Mathematics of Deep Learning 1st Edition

Grab the rigorous foundation behind today’s breakthroughs with Mathematics of Deep Learning, 1st Edition by Leonid Berlyand and Pierre-Emmanuel Jabin. This authoritative volume speaks directly to researchers, graduate students, and practitioners who want a clear, mathematical entry into neural networks and learning theory.

Dive into a structured, accessible treatment that connects approximation theory, optimization, and partial differential equations to the concrete mechanics of deep learning. The authors bridge intuition and proof: you’ll find precise formulations of convergence, stability, and generalization alongside practical insights about training dynamics and network architectures. Carefully chosen theorems, well-motivated examples, and thoughtful exposition make advanced concepts approachable without sacrificing rigor.

Whether you’re preparing for research, teaching a graduate course, or strengthening the theoretical backbone of applied projects, this book equips you with tools to analyze architectures and algorithms confidently. Emphasizing global relevance, its methods suit work in universities, industry labs, and startups across North America, Europe, Asia and beyond.

Rich in mathematical clarity and real-world perspective, this 1st Edition is both a reference and a learning pathway—ideal for building lasting expertise in deep learning’s core principles. Add Mathematics of Deep Learning to your library today to deepen your theoretical understanding and elevate your practical work in machine learning. Order your copy now and transform how you think about neural networks.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.