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 intelligence
The code for book may be accessed by visiting the companion website: https://www.
elsevier.com/books-and-journals/book-companion/9780443439544

• Provides in-depth explanations and practical code examples for the latest deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers
• Examines theoretical concepts and the engineering practices required for deploying deep learning models in real-world scenarios
• Covers the use of distributed systems for training and deploying models
• Includes detailed case studies and applications of deep learning models in various domains including image classification, object detection, and semantic segmentation