This book provides a comprehensive yet accessible overview of foundational neural network architectures driving recent advancements in artificial intelligence. Beginning with an introduction to deep neural networks, the author outlines key principles and design components underpinning complex intelligent systems. Convolutional and recurrent neural networks are also explored in detail, elucidating their specialized capabilities in processing image, text, speech, and time-series data. Expanding beyond supervised learning, the book dives into reinforcement learning for developing intelligent agents that can operate independently in dynamic environments. Groundbreaking algorithms behind large language models like ChatGPT and GPT are analyzed as well, revealing inner workings of systems capable of sophisticated text generation. While avoiding advanced mathematics, the book focuses on high-level conceptual understanding and real-world applications throughout. With informative diagrams and clear prose, readers gain insight into the fundamentals behind artificial intelligence innovations transforming society today. Ideal for beginners and moderately experienced practitioners alike.