Discover how GANs revolutionize AI by pitting neural networks against each other to create lifelike images, text, and data. Dive into their architecture, training methods, and multifaceted applications across healthcare, media, and research, offering key insights for students, data scientists, and AI practitioners.
Discover how GANs revolutionize AI by pitting neural networks against each other to create lifelike images, text, and data. Dive into their architecture, training methods, and multifaceted applications across healthcare, media, and research, offering key insights for students, data scientists, and AI practitioners.
Adele Kuzmiakova is a machine learning engineer working at the intersection of machine learning, computer vision, and natural language processing. Adele attended Cornell University in New York, United States for her undergraduate studies. She studied engineering with a focus on applied math. Some of the deep learning problems Adele worked on include predicting air quality from public webcams, developing a real-time human movement tracking, using 3D computer vision to create 3D avatars from selfies in order to bring online clothes shopping closer to reality, and creating visual stories and photobooks from photos on mobile devices. She is also passionate about exchanging ideas and inspiring other people and acted as a workshop organizer at Women in Data Science conference in Geneva, Switzerland in 2022 and 2023.
Inhaltsangabe
Chapter 1 Introduction to Generative Adversarial Networks (GANs) Chapter 2 Architecture of Generative Adversarial Networks Chapter 3 Types of Generative Adversarial Networks Chapter 4 Training Generative Adversarial Networks (GANs) Chapter 5 Security Issues in Generative Adversarial Networks Chapter 6 Image Editing Using GANs Chapter 7 Practical Applications of GANs Chapter 8 Advanced Concepts in GANs
