Generative Adversarial Networks with Industrial Use Cases: Learning how to build GAN applications for Retail, Healthcare, Telecom, Media, Education, andHRTech eBook

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. Table of Contents 1. Basics of GAN 2. Introduction 3. Problem with GAN 4. Famous Types Of GANs About the Author Navin K Manaswi has been developing AI solutions/products for HRTech, Retail, ITSM, Healthcare, Telecom, Insurance, Digital Marketing, and Supply Chain while working for Consulting companies in Malaysia, Singapore, and Dubai . He is a serial entrepreneur in Artificial Intelligence and Augmented Reality Space. He has been building solutions for video intelligence, document intelligence, and human-like chatbots. He is Guest Faculty at IIT Kharagpur for AI Course and an author of the famous book on deep learning. He is officially a Google Developer Expert in machine learning. He has been organizing and mentoring AI hackathons and boot camps at Google events and college events. His startup WoWExp has been building awesome products in AI and AR space. Your Blog links: www.navinmanaswi.com Your LinkedIn Profile: https://www.linkedin.com/in/navin-manaswi-1a708b8/

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