Three speakers giving three distinct talks on Diffusion Models.
Evelyn Boettcher will give a high level talk on what diffusion models .
PhD student Daniel Brignac will do a deep dive on the math, followed up with
Wesley Giles giving a MLOPs example of how diffusion models are used.
What are Diffusion models.
Diffusion models are a type of generative AI model used to create new data, such as images or audio, from random noise:
Starting point: Begin with random noise.
Forward process: Gradually add noise to real data samples until they become indistinguishable from random noise.
Reverse process: Train a neural network to learn how to remove noise step-by-step.
Generation: To create new data, start with random noise and use the trained model to progressively remove noise, revealing a new, coherent sample.
Key points:
Diffusion models learn to denoise data
They work iteratively, making small improvements each step
The process mimics how information spreads or "diffuses" in nature
They've shown impressive results in image and audio generation
The Gem City Machine Learning (ML) group is part of the GemCity TECH family of user communities in Dayton OH.
Each month we meet at the Innovation Hub located in the newly renovated Downtown Dayton Arcade.
We meet to explore the exciting and growing field of Machine Learning (ML) and Artificial Intelligence (AI).
We regularly meet on the third Thursday of the month. You can find our next event on the GemCity TECH Meetup events calendar.
Would you like to discover more about machine learning or artificial intelligence?
Are you interested in being part of a community who are also interested in exploring the field of machine learning?
Do you work in the field of machine learning and would like to share your knowledge and experience?
Our goal is to have a space where people can present and learn new ML/AI ideas, ask for help on problems they are working on, and meet new people.
We have short talks about machine learning (ML) and how to get into this field.
The format is:
Social: ~ 30 min
Lecture: ~ 1 hour
Social: ~ 30 min