Accelerating Deep Neural Networks

This book for engineers, researchers, and students working with deep learning AI models explains how to make these models faster and more efficient using clear examples, simple theory, and hands-on code. It offers a practical, insightful guide for anyone looking to reduce costs and improve performance in real-world applications.

Deep learning models are powerful, but are often large, slow, and expensive to run. This book is a practical guide to accelerating and compressing neural networks using proven techniques such as quantization, pruning, distillation, and fast architectures. It explains how and why these methods work, fostering a comprehensive understanding. Written for engineers, researchers, and advanced students, the book combines clear theoretical insights with hands-on PyTorch implementations and numerical results. Readers will learn how to reduce inference time and memory usage, lower deployment costs, and select the right acceleration strategy for their task. Whether you're working with large language models, vision systems, or edge devices, this book gives you the tools and intuition needed to build faster, leaner AI systems, without sacrificing performance. It is perfect for anyone who wants to go beyond intuition and take a principled approach to optimizing AI systems