"Deep Learning and Reinforcement Learning" ed. by Jucheng Yang, et al.

This book examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.

Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition.

Contents
1. Utilized System Model Using Channel State Information Network with Gated Recurrent Units (CsiNet-GRUs)
2. Graph Neural Networks and Reinforcement Learning: A Survey
3. IoT Device Identification Using Device Fingerprint and Deep Learning
4. MultiRes Attention Deep Learning Approach for Abdominal Fat Compartment Segmentation and Quantification
5. Deep Learning for Natural Language Processing
6. Deep Learning in Medical Imaging


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