Differentiable Programming for AI Engineers: Practical Workflows with PyTorch, JAX, and Julia

Differentiable programming is rapidly becoming a cornerstone of modern artificial intelligence, extending the power of gradient-based optimization far beyond neural networks into domains as diverse as physics, control systems, graphics, and large-scale simulation. While research papers and scattered tutorials have introduced fragments of the field, Differentiable Programming for AI Engineers is the first and only comprehensive guide designed to help practitioners and advanced students grasp its principles and apply them effectively using today’s leading frameworks.

This book unifies the concepts, tools, and methods of differentiable programming in a clear and practical fashion, balancing mathematical rigor with hands-on engineering workflows. Through carefully chosen explanations and code examples in PyTorch, JAX, and Julia, the book demonstrates how to move from theory to implementation without losing sight of precision or usability.

The book covers the foundations of automatic differentiation, differentiable optimization, and end-to-end trainable systems, while extending to cutting-edge applications such as differentiable physics engines, differentiable graphics and rendering, and optimization in complex industrial systems. Alongside these, it introduces advanced practices for workflow design, hybrid modeling, and the integration of differentiable components into real-world AI pipelines.

With an emphasis on clarity and practical value, this book shows how to construct, analyze, and deploy differentiable programs that can optimize themselves in dynamic environments. Each chapter pairs essential mathematical ideas with runnable code, making the material directly applicable for engineers and researchers alike.

Based on years of experience building AI applications, this volume will help readers understand the principles of differentiable programming, evaluate its role in modern AI systems, and design workflows that extend learning into new domains. It is essential reading for machine learning engineers, applied researchers, and anyone aiming to master the next generation of optimization-driven AI.