SkillsFuture Singapore (SSG)

Embark on a cutting-edge learning experience with our WSQ-endorsed Computational Modeling with Generative Adversarial Network (GAN) course. Dive deep into the world of GANs, an essential tool in modern data science and artificial intelligence. Through hands-on projects and interactive learning, you will understand how to create and use computational models for solving complex problems, giving you practical insights into artificial neural networks and machine learning.

By the course's end, you'll be equipped to tackle real-world challenges using GANs. Whether you're a budding data scientist or an AI enthusiast, this course will significantly upgrade your skills, teaching you how to use computational models for data generation, analysis, and problem-solving.

Learning Outcome:
- direct GAN modeling efforts across the organization
- apply GAN computational methodologies to the problem.
- design advanced computational model with Variational Autoencoders (VAE)
- evaluate a broad range of GAN algorithms
- spear the application of GAN algorithms to new domains
- establish guidelines on GAN algorithm selection

Course Outline:
Topic 1 Introduction to Generative Adversarial Network (GAN)
Overview of Generative Adversarial Network (GAN)
Basic Theory of GAN
General Framework of GAN

Topic 2 Conditional GAN
Overview of Conditional GAN
Basic Application of Conditional GAN

Topic 3 Introduction to Variational Autoencoders (VAEs)
Autoencoders
Variational Autoencoders (VAEs)

Topic 4 Introduction to GAN Algorithms
DC GAN
Disco GAN
Cycle GAN
Star GAN
Energy-Based GAN (EBGAN)
VAE-GAN

Topic 5 Applications of GAN
Photo Editing Using GAN
Style Transfer Using GAN
Image to Image Transformation (Pic2Pic)

Topic 6 GAN Evaluation and Guidelines
Likelihood and Quality of GAN
Objective Evaluation
Mode Dropping

Knowledge and Skills
• Able to operate using computer functions
• Minimum Polytechnic Diploma
• Basic programming skill, preferably Python.

Attitude
• Positive Learning Attitude
• Enthusiastic Learner

Experience
• Minimum of 1 year of working experience.