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. It 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, it covers the applications of these models in areas such as image classification, object detection, semantic segmentation, etc. The book also considers the advancements in deep reinforcement learning and generative adversarial networks.

• 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. It covers the use of distributed systems for training and deploying models
• Includes detailed case studies and applications of deep learning models in various domains such as image classification, object detection, semantic segmentation, etc. These practical examples help readers apply theoretical knowledge to solve real-world problems