Methods and Techniques in Deep Learning

This book offers a detailed exploration of deep learning techniques for mmWave radar applications, highlighting innovative solutions and practical implementations.

This book introduces multiple state-of-the-art deep learning architectures specifically designed for mmWave radar applications across various advanced fields. Methods and Techniques in Deep Learning provides a comprehensive overview of how artificial intelligence (AI) can enhance mmWave radar technology, focusing on practical applications and deep learning techniques. The text covers the fundamentals of deep learning and explores cutting-edge methods such as deep metric learning and reinforcement learning (RL) algorithms. It also highlights the significance of domain adaptation (DA) in improving machine learning (ML) performance.

Written by a team of authors with extensive expertise, including over 70 filed patents and 100 published papers, the book illustrates how deep learning is transforming industrial, consumer, and automotive applications of mmWave radars. In-depth chapters delve into topics such as multi-modal deep learning, Bayesian deep learning, and geometric deep learning for processing point clouds. Readers will find discussions on various advanced applications, the challenges faced, and the innovative deep learning architectures that address these issues.

Furthermore, Methods and Techniques in Deep Learning emphasizes the role of deep parametric learning in optimizing data flow and reducing trainable parameters. It also showcases several human-machine interface (HMI) applications, including gesture recognition and in-cabin automotive sensing. This book serves as an invaluable resource for industry professionals, researchers, and graduate students engaged in systems engineering, signal processing, sensors, data science, and AI.