This book dives into the fundamentals of deep learning, engineering practices, and its practical deployment and usage in various industries. It introduces the theory behind deep learning along with detailed explanations of classic Convolutional Neural Networks, Recurrent Neural Networks, Transformer networks using self-attention mechanisms, and their variations through code demonstrations. The book explores how these models are applied in fields like image classification, object detection, semantic segmentation, fraud detection, speech recognition, and more. Moreover, it delves into the cutting-edge progress of deep reinforcement learning and Generative Adversarial Networks. In terms of system engineering and industry applications, the book discusses how to utilize distributed systems for training and deploying models to handle vast amounts of data. It provides a systematic guide on constructing deep learning inference systems and breaks down the engineering considerations needed for distributed deep learning inference systems using code examples.