This book aims at simplifying Gan for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Universities and online course instructors will find this book very interesting for teaching advanced deep learning, specially generative adversarial networks(gan). industry professionals, coders, and data scientists can learn Gan from scratch. They can learn how to build Gan codes for industrial applications for healthcare, retail, hrtech, edutech, telecom, media, and entertainment. Mathematics of Gan is discussed and illustrated. Kl divergence and other parts of Gan are illustrated and discussed mathematically. This book teaches how to build codes for pix2pix Gan, dcgan, cgan, stylegan, cycled, and many other Gan. Machine learning and deep learning researchers will learn Gan in the shortest possible time with the help of this book.