تسريع الشبكات العصبية العميقة

Deep learning models are powerful, but they are often bulky, slow, and expensive to run. This book provides a practical guide to accelerating and compressing neural networks using proven techniques such as quantization, pruning, distillation, and fast architectures. The book explains how and why these methods work, promoting a comprehensive understanding of them. Written for advanced engineers, researchers, and students, this book combines clear theoretical insights with practical applications using PyTorch and numerical results. Readers will learn how to reduce inference time and memory consumption, reduce deployment costs, and choose the appropriate acceleration strategy for their task. Whether you work with large language models, vision systems, or edge devices, this book provides you with the tools and intuition needed to build faster, more efficient AI systems, without sacrificing performance. It is ideal for anyone who wants to go beyond intuition and take a principles-based approach to improving AI systems. It bridges the gap between research and application by compiling information about accelerator techniques into a systematic and practical resource. This program enables readers to go beyond theory and apply the techniques directly to their own models using ready-to-use implementation code. This book shows the trade-offs between different approaches through numerical comparisons of speed, accuracy, and memory use, helping readers more easily choose the best approach for their specific task.