How to Build a Diffusion Model - Introduction

Explore the fascinating world of generative models with a deep focus on diffusion models for high-quality image generation. You’ll begin by mastering the core principles of diffusion and then advance to the architectures that power modern text-to-image systems. Learn how these models transform random noise into stunning visuals through forward and reverse processes, and discover optimization techniques using loss functions and training strategies. By the end of this course, you’ll be equipped to build your own diffusion models from scratch, fine-tune them for specific tasks, and evaluate their performance using real-world metrics. Whether you’re an ML engineer, data scientist, or AI enthusiast, this course will give you the practical skills to excel in one of the most transformative areas of generative AI

• Explain core diffusion model concepts and their role in generative AI
• Design diffusion models from scratch with effective training strategies
• Build text-to-image generation systems using advanced techniques
• Evaluate model performance using real-world metrics

• Basic familiarity with key concepts and terminology
• Willingness to engage in practical exercises and case studies