Accelerating Deep Neural Networks

Sato, Ryoma

Cambridge University Press（2026/04発売）

• 製本 Hardcover:ハードカバー版／ページ数 311 p.
• 言語 ENG
• 商品コード 9781009687089
• DDC分類 006.32

Full Description

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

Contents

1. Introduction; 2. Overview of acceleration methods; 3. Quantization and low precision; 4. Pruning; 5. Distillation; 6. Low-rank approximation; 7. Fast architectures; 8. Tools for tuning; 9. Efficient training; Conclusion; References; Index.