Generative Adversarial Networks with Industrial Use Cases: Learning how to build GAN applications for Retail

Best Book on GAN

Key Features
● Understanding the deep learning landscape and GAN’s relevance
● Learning basics of GAN
● Learning how to build GAN from scratch
● Understanding mathematics and limitations of GAN
● Understanding GAN applications for Retail, Healthcare, Telecom, Media and EduTech
● Understanding the important GAN papers such as pix2pixGAN, styleGAN, cycleGAN, DCGAN
● Learning how to build GAN code for industrial applications
● Understanding the difference between varieties of GAN

Description
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, cycleGAN, and many other GAN. Machine Learning and Deep Learning Researchers will learn GAN in the shortest possible time with the help of this book.

What will you learn
● Machine Learning Researchers would be comfortable in building advanced deep learning codes for Industrial applications
● Data Scientists would start solving very complex problems in deep learning
● Students would be ready to join an industry with these skills
● Average data engineers and scientists would be able to develop complex GAN codes to solve the toughest problems in computer vision

Who this book is for
This book is perfect for machine learning engineers, data scientists, data engineers, deep learning professionals and computer vision researchers. This book is also very useful for medical imaging professionals, autonomous vehicles professionals, retail fashion professionals, media & entertainment professional, edutech and HRtech professionals. Professors and Students working in machine learning, deep learning, computer vision and industrial applications would find this book extremely useful.