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Richard Heimann

Based on Ilya Sutskever’s famous “must-read” list of ~30 AI papers, this book walks you through the research that shaped today’s deep learning—from AlexNet to Transformers.

It’s not just summaries—Heimann adds context, explains the ideas in plain language, and connects them to how AI is built and scaled today. Perfect if you want to understand why modern AI works the way it does, not just how.

Why This Book Matters

• “If you really learn all of these, you’ll know 90% of what matters today.” That’s how OpenAI co-founder Ilya Sutskever described his list of approximately 30 seminal papers—covering the breakthroughs that underpin modern deep learning.

• Richard Heimann brings these key works to life with clear explanations, vivid historical context, and accessible interpretations—even for readers who aren’t experts in deep learning.

• You’ll get more than summaries. Heimann weaves these papers into a broader narrative—connecting technical advances to organizational decisions, AI safety debates, scaling strategies, and cultural shifts within AI.

Who It’s For

Anyone with a working familiarity with AI—whether you’ve played with neural networks, followed AI trends, or built projects—will find this book immensely valuable. It’s about understanding why modern AI works the way it does, not just how.

Key Highlights

• Learn how breakthroughs from AlexNet to Transformers shaped modern AI systems—from vision to language and beyond.

• Gain insight into engineering patterns and design choices that scale research breakthroughs into real-world AI platforms.

• Develop a practical lens for evaluating both the promise and limitations of deep learning—without succumbing to hype or sensationalism.

• Apply Sutskever’s mental models to anticipate future trends and emerging directions in AI

• Full details: https://www.manning.com/books/sutskevers-list

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