Deep learning has emerged as a transformative branch of artificial intelligence, powering breakthroughs in fields ranging from medical diagnosis and autonomous driving to natural language processing and personalized recommendations. This chapter lays the foundation for “Hands-on Deep Learning” by first defining artificial intelligence, machine learning, and deep learning, and by illustrating their hierarchical relationship. It introduces the mental model for constructing deep learning systems, emphasizing the interplay among four essential elements: a well-formulated problem statement, high-quality data, adequate computational resources, and the deep learning model itself. Core concepts—such as neural network structure, the distinction between shallow and deep networks, and the impact of model size—are presented alongside a high-level overview of the four building-block categories: optimization algorithms, bias–variance trade-off techniques, neural network architectures, and training/validation strategies. By framing deep learning model development as an iterative blend of scientific rigor and creative design, this chapter equips readers with the conceptual tools and terminology needed to build models from scratch and paves the way for the detailed explorations in subsequent chapters.