Learn and build diffusion models from the ground up. Start with an image of pure noise, and arrive at a final image, learning and building intuition at each step along the way. In How Diffusion Models Work, you will gain a deep familiarity with the diffusion process and the models which carry it out. More than simply pulling in a pre-built model or using an API, this course will teach you to build a diffusion model from scratch.
In this course you will:
- Explore the cutting-edge world of diffusion-based generative AI and create your own diffusion model from scratch.
- Gain deep familiarity with the diffusion process and the models driving it, going beyond pre-built models and APIs.
- Acquire practical coding skills by working through labs on sampling, training diffusion models, building neural networks for noise prediction, and adding context for personalized image generation.
At the end of the course, you will have a model that can serve as a starting point for your own exploration of diffusion models for your applications.
This one-hour course, taught by Sharon Zhou will expand your generative AI capabilities to include building, training, and optimizing diffusion models.
Hands-on examples make the concepts easy to understand and build upon. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and labs presented in the course.
Who should join?
How Diffusion Models Work is an intermediate-level course. Knowledge of Python, Tensorflow, or Pytorch will help you get the most out of this course.
