Deep Learning : From Algorithmic Essence to Industrial Practice

Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligenceThe code for book may be accessed by visiting the companion website: https://www. elsevier.com/books-and-journals/book-companion/9780443439544

Deep Learning: From Algorithmic Essence to Industrial Practice serves as a comprehensive guide, bridging the gap between foundational theories and real-world implementation. The book delves into the mechanisms behind deep learning models, breaking down complex algorithms into digestible explanations. Engineering students, AI enthusiasts, and professionals alike will find this resource invaluable as it navigates the intricacies of training neural networks, optimizing performance, and effectively deploying them in various sectors. Beyond theoretical knowledge, the book emphasizes practical applications, showcasing how deep learning powers advancements in fields like healthcare, finance, and autonomous systems. It also discusses the challenges of scaling models, the ethical considerations surrounding AI, and the future trajectory of this transformative technology. With its blend of academic rigor and industrial insights, this book equips readers with the tools to innovate and lead in the ever-evolving world of artificial intelligence.