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.
How to Build a Diffusion Model - An Introduction
How to Build a Diffusion Model - An Introduction
Instructors: Fractal Analytics Academy
Access provided by Matrix
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explain the core concepts of diffusion models and their role in the generative AI landscape.
Design diffusion models from scratch using effective training strategies.
Build text-to-image generation systems leveraging advanced techniques.
Evaluate model performance using real-world metrics.
Skills you'll gain
Details to know
Shareable certificate
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Assessments
7 assignments
Taught in English
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There are 3 modules in this course
Explore the fundamentals of deep learning and generative models. Understand the diffusion process, its types, and applications in AI.
What's included
7 videos3 readings3 assignments
Learn the architecture and mechanics of diffusion models. Dive into forward/reverse passes, loss functions, and training strategies.
What's included
29 videos5 readings4 assignments5 ungraded labs
Build end-to-end text-to-image systems. Cover data preparation, model construction, training, evaluation, and hands-on labs.
What's included
1 video3 readings1 peer review
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