Generative AI with Diffusion Models (Part 1 of 3)
This three-part workshop will dive deeper into denoising diffusion models, which are a popular choice for text-to-image pipelines.
Applications of this technology include creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more.
This session (Nov 4) will introduce U-Net architecture and training a model to remove noise from an image.
- The second session (Nov 11) will focus on autoencoders and variational autoencoders and then go toward diffusion models.
- The third session (Nov 18) focuses on diffusion models and how to train them.
Important
This is a three-part course. Make sure you can attend all three sessions before you register.
A laptop is required to participate. Students can borrow a laptop through the library’s Tech Lending program.
Prerequisites:
- A basic understanding of Deep Learning Concepts.
- Familiarity with a Deep Learning framework such as TensorFlow, PyTorch, or Keras. This course uses PyTorch.
- Date:
- Monday, November 4, 2024 Show more dates
- Time:
- 1:00pm - 3:00pm
- Location:
- The Catalyst (Parks 199)
- Audience:
- Faculty Grad students & postdocs ISU staff Undergrads
- Categories:
- Workshop > The Catalyst
Office hours will be held for this class from 3-4 p.m., on Mondays, November 4, 11, and 18.
This workshop is part of a collaboration between Transitional AI Center (TrAC) and University Library to bring classes from the NVIDIA Deep Learning Institute to Iowa State. Questions about the program can be sent to trac@iastate.edu