Intro
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