Generative Adversarial Networks (GANs) by Adele Kuzmiakova
Generative Adversarial Networks (GANs) are a class of machine learning models that have transformed the fields of artificial intelligence and creative technologies. By pitting two neural networks against each other, GANs generate highly realistic data, from images to text. This book explores the architecture, training methods, and diverse applications of GANs in healthcare, media, and research. With its in-depth analysis, it is essential for students, data scientists, and AI practitioners seeking to master this groundbreaking technology.
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.
| SKU | Unavailable |
| ISBN 13 | 9781779564177 |
| ISBN 10 | 1779564171 |
| Title | Generative Adversarial Networks (GANs) |
| Author | Adele Kuzmiakova |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Arcler Press |
| Year published | 2025-12-31 |
| Number of pages | 240 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |