Preface

Nowadays, deep learning and reinforcement learning have become some of the hottest research directions in computer science. They can solve complex problems such as natural language processing, computer vision, medical image analysis, and more by training powerful neural networks. The deep learning algorithm has become one of the most important and promising technologies in the field of artificial intelligence. In addition, reinforcement learning can autonomously learn and adjust to maximize rewards, which is expected to solve complex sequential decision tasks, such as intelligent games and robot control.

In recent years, the rapid development and widespread application of deep learning and reinforcement learning have created enormous commercial and social value. This book introduces the latest advances in the fields of deep learning and reinforcement learning, covering a variety of key areas like natural language processing, medicine analysis, and Internet of Things (IoT) device recognition.

This book consists of two sections: "Theory and Algorithms of Deep Learning and Reinforcement Learning" and "Applications of Deep Learning and Reinforcement Learning." Sections I and II contain two and four chapters, respectively. Section I discusses new network structures and algorithms for deep learning and reinforcement learning. Section II explores new deep learning and reinforcement learning solutions to the challenges faced by the fields of natural language processing, medicine analysis, and IoT device recognition.

I would like to express my sincerest gratitude to the editors, authors, and reviewers who have contributed to this book.

Thank you!

**Jucheng Yang, Yarui Chen, Tingting Zhao, Yuan Wang and Xuran Pan** College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China

### Section 1
