Unstructured point clouds serve as a sparse representation of the 3D world, playing pivotal roles in 3D perception, generation, autonomous driving, virtual/augmented reality, and robotics. Despite their significance, there lacks a comprehensive resource covering state-of-the-art approaches and engineering nuances in deep point cloud networks. This tutorial aims to fill this gap by offering an comprehensive exploration of the subject. It features lectures that progress from classical point cloud backbones to state-of-the-art point transformers, large-scale 3D representation learning (including pre-training technologies), efficient libraries for sparse systems, and diverse applications for deep point cloud networks. Participants will acquire systematic and practical knowledge on managing and extracting robust deep feature representations from point cloud data. They'll also learn to make informed decisions regarding model architectures and data structures when dealing with point cloud data. Armed with these skills, attendees will be well-equipped to comprehend and leverage these models in real-world applications across various fields, including autonomous driving, embodied AI, and other domains grappling with sparse data in low-dimensional Euclidean spaces.