Sutskever's list / Rich Heimann.

Format:

Video

Author/Creator:

Heimann, Rich, author.

Contributor:

Manning Publications (Firm), publisher.

Language:

English

Subjects (All):

Artificial intelligence.

Deep learning (Machine learning).

Physical Description:

1 online resource (1 video file (11 hr., 12 min.)) : sound, color.

Edition:

Video Edition.

[First edition].

Place of Publication:

[Shelter Island, New York] : Manning Publications, 2026.

Summary:

"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. About the Technology About the Book 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. Quotes A clear, insightful overview of foundational papers of AI, connecting theory to practical modern applications. - Anirban Majumder, Amazon If you've ever wanted to understand the intellectual DNA of modern AI, this is the book to read. - Oliver Roskill, The Hacking Games Perfectly summarizes the highlights of the Sutskever's list while providing the historical and technical context. - Nicolas Bievre, Meta A working reconstruction of the field's foundational ideas, the kind of understanding that only implementation produces. - Amarda Shehu, George Mason University Transforms a decade of research into a powerful toolkit, codifying a philosophy of minimum innovation for maximum leverage. - I-Sah Hsieh, State of North Carolina Gets across and connects most of the foundational ideas leading up to modern AI. A remarkable achievement. - Peter Grünwald, Leiden University If you want to learn where today's methods came from, what motivated them, and how a relatively small set of ideas helped define the trajectory of the field, this is an excellent place to begin. - From the Foreword by Sebastian Raschka, author of Build a Large Language Model (From Scratch) Provides a clear, concise and well-contextualized overview of the most important breakthroughs in AI research since the 1990s. It's a highly enjoyable read, with the author weaving the personalities of Sutskever and his peers into the broader narrative. - Nico Smuts, Data Science Executive The author refuses to treat Sutskever's List as what it superficially appears to be--just a reading list, a paper after a paper that you have to treat in isolation. Instead, the author reconstructs it as a carefully argued intellectual journey. - Francisco Perez-Sorrosal, independent AI Engineer and Advisor The book is instrumental in forming a mental picture of the main ideas that shaped today's achievements in AI and their interplay. - Stefano Lottini, Software Engineer The author has an exquisitely deep, detailed, and nuanced knowledge of the subject area. - Wendy Langer, Good Stuff! Tutoring Sutskever's List is all you need if you want to learn the history, and breadth and depth of deep learning by understanding foundational research papers! Author's intuitions at the end of each chapter have so much wisdom and insights--you will love reading them. - Bhavin Thaker, New Relic I like how the author made this book into a story. I enjoyed it thoroughly. - Jay Kelkar, Kelkar Systems Chapter 8 gave me a clear conceptualization of complexity and grokking research I didn't have before. - Leo Huovinen, Tampere University.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1613816716

Publisher Number:

9781633434790VE