Accelerating Deep Neural Networks

Deep learning models are powerful, but 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

Bridges the gap between research and practice by synthesizing information on acceleration techniques into a systematic and practical resource Allows readers to go beyond theory and immediately apply the techniques to their own models with ready-to-use implementation code Shows the trade-offs between different methods through numerical comparisons of speed, accuracy, and memory usage, helping readers more easily choose the best approach for their specific task