Depth Over Rules: Why Layers Outperform Logic

Intelligence scales through representation, not instruction. Depth Over Rules is a deep learning book devoted to explaining why layered neural networks outperform rule-based systems in complex, real-world tasks.
The writing traces the shift from hand-crafted logic to learned representations. Instead of encoding behavior explicitly, deep learning systems discover structure through depth—stacking layers that progressively capture meaning from raw input.
Rather than focusing only on equations, the book builds intuition. Readers learn how layers transform information, why hierarchy matters, and how depth enables abstraction beyond what rules can express. Each chapter connects theoretical ideas to practical breakthroughs in language, vision, and perception.
The tone is explanatory and insight-driven, suitable for learners and practitioners alike. Language remains clear and structured, helping readers understand not just how deep learning works, but why it succeeds where rules fail.
Depth Over Rules moves through representation learning, hierarchical features, optimization, and generalization—revealing depth as the foundation of modern intelligence.
Key themes explored include:
• Representation vs rules
• Power of layered learning
• Abstraction and hierarchy
• Limits of symbolic logic
• Why deep models scale
Depth Over Rules is for readers seeking understanding—offering a clear explanation of why modern AI learns through depth, not instruction.