Generative Adversarial Networks (GANs)

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Inbunden, Engelska, 2026

2 809 kr

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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.

Produktinformation

Utgivningsdatum2026-03-31
Mått152 x 229 x undefined mm
FormatInbunden
SpråkEngelska
Antal sidor240
FörlagArcler Press
ISBN9781779564177

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Nätverk och kommunikation inom Data och IT

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.

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