Alex Punnen is a software architect and technologist with over 20 years of experience in designing, building, and evolving large-scale software systems. His career spans multiple generations of computing—from early distributed systems and object-oriented middleware to modern cloud-native architectures, microservices, and containerized platforms.

A polyglot programmer with a strong systems mindset, Alex has worked across the full technology stack, including low-level protocols, distributed data platforms, and high-performance backend systems. He is driven by a deep curiosity for first principles—understanding not just how systems work, but why they work.

In recent years, this curiosity has led him into Artificial Intelligence and Deep Learning, with a particular focus on the mathematics that underpins neural networks. Rather than treating AI as a black box, Alex approaches it as a composition of linear algebra, calculus, probability, and optimization.

This book reflects that philosophy. The Maths of Deep Learning is written for engineers and learners who want to move beyond intuition and buzzwords, and instead build a clear, rigorous understanding of deep learning—one equation, one idea, and one insight at a time.