附註:Includes bibliographical references and index.
Preface -- Chapter 1. An Introduction to reinforcement learning and its application in various domains -- Chapter 2. Transfer learning for computer vision: from theory to implementation -- Chapter 3. The importance of interpretability in AI systems in its implications for deep learning: ensuring transparency in intelligent systems -- Chapter 4. Generative AI from theory to model: unleashing the creative power of artificial intelligence -- Chapter 5. Robust adversarialdeep reinforcement learning -- Chapter 6. Human action recognition based on YOLOv7 -- Chapter 7. Facial emotion recognition using ensemble learning -- Chapter 8. Real-time billiard shot stability detection based on YOLOv8 -- Chapter 9. Vehicle detection and distance estimation using improved YOLOv7 model -- Chapter 10. Real-time pose recognition for billiard players using deep learning -- Chapter 11. Analysis model at the sentence level for phishing detection -- Compilation of references -- Related references -- About the contributors -- Index.
摘要:"The applications of rapidly advancing intelligent systems are so varied that many are still yet to be discovered. There is often a disconnect between experts in computer science, artificial intelligence, machine learning, robotics,and other specialties, which inhibits the potential for the expansion of this technology and its many benefits. A resource that encourages interdisciplinary collaboration is needed to bridge the gap between these respected leaders of theirown fields.Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems represents an exploration of the forefront of artificial intelligence, navigating the complexities of this field and its many applications. This guide expertly navigates through the intricate domains of deep learning and reinforcement learning, offering an in-depth journey through foundational principles, advanced methodologies, and cutting-edge algorithms shaping the trajectory of intelligent systems. The book covers an introduction to artificial intelligence and its subfields, foundational aspects of deep learning, a demystification of the architecture of neural networks, the mechanics of backpropagation, and the intricacies of critical elements such as activation and loss functions. The book serves as a valuable educational resource for professionals. Its structured approach makes it an ideal reference for students, researchers, and industry professionals."--
