A Primer on Generative Adversarial Networks

Description

This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics. The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. Preface Acknowledgments Contents Chapter 1: Overview of GAN Structure 1.1 Introduction 1.2 Generative Models 1.3 GANS Overview of GAN Structure The Discriminator The Generator Training the GAN Loss Function GANs Weaknesses References Chapter 2: Your First GAN 2.1 Preparing the Environment Hardware Requirements Software Requirements Importing Required Modules and Libraries Prepare and Preprocess the Dataset 2.2 Implementing the Generator 2.3 Implementing the Discriminator 2.4 Training Stage Model Construction Loss Function Plot Generated Data Samples Training GAN Common Challenges While Implementing GANs References Chapter 3: Real-World Applications 3.1 Human Faces Generation Data Collection and Preparation Model Design The Generator Model The Discriminator Model Training Evaluation and Refinement Deployment 3.2 Deep Fake Data Collection and Preparation Model Design Training 3.3 Image-to-Image Translation Data Collection and Preparation Model Design The Generator Model The Discriminator Model The Adversarial Network Training 3.4 Text to Image Module Requirements Dataset Data Preprocessing Model Design Generator Model Discriminator Model Adversarial Model Training Stage Evaluation and Refinement 3.5 CycleGAN Dataset Model Design Generator Model Discriminator Model Training Stage 3.6 Enhancing Image Resolution Dataset Model Design Generator Model Discriminator Model Training Stage 3.7 Semantic Image Inpainting Dataset Model Design Generator Model Discriminator Model Training 3.8 Text to Speech Dataset Data Preprocessing Model Design Generator Model Discriminator Model Training References Chapter 4: Conclusion