applications, and he is versed in decades of philosophical debate. An author of several books, he excels at translating complex ideas into clear, engaging insights for audiences from practitioners to policymakers.

Product details

  • Publisher ‏ : ‎ Manning Publications
  • Publication date ‏ : ‎ July 28, 2026
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 336 pages
  • ISBN-10 ‏ : ‎ 1633434796
  • ISBN-13 ‏ : ‎ 978-1633434790
  • Item Weight ‏ : ‎ 11.7 ounces
  • Dimensions ‏ : ‎ 7.38 x 0.84 x 9.25 inches
  • Best Sellers Rank: #59,124 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.7 out of 5 stars (14)

About the author

Follow authors to get new release updates, plus improved recommendations.
Richard Heimann

Richard Heimann

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Richard was the Chief Artificial Intelligence Officer at Silversky and, before that, at Cybraics. He previously supported DARPA's Network Defense program, which focused on research and development of unsupervised machine learning and behavioral analytics for the complex, ill-defined problem of intrusion detection and distributed computation. He also performed on DARPA’s Nexus 7 program, which won a Joint Meritorious Unit Award. Heimann is the former Chief Data Scientist and Technical Fellow at L-3, a former adjunct professor at the University of Maryland, where he taught computational statistics and statistical reasoning, and an instructor at George Mason University, where he taught computational social science. He is currently the State Director of AI for South Carolina.

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