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CODE: NAI13
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course explores current generative models and their applications in computer vision. Participants will examine the principles behind Generative Adversarial Networks (GANs), delve into advanced architectures such as conditional GANs, CycleGANs, and StyleGANs, and study how these models enable creative applications like image synthesis, super resolution, and artistic style transfer. Through a blend of theoretical foundations, practical coding exercises, and case studies, the course will equip participants with both the conceptual knowledge and hands-on skills required to design, implement and evaluate generative vision systems.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the theoretical foundations of GANs and style transfer models.
Implement and train different GAN architectures for diverse vision tasks.
Apply style transfer techniques for artistic and practical applications.
Critically evaluate generative model performance and address challenges such as mode collapse, training instability, and data limitations.
Design innovative applications of GANs and style transfer in industry and research contexts.
At the end of this course, you’ll understand:
This course is designed for researchers, data scientists, AI practitioners, and advanced machine learning engineers who already have a solid foundation in deep learning and computer vision. It is particularly suited to those seeking to deepen their expertise in generative modeling techniques and apply them to real world challenges in areas such as creative media, design, healthcare imaging, and synthetic data generation.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
