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More Than Image Generators: A Science of Problem-Solving using Probability | Diffusion Models
A 52-minute video by Depth First about probability and information, machine learning mechanisms and algorithms and data structures, on the AI shelf. It came up in r/StableDiffusion, in a thread called “The best tutorial on Diffusion I have seen so far”. It is pitched at advanced level.
Depth First, Video, 52 min, 2025, on the AI shelf
This is my entry to #SoME4, 3Blue1Brown's Summer of Math Exposition Competition!
Study notes
Advanced, Derivation. Step 11 of 11 in From probability to modern AI
Helpful first
- Probability distributions
- Neural-network basics
- Comfort with mathematical models
What it builds
- Ask why denoising is worth the theoretical effort.
- Connect generative modelling with natural phenomena.
- Interpret sampling as drawing from a probability distribution.
Useful moments
Check yourself: What must be represented probabilistically before diffusion can be applied to a non-image problem?
Notes are based on metadata, Reddit context and selected English caption passages. They are not a review of every claim in the video.
